POSITION DETECTING METHOD, POSITION DETECTING DEVICE, AND INTERACTIVE PROJECTOR

Abstract

A position detecting method includes (a) acquiring a first captured image and a second captured image using a first camera and a second camera, (b) acquiring a first image for processing and a second image for processing from the first captured image and the second captured image, (c) extracting a first region of interest image and a second region of interest image from the first image for processing and the second image for processing, and (d) determining a distance-related parameter related to a distance between an operation surface and a pointer using a convolutional neural network including an input layer to which the first region of interest image and the second region of interest image are input and an output layer that outputs the distance-related parameter.

Description

The present application is based on, and claims priority from JP Application Serial Number 2019-014290, filed Jan. 30, 2019, the disclosure of which is hereby incorporated by reference herein in its entirety.

BACKGROUND

1. Technical Field

The present disclosure relates to a technique for detecting the position of a pointer.

2. Related Art

JP A-2016-184850 (Patent Literature 1) discloses a projector capable of projecting a projection screen onto a screen, capturing, with a camera, an image including a pointer such as a finger, and detecting the position of the pointer using the captured image. When the tip of the pointer is in contact with the screen, the projector recognizes that a predetermined instruction for drawing or the like is input to the projection screen and draws the projection screen again according to the instruction. Therefore, a user is capable of inputting various instructions using the projection screen as a user interface. The projector of the type that can use the projection screen on the screen as the user interface, with which the user is capable of inputting instructions, in this way is called “interactive projector”. A screen surface functioning as a surface used to input instructions using the pointer is called “operation surface” as well. The position of the pointer is determined by triangulation using a plurality of images captured by a plurality of cameras.

However, in the related art, detection accuracy of the distance between the pointer and the operation surface and other distance-related parameters related to the distance is not always sufficient. Therefore, there has been demands for improvement of the detection accuracy of the distance-related parameters related to the distance between the pointer and the operation surface.

SUMMARY

According to an aspect of the present disclosure, there is provided a position detecting method for detecting a parameter related to a position of a pointer with respect to an operation surface. The position detecting method includes: (a) imaging, using a first camera, the pointer over the operation surface as a background to capture a first captured image, and imaging, using a second camera disposed in a position different from a position of the first camera, the pointer over the operation surface as the background to capture a second captured image; (b) acquiring a first image for processing from the first captured image and acquiring a second image for processing from the second captured image; (c) extracting a first region of interest image and a second region of interest image, each including the pointer, from the first image for processing and the second image for processing; and (d) determining a distance-related parameter related to a distance between the operation surface and the pointer using a convolutional neural network including an input layer including a first input channel to which the first region of interest image is input and a second input channel to which the second region of interest image is input and an output layer that outputs the distance-related parameter.

The present disclosure can also be realized in a form of a position detecting device and can be realized in various forms other than the position detecting method and the position detecting device. The present disclosure can be realized in various forms such as an interactive projector, a computer program for realizing functions of the method or the device, and a non-transitory recording medium having the computer program recorded therein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of an interactive projection system in a first embodiment.

FIG. 2 is a side view of the interactive projection system.

FIG. 3 is a front view of the interactive projection system.

FIG. 4 is a functional block diagram of an interactive projector.

FIG. 5 is a flowchart showing a procedure of position detection processing.

FIG. 6 is an explanatory diagram showing processing content of steps S100 to S300 in FIG. 5.

FIG. 7 is a flowchart showing a procedure of imaging processing in step S100.

FIG. 8 is an explanatory diagram showing content of the imaging processing.

FIG. 9 is an explanatory diagram showing a configuration example of a convolutional neural network.

FIG. 10 is an explanatory diagram showing a processing example by a convolutional layer.

FIG. 11 is a front view of a position detecting system in a second embodiment.

FIG. 12 is a functional block diagram of the position detecting system.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

A. First Embodiment

FIG. 1 is a perspective view of an interactive projection system 800 in a first embodiment. The system 800 includes an interactive projector 100 and a screen plate 820. The front surface of the screen plate 820 is used as an operation surface SS used to input an instruction using a pointer 80. The operation surface SS is also used as a projection surface on which a projection screen PS is projected. The projector 100 is fixed to a wall surface and set in the front of and above the screen plate 820. Although the operation surface SS is vertically disposed in FIG. 1, the system 800 can also be used with the operation surface SS disposed horizontally. In FIG. 1, the forward direction of the screen plate 820 is a Z direction, the upward direction of the screen plate 820 is a Y direction, and the right direction of the screen plate 820 is an X direction. For example, with Z=0, a position in a plane of the operation surface SS can be detected in a two-dimensional coordinate system (X, Y).

The projector 100 includes a projection lens 210 that projects an image onto the screen plate 820, two cameras 310 and 320 that capture images including the pointer 80, and two illuminating sections 410 and 420 that irradiate infrared lights for detecting the pointer 80, the two illuminating sections 410 and 420 corresponding to the two cameras 310 and 320.

The projection lens 210 projects the projection screen PS onto the operation surface SS. The projection screen PS includes an image drawn in the projector 100. When an image drawn in the projector 100 is absent, light is irradiated on the projection screen PS from the projector 100 and a white image is displayed. In this specification, the “operation surface SS” means a surface used to input an instruction using the pointer 80. The “projection screen PS” means a region of an image projected onto the operation surface SS by the projector 100.

In the system 800, one or a plurality of non-light emitting pointers 80 are usable. As the pointer 80, non-light emitting objects such as a finger and a pen are usable. A tip portion for an instruction of the non-light emitting pointer 80 is desirably excellent in a characteristic for reflecting infrared light and has a retroreflection characteristic.

A first camera 310 and a second camera 320 are respectively set to be capable of imaging the entire operation surface SS and have a function of respectively capturing images of the pointer 80 over the operation surface SS as a background. That is, the first camera 310 and the second camera 320 create images including the pointer 80 by receiving lights reflected on the operation surface SS and the pointer 80 in the infrared lights irradiated from a first illuminating section 410 and a second illuminating section 420. When two images captured by the first camera 310 and the second camera 320 are used, a three-dimensional position of the pointer 80 can be calculated by triangulation or the like. The number of cameras may be three or more.

The first illuminating section 410 has a function of a peripheral illuminating section that illuminates the periphery of an optical axis of the first camera 310 with infrared light. In the example shown in FIG. 1, the first illuminating section 410 includes four illuminating elements disposed to surround the periphery of the first camera 310. The first illuminating section 410 is configured such that a shadow of the pointer 80 due to the first illuminating section 410 is not substantially formed when an image of the pointer 80 is captured by the first camera 310. “A shadow is not substantially formed” means that the shadow of the pointer 80 is so thin as to not affect processing for calculating a three-dimensional position of the pointer 80 using the image. The second illuminating section 420 has the same configuration and the same function as the configuration and the function of the first illuminating section 410 and has a function of a peripheral illuminating section that illuminates the periphery of an optical axis of the second camera 320 with infrared light.

The number of illuminating elements configuring the first illuminating section 410 is not limited to four and may be any number equal to or larger than two. However, a plurality of illuminating elements configuring the first illuminating section 410 are desirably disposed in rotationally symmetrical positions centering on the first camera 310. The first illuminating section 410 may be configured using a ring-like illuminating element instead of using the plurality of illuminating elements. Further, a coaxial illuminating section that emits infrared light through a lens of the first camera 310 may be used as the first illuminating section 410. These modifications are applicable to the second illuminating section 420 as well. When, with N set to an integer equal to or larger than 2, N cameras are provided, peripheral illuminating sections or coaxial illuminating sections are desirably provided respectively for the cameras.

FIG. 2 is a side view of the interactive projection system 800. FIG. 3 is a front view of the interactive projection system 800. In this specification, a direction from the left end to the right end of the operation surface SS is defined as an X direction, a direction from the lower end to the upper end of the operation surface SS is defined as a Y direction, and a direction along the normal of the operation surface SS is defined as a Z direction. For convenience, the X direction is referred to as “width direction” as well, the Y direction is referred to as “upward direction” as well, and the Z direction is referred to as “distance direction” as well. In FIG. 2, for convenience of illustration, hatching is applied to a range of the projection screen PS in the screen plate 820. A coordinate position of the operation surface SS onto which the projection screen PS is projected can be detected as, for example, with Z=0, a two-dimensional coordinate of a two-dimensional coordinate system (X, Y). A two-dimensional coordinate system (V, U) of a captured image of the first camera 310 and a two-dimensional coordinate system (η, ξ) of a captured image of the second camera 320 are different from each other because of the dispositions and characteristics of the first camera 310 and the second camera 320 and are also different from the coordinate system (X, Y) of the projection screen PS and the operation surface SS. These coordinate systems are associated by calculating a conversion coefficient or the like with calibration processing.

An example shown in FIG. 3 shows a state in which the interactive projection system 800 is operating in a white board mode. The white board mode is a mode in which the user can optionally draw on the projection screen PS using the pointer 80. The projection screen PS including a tool box TB is projected on the operation surface SS. The tool box TB includes an undo button UDB for resetting processing, a pointer button PTB for selecting a mouse pointer, a pen button PEB for selecting a pen tool for drawing, an eraser button ERB for selecting an eraser tool for erasing a drawn image, and a front/rear button FRB for advancing or returning a screen. By clicking the buttons using the pointer 80, the user is capable of performing processing corresponding to the buttons and selecting tools corresponding to the buttons. Immediately after a start of the system 800, the mouse pointer may be selected as a default tool. In the example shown in FIG. 3, a state is drawn in which, after selecting a pen tool, the user moves the tip portion of the pointer 80 in the projection screen PS in a state in which the tip portion of the pointer 80 is in contact with the operation surface SS, whereby a line is drawn in the projection screen PS. The drawing of the line is performed by a projection-image creating section explained below.

The interactive projection system 800 is also operable in modes other than the white board mode. For example, the system 800 is also operable in a PC interactive mode for displaying, on the projection screen PS, an image of data transferred from a not-shown personal computer via a communication line. In the PC interactive mode, an image of data of spreadsheet software or the like is displayed. Input, creation, correction, and the like of data can be performed using various tools and icons displayed in the image.

FIG. 4 is a functional block diagram of the interactive projector 100. The projector 100 includes a control section 700, a projecting section 200, a projection-image generating section 500, a position detecting section 600, an imaging section 300, and an infrared illuminating section 400. The imaging section 300 includes the first camera 310 and the second camera 320. The infrared illuminating section 400 includes the first illuminating section 410 and the second illuminating section 420.

The control section 700 performs control of the sections of the projector 100. The control section 700 has a function of an imaging control section 710 that acquires an image of the pointer 80 using the imaging section 300 and the infrared illuminating section 400. Further, the control section 700 has a function of an operation executing section 720 that recognizes content of an instruction performed on the projection screen PS by the pointer 80 detected by the position detecting section 600 and instructs the projection-image generating section 500 to create or change a projection image according to the content of the instruction.

The projection-image generating section 500 includes an image memory 510 that stores a projection image. The projection-image generating section 500 has a function of generating a projection image to be projected onto the operation surface SS by the projecting section 200. The projection-image generating section 500 desirably further has a function of a keystone correction section that corrects trapezoidal distortion of the projection screen PS.

The projecting section 200 has a function of projecting the projection image generated by the projection-image generating section 500 onto the operation surface SS. The projecting section 200 includes a light modulating section 220 and a light source 230 besides the projection lens 210 explained with reference to FIG. 2. The light modulating section 220 forms projection image light IML by modulating light from the light source 230 according to projection image data given from the image memory 510. The projection image light IML is typically color image light including visible lights of three colors of RGB and is projected onto the operation surface SS by the projection lens 210. As the light source 230, various light sources such as a light emitting diode and a laser diode can be adopted besides alight source lamp such as an ultrahigh pressure mercury lamp. As the light modulating section 220, a liquid crystal panel, a digital mirror device, and the like of a transmission type or a reflection type can be adopted. The projecting section 200 may include a plurality of light modulating sections 220 for each of color lights.

The infrared illuminating section 400 includes the first illuminating section 410 and the second illuminating section 420 explained with reference to FIG. 1. The first illuminating section 410 and the second illuminating section 420 are capable of respectively irradiating, on the operation surface SS and a space in front of the operation surface SS, illumination detection light IDL for detecting the tip portion of the pointer 80. The irradiation detection light IDL is infrared light. As explained below, the first illuminating section 410 and the second illuminating section 420 are lit at exclusive timings.

The imaging section 300 includes the first camera 310 and the second camera 320 explained with reference to FIG. 2. The two cameras 310 and 320 have a function of receiving light in a wavelength region including a wavelength of the irradiation detection light IDL and imaging the light. In an example shown in FIG. 4, a state is drawn in which the irradiation detection light IDL irradiated by the infrared illuminating section 400 is reflected by the pointer 80 and reflected detection light RDL of the irradiation detection light IDL is received and imaged by the two cameras 310 and 320.

The position detecting section 600 has a function of calculating a position of the tip portion of the pointer 80 using a first captured image captured and acquired by the first camera 310 and a second captured image captured and acquired by the second camera 320. The position detecting section 600 includes an image-for-processing acquiring section 610, a region-of-interest extracting section 620, and a convolutional neural network 630. These sections may be stored in a storage region of the position detecting section as models. The image-for-processing acquiring section 610 acquires, from the two captured images captured by the two cameras 310 and 320, a first image for processing and a second image for processing, which are two images for processing to be processed by the region-of-interest extracting section 620. In an example, the image-for-processing acquiring section 610 creates two calibration images by performing stereo calibration on the two captured images captured by the two cameras 310 and 320 and acquires the two calibration images as two images for processing. The region-of-interest extracting section 620 extracts, from the two images for processing, a first region of interest image and a second region of interest image, which are two region of interest images, each including the pointer 80. The convolutional neural network 630 is configured to include an input layer to which the two region of interest images are input and an output layer that outputs a distance-related parameter related to the distance between the operation surface SS and the pointer 80. Details of functions of the sections 610 to 630 are explained below.

Functions of the sections of the control section 700 and functions of the sections of the position detecting section 600 are realized by, for example, a processor in the projector 100 executing computer programs. A part of the functions of the sections may be realized by a hardware circuit such as an FPGA (field-programmable gate array).

FIG. 5 is a flowchart showing a procedure of position detection processing in the embodiment. FIG. 6 is an explanatory diagram showing processing content of steps S100 to S300 in FIG. 5. This processing is repeatedly executed during the operation of the interactive projection system 800.

In step S100, the imaging section 300 acquires a plurality of images by imaging the pointer 80 over the operation surface SS as the background.

FIG. 7 is a flowchart showing a procedure of imaging processing in step S100 in FIG. 5. FIG. 8 is an explanatory diagram showing content of the imaging processing. First images IM1_1 and IM1_2 are indicated by the two-dimensional coordinate system (U, V) captured by the first camera 310. Second images IM2_1 and IM2_2 are indicated by the two-dimensional coordinate system (η, ξ) captured by the second camera 320. The procedure shown in FIG. 7 is executed under control by the imaging control section 710.

In step S110, the imaging control section 710 turns on the first illuminating section 410 and turns off the second illuminating section 420. In step S120, the imaging control section 710 captures images using the first camera 310 and the second camera 320. As a result, the first image IM1_1 and the second image IM2_1 shown in an upper part of FIG. 8 are acquired. A broken line surrounding the periphery of the first image IM1_1 is added for emphasis. Both the images IM1_1 and IM2_1 are images including the pointer 80 over the operation surface SS as the background. As explained with reference to FIG. 1, the first illuminating section 410 is configured such that a shadow of the pointer 80 due to the first illuminating section 410 is not substantially formed when an image of the pointer 80 is captured by the first camera 310. Therefore, of the two images acquired in step S120, the first image IM1_1 is a captured image captured by the first camera 310 when the first illuminating section 410 is lit. The first image IM1_1 does not substantially include a shadow of the pointer 80. On the other hand, the second image IM2_1 is a captured image captured by the second camera 320 when the second illuminating section 420 is extinguished. The second image IM2_1 includes a shadow SH1 of the pointer 80. The second image IM2_1 may not be captured.

In step S130, the imaging control section 710 turns off the first illuminating section 410 and turns on the second illuminating section 420. In step S140, the imaging control section 710 captures images using the first camera 310 and the second camera 320. As a result, the first image IM1_2 and the second image IM2_2 shown in a middle part of FIG. 8 are acquired. The second illuminating section 420 is configured such that a shadow of the pointer 80 due to the second illuminating section 420 is not substantially formed when an image of the pointer 80 is captured by the second camera 320. Therefore, of the two images acquired in step S140, the second image IM2_2 is an image captured by the second camera 320 when the second illuminating section 420 is lit. The second image IM2_2 does not substantially include a shadow of the pointer 80. On the other hand, the first image IM1_2 is an image captured by the first camera 310 when the first illuminating section 410 is extinguished. The first image IM1_2 includes a shadow SH2 of the pointer 80. The first image IM1_2 may not be captured.

When the imaging in step S120 and step S140 ends, as shown in a lower part of FIG. 8, the first image IM1_1 not substantially having a shadow captured by the first camera 310 and the second image IM2_2 not substantially having a shadow captured by the second camera 320 are obtained. The first image IM1_1 is a first captured image and the second image IM2_2 is a second captured image. In step S150 in FIG. 7, the imaging control section 710 turns off the two illuminating sections 410 and 420, ends the processing in step S100, and stays on standby until the next imaging. Step S150 may be omitted. After ending the processing in FIG. 7, the imaging control section 710 may immediately resume the processing shown in FIG. 7.

When the processing in step S100 ends in this way, in step S200 in FIG. 5, the image-for-processing acquiring section 610 acquires, from the two images M1_1 and IM2_2 obtained in step S100, two images for processing to be processed by the region-of-interest extracting section 620. As a method of acquiring images for processing, for example, any one of the following methods 1 to 3 can be selected.

Method 1

Two calibration images are created by performing stereo calibration on the two images IM1_1 and IM2_2. The two calibration images are set as images for processing.

In this embodiment, as the “stereo calibration”, processing for adjusting a coordinate of one of the two images IM1_1 and IM2_2 is performed to eliminate a parallax on the operation surface SS. For example, when the first image IM1_1, which is the coordinate system (U, V), is set as a reference image and the second image IM2_2 is set as a comparative image to calculate a parallax, calibration can be performed to eliminate a parallax between the first image IM1_1 and the second image IM2_2 on the operation surface SS by adjusting the coordinate system (η, ξ) of the second image IM2_2 to the coordinate system (U, V). Calibration parameters such as a conversion coefficient necessary for the stereo calibration are determined in advance and set in the calibration executing section 610. Two images IM1 and IM2 shown in an upper part of FIG. 6 indicate two calibration images after the stereo calibration. However, the pointer 80 is drawn to be simplified in the calibration images IM1 and IM2. With the projection screen PS, which is the (X, Y) coordinate system, set as a reference image, the respective calibration images IM1 and IM2 of the first image IM1_1 captured by the first camera 310 and the second image IM2_2 captured by the second camera 320 may be created to perform the stereo calibration. In this case, a calibration parameter for converting the two-dimensional coordinate system (U, V) of the first image IM1 into the two-dimensional coordinate system (X, Y) of the projection image PS and a calibration parameter for converting the two-dimensional coordinate system (η, ξ) of the second image IM2 into the two-dimensional coordinate system (X, Y) of the projection image PS are determined in advance and set in the calibration executing section 610. In the first embodiment, the two calibration images IM1 and IM2 obtained by the method 1 are used as two images for processing to be processed by the region-of-interest extracting section 620.

Method 2

The two images IM1_1 and IM2_2 themselves are acquired as two images for processing.

Method 3

Two images for processing are created by executing preprocessing such as distortion correction or parallelization on the two images IM1_1 and IM2_2.

According to an experiment by the inventors, the distance-related parameters were able to be most accurately determined when the method 1 was used among the method 1 to the method 3. This is assumed to be because peculiar lens distortion and distortion of an image due to positional deviation of a camera are corrected by performing the stereo calibration. However, the method 2 and the method 3 have an advantage that processing can be simplified compared with the method 1.

Instead of setting illumination periods for the two illuminating sections 410 and 420 at the exclusive timings different from each other and sequentially capturing images in the respective illumination periods as explained with reference to FIGS. 7 and 8, the stereo calibration may be executed using two images captured at the same timing by the two cameras 310 and 320. In this case, the two illuminating sections 410 and 420 explained with reference to FIG. 1 do not need to be provided. It is sufficient to provide one illuminating section used in common to the two cameras 310 and 320. However, in the imaging method explained with reference to FIGS. 7 and 8, the two images IM1_1 and IM2_2 not substantially having a shadow are obtained. Therefore, there is an advantage that the processing shown in FIG. 5 can be more accurately performed.

In step S300 in FIG. 5, the region-of-interest extracting section 620 extracts region of interest images RO1 and RO2 respectively from the two images for processing IM1 and IM2. As shown in upper and middle parts of FIG. 6, the region of interest images RO1 and RO2 are images of a region including the tip portion of the pointer 80 and are images extracted as targets of later processing. Extraction processing of the region of interest images RO1 and RO2 can be executed using publicly-known various kinds of image processing such as a background difference method, an average background difference method, binarization, morphology conversion, edge detection, and convex hull detection. Each of the region of interest images RO1 and RO2 is extracted as, for example, a square image centering on the tip portion of the pointer 80 and having 100 to 300 pixels on one side. The positions of the pixels in the region of interest image RO1 are represented by two-dimensional coordinates u, v of the region of interest image RO1. The same applies to the other region of interest image RO2.

In step S400, the convolutional neural network 630 determines distance-related parameters from the two region of interest images RO1 and RO2. In the first embodiment, the distance itself between the operation surface SS and the pointer 80 is used as the distance-related parameter.

FIG. 9 is an explanatory diagram showing a configuration example of the convolutional neural network 630. The convolutional neural network 630 includes an input layer 631, an intermediate layer 632, a fully coupled layer 633, and an output layer 634. The input layer 631 includes a first channel and a second channel, which are two input channels to which the two region of interest images RO1 and RO2 obtained in step S400 are input. The intermediate layer 632 includes convolutional layers CU1, CU2, CU3, normalization layers RU1, RU2, and pooling layers PU2. The combination and the disposition of convolutional layers, normalization layers, and pooling layers are examples. Various combinations and dispositions other than this are possible. A plurality of feature values corresponding to the two region of interest images RO1 and RO2 are output from the intermediate layer 632 and input to the fully coupled layer 633. The fully coupled layer 633 may include a plurality of fully coupled layers. The output layer 634 includes three output nodes N1 to N3. A first output node N1 outputs a distance ΔZ between the operation surface SS and the pointer 80 as the distance-related parameter. A second output node N2 outputs a u coordinate value of the tip of the pointer 80. A third output node N3 outputs a v coordinate value of the tip of the pointer 80. The u coordinate value and the v coordinate value are coordinate values of the two-dimensional coordinate system of the region of interest image RO1 shown in FIG. 6. The second output node N2 and the third output node N3 may be omitted.

Numerical value examples of a pixel size Nx in the X direction, a pixel size Ny in the Y direction, and the number of channels Nc in outlets of the layers are shown in the lower right of layers shown in FIG. 9. For example, concerning data input from the input layer 631 to a first convolutional layer CU1, Nx=100, Ny=100, and Nc=2. Concerning data input from the first convolutional layer CU1 to the normalization layer RU1, Nx=98, Ny=98, and Nc=64. That is, in the first convolutional layer CU1, a size of an image region decreases by two pixels per one side. The number of channels increases from two to sixty-four.

FIG. 10 is an explanatory diagram showing a processing example by the convolutional layers CU1 and CU2. Illustration of a normalization layer is omitted because the normalization layer does not affect a data size. The convolutional layer CU1 includes a plurality of filters F11, F12, applied to the region of interest images RO1 and RO2 of two channels. A first filter F11 is configured by a filter F11_1 for a first channel and a filter F11_2 for a second channel. In processing by the first filter F11, a convolution result of the filter F11_1 for the first channel and the first region of interest image RO1 and a convolution result of the filter F11_2 for the second channel and the second region of interest image RO2 are added up and a result of the addition is created as a new image MM11. In this example, since a size of the filters F11_1 and F11_2 is 3×3 pixels, a pixel size of one side of the image MM11 is smaller than a pixel size of one side of the original region of interest images RO1 and RO2 by two pixels. A second filter F12 has the same size as the size of the first filter F11. A new image MM12 is created by processing by the second filter F12. The convolutional layer CU1 includes sixty-four filters F11, F12. Therefore, in the example shown in FIG. 9, an output of a first convolutional layer CU1 increases to sixty-four channels. A second convolutional layer CU2 includes filters F21, F22, of 3×3 pixels applied to the channels.

The configuration of the convolutional neural network 630 shown in FIGS. 9 and 10 is an example. Various configurations other than this can be adopted.

The distance-related parameter can be determined using the convolutional neural network 630 because the distance-related parameter has a positive or negative correlation with feature values of the two region of interest images RO1 and RO2. As the feature values having the correlation with the distance-related parameter, there is a representative correlation value indicating a correlation between the two region of interest images RO1 and RO2. As an example of a method of creating representative correlation value of the two region of interest images RO1 and RO2, there is a method of, first, calculating correlation values for each of pixels of the two region of interest images RO1 and RO2 using kernel regions centering on the pixels of the two region of interest images RO1 and RO2 to thereby create a correlation image formed by the correlation values and further calculating a statistical representative value of the correlation values in the correlation image. As the correlation values, a correlation coefficient, an SAD (Sum of Absolute Difference), an SSD (Sum of Squared Difference), and the like can be used. An average, a maximum, a median, and the like correspond to the statistical representative value. Such a representative correlation value or a value similar to the representative correlation value is calculated as one of feature values of the two region of interest images RO1 and RO2 in the intermediate layer 632 of the convolutional neural network 630 and input to the fully coupled layer 633. As explained above, the distance ΔZ between the operation surface SS and the pointer 80 has a positive or negative correlation with the feature values of the two region of interest images RO1 and RO2. Therefore, it is possible to determine the distance ΔZ using the convolutional neural network 630 to which the two region of interest images RO1 and RO2 are input. During learning of the convolutional neural network 630, if causing the convolutional neural network 630 to learn a distance-related parameter other than the distance ΔZ, it is possible to obtain the distance-related parameter using the convolutional neural network 630.

In step S500 in FIG. 5, the operation executing section 720 determines whether the distance ΔZ between the operation surface SS and the pointer 80 is equal to or smaller than a preset threshold Th. If the distance ΔZ is equal to or smaller than the threshold Th, in step S600, the operation executing section 720 executes operation corresponding to the tip position of the pointer 80. The threshold Th is a value with which it can be determined that the tip of the pointer 80 is extremely close to the operation surface SS. The threshold Th is set in a range of, for example, 3 to 5 mm. The operation in step S600 is processing on the operation surface SS such as the drawing explained with reference to FIG. 3. An XY coordinate of the tip position of the pointer 80 on the operation surface SS can be obtained by converting a uv coordinate of the tip position of the pointer 80 output from the two output nodes N2 and N3 of the convolutional neural network 630 into an XY coordinate. When the convolutional neural network 630 does not include an output node for outputting the uv coordinate of the tip position of the pointer 80, the XY coordinate of the tip position of the pointer 80 may be determined by any other method. For example, the XY coordinate of the tip position of the pointer 80 can be determined using a publicly-known method such as pattern matching or characteristic detection of the pointer 80 in the two region of interest images RO1 and RO2.

In step S400, the distance ΔZ between the operation surface SS and the pointer 80 is determined as the distance-related parameter. However, a parameter other than the distance ΔZ may be calculated as the distance-related parameter. For example, when, from the feature values obtained according to the two region of interest images RO1 and RO2, it can be assumed in step S400 that the distance ΔZ is sufficiently small, the operation in step S600 may be immediately executed without calculating the distance ΔZ. In this case, the distance-related parameter is an operation execution parameter such as a flag or a command indicating execution of operation corresponding to the position of the pointer 80. The operation execution parameter is output from the convolutional neural network 630. With this configuration, in a situation in which the distance ΔZ between the pointer 80 and the operation surface SS is assumed to be sufficiently small, it is possible to execute operation on the operation surface SS using the pointer 80 without determining the distance ΔZ between the pointer 80 and the operation surface SS.

As explained above, in the first embodiment, the distance-related parameter related to the distance ΔZ between the operation surface SS and the pointer 80 is determined using the convolutional neural network 630 to which the two region of interest images RO1 and RO2 are input and from which the distance-related parameter is output. Therefore, it is possible to accurately determine the distance-related parameter.

In the first embodiment, the region of interest images RO1 and RO2 input to the convolutional neural network 630 are stereo-calibrated images. Therefore, peculiar lens distortion and distortion of an image due to positional deviation of a camera are corrected by the stereo calibration. Consequently, it is possible to reduce an extraction error of characteristics by the convolutional neural network 630. As a result, there is an advantage that the learnt convolutional neural network 630 can be applied to different lenses and cameras as well.

The number of cameras may be three or more. That is, with N set to an integer equal to or larger than 3, N cameras may be provided. In this case, the image-for-processing acquiring section 610 acquires N images for processing. The region-of-interest extracting section 620 extracts N region of interest images, each including the pointer 80, from the N images for processing. The input layer 631 of the convolutional neural network 630 is configured to include N input channels to which the N region of interest images are input. With this configuration, the distance-related parameter is determined from the N region of interest images. Therefore, it is possible to accurately determine the distance-related parameter.

B. Second Embodiment

FIG. 11 is a front view of a position detecting system 900 in a second embodiment. The position detecting system 900 includes an image display panel 200a, the two cameras 310 and 320 that capture images including the pointer 80, and the two illuminating sections 410 and 420 that irradiate infrared lights for detecting the pointer 80. The configurations of the cameras 310 and 320 and the illuminating sections 410 and 420 are the same as the configurations of those in the first embodiment. The image display panel 200a is a so-called flat panel display. An image display surface of the image display panel 200a is equivalent to the operation surface SS.

FIG. 12 is a functional block diagram of the position detecting system 900. In the position detecting system 900, among the components of the interactive projector 100 shown in FIG. 4, the projecting section 200 is changed to the image display panel 200a and the projection-image generating section 500 is changed to an image generating section 500a. The other components are the same as the components of the interactive projector 100. Position detection processing by the position detecting system 900 is the same as the processing in the first embodiment explained with reference to FIGS. 4 to 10. Therefore, explanation of the position detection processing is omitted. The second embodiment achieves the same effects as the effects in the first embodiment.

C. Other Embodiments

The present disclosure is not limited to the embodiments explained above and can be realized in various forms in a range not departing from the gist of the present disclosure. For example, the present disclosure can also be realized by the following aspects. The technical features in the embodiments corresponding to technical features in the aspects described below can be substituted or combined as appropriate in order to solve a part or all of the problems of the present disclosure or in order to achieve a part or all of the effects of the present disclosure. If the technical features are not explained as essential technical features in this specification, the technical features can be deleted as appropriate.

(1) According to a first aspect of the present disclosure, there is provided a position detecting method for detecting a parameter related to a position of a pointer with respect to an operation surface. The position detecting method includes: (a) imaging, using a first camera, the pointer over the operation surface as a background to capture a first captured image, and imaging, using a second camera disposed in a position different from a position of the first camera, the pointer over the operation surface as the background to capture a second captured image; (b) acquiring a first image for processing from the first captured image and acquiring a second image for processing from the second captured image; (c) extracting a first region of interest image and a second region of interest image, each including the pointer, from the first image for processing and the second image for processing; and (d) determining a distance-related parameter related to a distance between the operation surface and the pointer using a convolutional neural network including an input layer including a first input channel to which the first region of interest image is input and a second input channel to which the second region of interest image is input and an output layer that outputs the distance-related parameter.

With the position detecting method, since the distance-related parameter related to the distance between the operation surface and the pointer is determined using the convolutional neural network to which the two region of interest images are input and from which the distance-related parameter is output, it is possible to accurately determine the distance-related parameter.

(2) In the position detecting method, in the (a), with N set to an integer equal to or larger than 3, the pointer over the operation surface as the background may be captured using N cameras to acquire N captured images, in the (b), N images for processing may be acquired from the N captured images, in the (c), N region of interest images, each including the pointer, may be extracted from the N images for processing, and, in the (d), the distance-related parameter may be determined using a convolutional neural network including an input layer including N input channels to which the N region of interest images are input and an output layer that outputs the distance-related parameter.

With the position detecting method, since the distance-related parameter related to the distance between the operation surface and the pointer is determined using the convolutional neural network to which the N region of interest images are input and from which the distance-related parameter is output, it is possible to more accurately determine the distance-related parameter.

(3) In the position detecting method, in the (b), the N images for processing may be created by performing stereo calibration on the first captured image and the second captured image.

With the position detecting method, since the two region of interest images are extracted from the two images for processing on which the stereo calibration is performed, it is possible to accurately determine the distance-related parameter using the convolutional neural network to which the two region of interest images are input.

(4) In the position detecting method, in the (b), the first captured image and the second captured image may be acquired as the first image for processing and the second image for processing.

With the position detecting method, since the first captured image and the second captured image are acquired as the first image for processing and the second image for processing, it is possible to simplify processing for calculating the distance-related parameter.

(5) In the position detecting method, the distance-related parameter may be the distance between the operation surface and the pointer.

With the position detecting method, it is possible to accurately determine the distance between the operation surface and the pointer using the convolutional neural network.

(6) In the position detecting method, the distance-related parameter may be an operation execution parameter indicating that operation on the operation surface corresponding to a position of the pointer is executed.

With the position detecting method, in a situation in which it is assumed that the distance between the pointer and the operation surface is sufficiently small, it is possible to execute the operation on the operation surface using the pointer without determining the distance between the pointer and the operation surface.

(7) In the position detecting method, the (a) may include: sequentially selecting a first infrared illuminating section provided to correspond to the first camera and a second infrared illuminating section provided to correspond to the second camera; and executing imaging using the first camera while performing illumination with the first infrared illuminating section without performing illumination with the second infrared illuminating section, executing imaging using the second camera while performing illumination with the second infrared illuminating section without performing illumination with the first infrared illuminating section, and sequentially acquiring the first captured image and the second captured image one by one at different timings, and the first infrared illuminating section and the second infrared illuminating section may be configured to include at least one of a coaxial illuminating section configured to perform coaxial illumination on the cameras and a peripheral illuminating section disposed to surround peripheries of optical axes of the cameras.

With this position detecting method, since the first captured image and the second captured image can be captured in a state in which a shadow of the pointer is less on the operation surface, it is possible to accurately determine the distance-related parameter.

(8) According to a second aspect of the present disclosure, there is provided a position detecting device that detects a parameter related to a position of a pointer with respect to an operation surface. The position detecting device includes: an imaging section including a first camera configured to image the pointer over the operation surface as a background to capture a first captured image and a second camera disposed in a position different from a position of the first camera and configured to image the pointer over the operation surface as the background to capture a second captured image; an image-for-processing acquiring section configured to acquire a first image for processing from the first captured image and acquire a second image for processing from the second captured image; a region-of-interest extracting section configured to extract a first region of interest image and a second region of interest image, each including the pointer, from the first image for processing and the second image for processing; and a convolutional neural network including an input layer including a first input channel to which the first region of interest image is input and a second input channel to which the second region of interest image is input and an output layer that outputs a distance-related parameter related to a distance between the operation surface and the pointer.

With the position detecting device, since the distance-related parameter related to the distance between the operation surface and the pointer is determined using the convolutional neural network to which the two region of interest images are input and from which the distance-related parameter is output, it is possible to accurately determine the distance-related parameter.

(9) In the position detecting device, the imaging section may include, with N set to an integer equal to or larger than 3, N cameras configured to image the pointer over the operation surface as the background to capture N captured images, the image-for-processing acquiring section may acquire N images for processing from the N captured images, the region-of-interest extracting section may extract N region of interest images, each including the pointer, from the N images for processing, and the convolutional neural network may include an input layer including N input channels to which the N region of interest images are input and an output layer that outputs the distance-related parameter.

With the position detecting device, since the distance-related parameter related to the distance between the operation surface and the pointer is determined using the convolutional neural network to which the N region of interest images are input and from which the distance-related parameter is output, it is possible to more accurately determine the distance-related parameter.

(10) In the position detecting device, the image-for-processing acquiring section may create the N images for processing by performing stereo calibration on the first captured image and the second captured image.

With the position detecting device, since the two region of interest images are extracted from the two images for processing on which the stereo calibration is performed, it is possible to accurately determine the distance-related parameter using the convolutional neural network to which the two region of interest images are input.

(11) In the position detecting device, the image-for-processing acquiring section may acquire the first captured image and the second captured image as the first image for processing and the second image for processing.

With the position detecting device, since the first captured image and the second captured image are acquired as the first image for processing and the second image for processing, it is possible to simplify processing for calculating the distance-related parameter.

(12) In the position detecting device, the distance-related parameter may be the distance between the operation surface and the pointer.

With the position detecting device, it is possible to accurately determine the distance between the operation surface and the pointer using the convolutional neural network.

(13) In the position detecting device, the distance-related parameter may be an operation execution parameter indicating that operation on the operation surface corresponding to a position of the pointer is executed.

With the position detecting device, in a situation in which it is assumed that the distance between the pointer and the operation surface is sufficiently small, it is possible to execute the operation on the operation surface using the pointer without determining the distance between the pointer and the operation surface.

(14) The position detecting device may further include: a first infrared illuminating section configured to include at least one of a coaxial illuminating section configured to perform coaxial illumination on the first camera and a peripheral illuminating section disposed to surround a periphery of an optical axis of the first camera; a second infrared illuminating section configured to include at least one of a coaxial illuminating section configured to perform coaxial illumination on the second camera and a peripheral illuminating section disposed to surround a periphery of an optical axis of the second camera; and an imaging control section configured to control imaging performed using the first camera and the first infrared illuminating section and the second camera and the second infrared illuminating section. The imaging control section may sequentially select the first camera and the first infrared illuminating section and the second camera and the second infrared illuminating section, execute imaging using the first camera while performing illumination with the first infrared illuminating section without performing illumination with the second infrared illuminating section, execute imaging using the second camera while performing illumination with the second infrared illuminating section without performing illumination with the first infrared illuminating section and sequentially capture the first captured image and the second captured image at different timings.

With this position detecting device, since the first captured image and the second captured image can be captured in a state in which a shadow of the pointer is less on the operation surface, it is possible to accurately determine the distance-related parameter.

(15) According to a third aspect of the present disclosure, there is provided an interactive projector that detects a parameter related to a position of a pointer with respect to an operation surface. The interactive projector includes: a projecting section configured to project a projection image onto the operation surface; an imaging section including a first camera configured to image the pointer over the operation surface as a background to capture a first captured image and a second camera disposed in a position different from a position of the first camera and configured to image the pointer over the operation surface as the background to capture a second captured image; an image-for-processing acquiring section configured to acquire a first image for processing from the first captured image and acquire a second image for processing from the second captured image; a region-of-interest extracting section configured to extract a first region of interest image and a second region of interest image, each including the pointer, from the first image for processing and the second image for processing; and a convolutional neural network including an input layer including a first input channel to which the first region of interest image is input and a second input channel to which the second region of interest image is input and an output layer that outputs a distance-related parameter related to a distance between the operation surface and the pointer.

With the interactive projector, since the distance-related parameter related to the distance between the operation surface and the pointer is determined using the convolutional neural network to which N region of interest images are input and from which the distance-related parameter is output, it is possible to accurately determine the distance-related parameter.

Claims

1. A position detecting method for detecting a parameter related to a position of a pointer with respect to an operation surface, the position detecting method comprising: (a) imaging, using a first camera, the pointer over the operation surface as a background to capture a first captured image, and imaging, using a second camera disposed in a position different from a position of the first camera, the pointer over the operation surface as the background to capture a second captured image;(b) acquiring a first image for processing from the first captured image and acquiring a second image for processing from the second captured image;(c) extracting a first region of interest image and a second region of interest image, each including the pointer, from the first image for processing and the second image for processing; and(d) determining a distance-related parameter related to a distance between the operation surface and the pointer using a convolutional neural network including an input layer including a first input channel to which the first region of interest image is input and a second input channel to which the second region of interest image is input and an output layer that outputs the distance-related parameter.

2. The position detecting method according to claim 1, wherein in the (a), with N set to an integer equal to or larger than 3, the pointer over the operation surface as the background is captured using N cameras to acquire N captured images,in the (b), N images for processing are acquired from the N captured images,in the (c), N region of interest images, each including the pointer, are extracted from the N images for processing, andin the (d), the distance-related parameter is determined using a convolutional neural network including an input layer including N input channels to which the N region of interest images are input and an output layer that outputs the distance-related parameter.

3. The position detecting method according to claim 1, wherein, in the (b), N images for processing are created by performing stereo calibration on the first captured image and the second captured image.

4. The position detecting method according to claim 1, wherein, in the (b), the first captured image and the second captured image are acquired as the first image for processing and the second image for processing.

5. The position detecting method according to claim 1, wherein the distance-related parameter is the distance between the operation surface and the pointer.

6. The position detecting method according to claim 1, wherein the distance-related parameter is an operation execution parameter indicating that operation on the operation surface corresponding to a position of the pointer is executed.

7. The position detecting method according to claim 1, wherein the (a) includes:sequentially selecting a first infrared illuminating section provided to correspond to the first camera and a second infrared illuminating section provided to correspond to the second camera; andexecuting imaging using the first camera while performing illumination with the first infrared illuminating section without performing illumination with the second infrared illuminating section, executing imaging using the second camera while performing illumination with the second infrared illuminating section without performing illumination with the first infrared illuminating section, and sequentially acquiring the first captured image and the second captured image one by one at different timings, andthe first infrared illuminating section and the second infrared illuminating section are configured to include at least one of a coaxial illuminating section configured to perform coaxial illumination on the cameras and a peripheral illuminating section disposed to surround peripheries of optical axes of the cameras.

8. A position detecting device that detects a parameter related to a position of a pointer with respect to an operation surface, the position detecting device comprising: an imaging section including a first camera configured to image the pointer over the operation surface as a background to capture a first captured image and a second camera disposed in a position different from a position of the first camera and configured to image the pointer over the operation surface as the background to capture a second captured image;an image-for-processing acquiring section configured to acquire a first image for processing from the first captured image and acquire a second image for processing from the second captured image;a region-of-interest extracting section configured to extract a first region of interest image and a second region of interest image, each including the pointer, from the first image for processing and the second image for processing; anda convolutional neural network including an input layer including a first input channel to which the first region of interest image is input and a second input channel to which the second region of interest image is input and an output layer that outputs a distance-related parameter related to a distance between the operation surface and the pointer.

9. The position detecting device according to claim 8, wherein the imaging section includes, with N set to an integer equal to or larger than 3, N cameras configured to image the pointer over the operation surface as the background to capture N captured images,the image-for-processing acquiring section acquires N images for processing from the N captured images,the region-of-interest extracting section extracts N region of interest images, each including the pointer, from the N images for processing, andthe convolutional neural network includes an input layer including N input channels to which the N region of interest images are input and an output layer that outputs the distance-related parameter.

10. The position detecting device according to claim 8, wherein the image-for-processing acquiring section creates N images for processing by performing stereo calibration on the first captured image and the second captured image.

11. The position detecting device according to claim 8, wherein the image-for-processing acquiring section acquires the first captured image and the second captured image as the first image for processing and the second image for processing.

12. The position detecting device according to claim 8, wherein the distance-related parameter is the distance between the operation surface and the pointer.

13. The position detecting device according to claim 8, wherein the distance-related parameter is an operation execution parameter indicating that operation on the operation surface corresponding to a position of the pointer is executed.

14. The position detecting device according to claim 8, further comprising: a first infrared illuminating section configured to include at least one of a coaxial illuminating section configured to perform coaxial illumination on the first camera and a peripheral illuminating section disposed to surround a periphery of an optical axis of the first camera;a second infrared illuminating section configured to include at least one of a coaxial illuminating section configured to perform coaxial illumination on the second camera and a peripheral illuminating section disposed to surround a periphery of an optical axis of the second camera; andan imaging control section configured to control imaging performed using the first camera and the first infrared illuminating section and the second camera and the second infrared illuminating section, andthe imaging control section sequentially selects the first camera and the first infrared illuminating section and the second camera and the second infrared illuminating section, executes imaging using the first camera while performing illumination with the first infrared illuminating section without performing illumination with the second infrared illuminating section, executes imaging using the second camera while performing illumination with the second infrared illuminating section without performing illumination with the first infrared illuminating section and sequentially captures the first captured image and the second captured image at different timings.

15. An interactive projector that detects a parameter related to a position of a pointer with respect to an operation surface, the interactive projector comprising: a projecting section configured to project a projection image onto the operation surface;an imaging section including a first camera configured to image the pointer over the operation surface as a background to capture a first captured image and a second camera disposed in a position different from a position of the first camera and configured to image the pointer over the operation surface as the background to capture a second captured image;an image-for-processing acquiring section configured to acquire a first image for processing from the first captured image and acquire a second image for processing from the second captured image;a region-of-interest extracting section configured to extract a first region of interest image and a second region of interest image, each including the pointer, from the first image for processing and the second image for processing; anda convolutional neural network including an input layer including a first input channel to which the first region of interest image is input and a second input channel to which the second region of interest image is input and an output layer that outputs a distance-related parameter related to a distance between the operation surface and the pointer.