The subject matter disclosed herein relates in general to panoramic images and in particular to methods for obtaining such images with multi-cameras (e.g. dual-cameras).
Multi-aperture cameras (or multi-cameras) are becoming the standard choice of mobile device (e.g. smartphone, tablet, etc.) makers when designing cameras for their high-ends devices. A multi-camera setup usually comprises a wide field-of-view (FOV) (or “angle”) aperture (“Wide” or “W” camera), and one or more additional lenses, either with the same FOV (e.g. a depth auxiliary camera), with a narrower FOV (“Telephoto”, “Tele” or “T” camera, with a “Tele FOV” or FOVT) or with Wide FOV (FOVW) or ultra-wide FOV (FOVUW) (“Ultra-Wide” or “UW” camera).
In recent years, panoramic photography has gained popularity with mobile users, as it gives a photographer the ability to capture a scenery and its surroundings with very large FOV (in general in vertical direction). Some mobile device makers have recognized the trend and offer an ultra-wide-angle (or “ultra-Wide”) camera in the rear camera setup of a mobile device such as a smartphone. Nevertheless, capturing a scenery on a single aperture is limited and image stitching is required when a user wishes to capture a large FOV scene.
A panoramic image (or simply “regular panorama”) captured on a mobile device comprises a plurality of FOVW images stitched together. The W image data is the main camera data used for the stitching process, since having a Wide FOV (also marked “FOVW”), the final (stitched) image (referred to as “Wide panorama”) consumes less memory than that required for a Tele camera-based panorama (or simply “Tele panorama”) capturing the same scene. Additionally, the W camera has a larger depth-of-field than a T camera, leading to superior results in terms of focus. In comparison to an ultra-W camera, a W camera also demonstrates superior results in terms of distortion.
Since a Wide panorama is limited by a Wide image resolution, the ability to distinguish between fine details, mainly of far objects, is limited. A user who wishes to zoom in towards an “object of interest” (OOI) within the panorama image, i.e. perform digital zoom, will notice a blurred image due to Wide image resolution limits. Moreover, the panoramic image may be compressed to an even lower resolution than Wide image resolution in order to meet memory constraints.
There is need and it would be beneficial to combine the benefits of a panorama image having a very large FOV and Tele images having a large image resolution.
To increase the resolution of OOIs, systems and methods for obtaining a “smart panorama” are disclosed herein. A smart panorama comprises a Wide panorama and at least one Tele-based image of an OOI captured simultaneously. That is, a smart panorama as described herein refers to an image data array comprising (i) a panorama image as known in the art and (ii) a set of one or more high-resolution images of OOIs that are pinned or located within the panorama FOV. While the panorama is being captured, an additional process analyzes the W camera FOVW scene and identifies OOIs. Once an OOI is identified, the “best camera” is chosen out of the multi-camera array. The “best camera” selection may be between a plurality of cameras, or it may be between a single Tele camera having different operational modes such as different zoom states or different points of view (POVs). The “best camera” selection may be based on the OOI's object size, distance from the camera etc., and a capture request to the “best camera” is issued. The “best camera” selection may be defined by a Tele capture strategy such as described below. In some embodiments with cameras that have different optical zoom states, the “best camera” may be operated using a beneficial zoom state. In other embodiments with cameras that have a scanning FOV the “best camera” may be directed towards that OOI.
Note that a method disclosed herein is not limited to a specific multi-camera module and could be used for any combination of cameras as long as the combination consists of at least two cameras with a FOV ratio different than 1.
In current multi-camera systems, the FOVT is normally in the center part of the FOVW, defining a limited strip where interesting objects that have been detected trigger a capture request. A Tele camera with a 2D scanning capability extends the strip such that any object detected in the scanning range could be captured, i.e. provides “zoom anywhere”. Examples of cameras with 2D scanning capability may be found in co-owned international patent applications PCT/IB2016/057366, PCT/IB2019/053315 and PCT/IB2018/050988.
Tele cameras with multiple optical zoom states can adapt the zoom (and FOVT) according to e.g. size and distance of OOIs. Cameras with that capability may be found for example in co-owned US international patent applications No. PCT/IB2020/050002 and PCT/IB2020/051405.
The panorama being displayed to the user will contain some differentiating element marking the area of the panorama where high resolution OOI image information is present, such differentiating element marking may include, for example, a touchable rectangle box. By touching the box, the full resolution optically zoomed image will be displayed, allowing the user to enjoy both the panoramic view and the high-resolution zoom-in view.
In various embodiments there are provide handheld mobile electronic devices, comprising: a Wide camera for capturing Wide images, each Wide image having a respective Wide field of view (FOVW); a Tele camera for capturing Tele images, each Tele image having a respective Tele field of view (FOVT) smaller than FOVW; and a processor configured to stitch a plurality of Wide images with respective FOVW into a panorama image with a field of view FOVP>FOVW and to pin a Tele image to a given location within the panorama image to obtain a smart panorama image.
In some embodiments, each Wide image includes Wide scene information that is different from scene information of other Wide images.
In some embodiments, the processor is configured to crop the Tele image before pinning it to the given location.
In some embodiments, the Tele images are cropped according to aesthetic criteria.
In some embodiments, the Wide camera is configured to capture Wide images autonomously.
In some embodiments, the Tele camera is configured to capture the Tele images autonomously.
In some embodiments, the processor is configured to use a motion model that predicts a future movement of the handheld device.
In some embodiments, the processor is configured to use a motion model that predicts a future movement of an object within the FOVP.
In some embodiments, the processor is configured to use particular capture strategies for the autonomous capturing of the Tele images.
In some embodiments, the pinning a Tele image to a given location within the panorama image is obtained by executing localization between the Wide images and Tele images.
In some embodiments, the pinning a Tele image to a given location within the panorama image is obtained by executing localization between the panorama image and Tele images.
In some embodiments, the Tele camera has a plurality of zoom states.
In some embodiments, the processor is configured to autonomously select a particular zoom state from the plurality of zoom states.
In some embodiments, a particular zoom state from the plurality of zoom states is selected by a human user.
In some embodiments, the plurality of zoom states includes a discrete number.
In some embodiments, at least one of the plurality of zoom states can be modified continuously.
In some embodiments, the Tele camera is a scanning Tele camera.
In some embodiments, the processor is configured to autonomously direct scanning of the FOVT to a specific location within a scene.
In some embodiments, the FOVT scanning is performed by rotating one optical path folding element.
In some embodiments, the FOVT scanning is performed by rotating two or more optical path folding elements.
In some embodiments, each Tele image includes scene information from a center of the panorama image.
In some embodiments, scene information in the Tele images includes scene information from a field of view larger than a native Tele field of view and smaller than a Wide field of view.
In some embodiments, a particular segment of a scene is captured by the Tele camera and is pinned to locations within the panorama image.
In some embodiments, the processor uses a tracking algorithm to capture the particular segment of a scene with the Tele camera.
In some embodiments, a program decides which scene information captured by the Tele camera and pinned to locations within the panorama image.
In some embodiments, the processor is configured to calculate a saliency map based on Wide image data to decide which scene information is captured by the Tele camera and pinned to locations within the panorama image.
In some embodiments, the processor is configured to use a tracking algorithm to capture scene information with the Tele camera.
In some embodiments, the Tele image pinned to a given location within the panorama image is additionally shown in another location within the panorama image.
In some embodiments, the Tele image pinned to a given location within the panorama image is shown in an enlarged scale.
In various embodiments there are provided methods, comprising: providing a plurality of Wide images, each Wide image having a respective FOVW and including Wide scene information different from other Wide images; providing a plurality of Tele images, each Tele image having a respective FOVT that is smaller than FOVW; using a processor for stitching a plurality of Wide images into a panorama image with a panorama field of view FOVP>FOVW; and using the processor to pin at least one Tele image to a given location within the panorama image.
In some embodiments, the handheld device is manually moved by a user to capture scene information in the FOVP.
Non-limiting examples of embodiments disclosed herein are described below with reference to figures attached hereto that are listed following this paragraph. The drawings and descriptions are meant to illuminate and clarify embodiments disclosed herein and should not be considered limiting in any way. Like elements in different drawings may be indicated by like numerals. Elements in the drawings are not necessarily drawn to scale. In the drawings:
Non-limiting examples of embodiments disclosed herein are described below with reference to figures attached hereto that are listed following this paragraph. The drawings and descriptions are meant to illuminate and clarify embodiments disclosed herein and should not be considered limiting in any way. Like elements in different drawings may be indicated by like numerals. Elements in the drawings are not necessarily drawn to scale. In the drawings:
Regular panorama images can be captured with vertical or horizontal sensor orientation. The panorama capturing direction could be either left-to-right or right-to-left and can comprise any angle of view up to 360 degrees. This capturing is applicable to spherical, cylindrical or 3D panoramas.
In other examples and as shown in
In some embodiments, the FOV scanning of the T camera may be performed not by OPFE actuation. In some embodiments, the FOV scanning of the T camera may be performed not by actuating one OPFE, but by actuating two or more OPFEs. A scanning T camera that performs FOV scanning by actuating two OPFEs is described for example in co-owned U.S. provisional patent application No. 63/110,057 filed Nov. 5, 2020.
Electronic device 400 further comprises a W camera module 420 with a FOVW larger than FOVT of camera module 402. W camera module 420 includes a second lens module 422 that forms an image recorded by a second (Wide) image sensor 424. A second lens actuator 426 may move lens module 422 for focusing and/or OIS. In some embodiments, second calibration data may be stored in a second memory 428.
Electronic device 400 may further comprise an application processor (AP) 430. Application processor 440 comprises a T image signal processor (ISP) 432 and a W image ISP 434. Application processor 430 further comprises a Real-time module 436 that includes a salient ROI extractor 438, an object detector 440, an object tracker 442 and a camera controller 444. Application processor 440 further comprises a panorama module 448 and a smart panorama module 450.
In some embodiments, first calibration data may be stored in a first memory 416 of the T camera module, e.g. in an EEPROM (electrically erasable programmable read only memory). In other embodiments, first calibration data may be stored in a third memory 470 such as a NVM (non-volatile memory). The first calibration data may comprise calibration data between sensors of a W module 420 and the T module 402. In other embodiments, the second calibration data may be stored in third memory 452. The second calibration data may comprise calibration data between sensors of a W module 420 and the T module 402. The T module may have an effective focal length (EFL) of e.g. 8 mm-30 mm or more, a diagonal FOV of 10 deg-40 deg and a f number of about f/#=1.8-6. The W module may have an EFL of e.g. 2.5 mm-8 mm, a diagonal FOV of 50 deg-130 deg and f/#=1.0-2.5.
In use, a processing unit such as AP 430 may receive respective Wide and T image data from camera modules 402 and 420 and supply camera control signals to camera modules 402 and 420.
Salient ROI extractor 438 may calculate a saliency map for each W image. The saliency maps may be obtained by applying various saliency or salient-object-detection (SOD) algorithms, using classic computer vision methods or neural networks models. Examples to saliency methods can be found in datasets known in the art such as the “MIT Saliency Benchmark” and the “MIT/Tuebingen Saliency Benchmark”. Salient ROI extractor 438 also extracts salient Regions-Of-Interest (ROIs) and may contain the OOIs discussed above. For each salient object (or ROI), a surrounding bounding box is defined which may include a scene segment and a saliency score. The saliency score may be used to determine the influence of an object on future decisions as described in later steps. The saliency score is selected as a combination of parameters that reflect object properties, for example the size of the object and a representation of the saliency scores in each object.
In some embodiments, object detector 440 may detect objects in the W image simultaneously to the calculation of the saliency map and provide a semantic understanding of the objects in the scene. The semantic information extracted may be considered in calculating the saliency score.
In other embodiments, object detector 440 may detect objects in the W image after calculation of the saliency map. Object detector 440 may use only segments of the W image, e.g. only segments that are classified as saliency ROIs by salient ROI extractor 438. Object detector 440 may additionally provide a semantic understanding of the ROIs wherein the semantic information may be used to re-calculate the saliency score.
Object detector 440 may provide data such as information on an ROI's location and classification type to an object tracker 442, which may update camera controller 444 on the ROI's location as well as to the camera controller 458. Camera controller 444 may consider capturing a ROI in dependence of particular semantic labels or of a ROI's location (e.g. for considering hardware limitation such as a limited Tele FOV coverage of the Wide FOV) within the Wide FOV or of a saliency score above a certain threshold etc.
Panorama module 448 stitches a plurality of W images to a panorama image as known in the art. Smart panorama module 450 matches the high-resolution ROIs to their corresponding locations on the panorama image and to an image selection module (not shown) that selects the T images that are to be used in the smart panorama image.
Camera controller 444 may select or direct the T camera to capture the ROIs according to different Tele capture strategies for providing a best user experience. For providing a best user experience, camera controller 444 may a “best camera” e.g. by selecting a suitable ZF or by directing the native FOVT towards a ROI within the FOVT.
In some examples a “best user experience” may refer to T images of ROIs that provide information on OOIs in highest resolution (Tele capture “strategy example 1” or “SE 1”), and a respective Tele capture strategy that provides this may be selected. However, in other examples a best user experience may be provided by strategy examples such as:
capturing the Tele ROI that contains the OOI with the highest saliency score (“SE 2”);
capturing multiple OOIs in one ROI Tele capture (“SE 3”);
a uniform or non-uniform depth-of-field distribution between the different ROI Tele captures (“SE 4”);
including not only the OOI, but also a certain amount of background (“SE 5”) e.g. so that aesthetic cropping can be applied;
capturing a plurality of ROIs with a particular zoom factor (“SE 6”);
capturing multiple OOIs in one ROI Tele capture wherein the OOIs may be distributed according to a particular distribution within the Tele FOV (“SE 7”);
capturing one or more OOIs in one ROIs wherein the OOIs are to be located at particular positions or areas within the T image (“SE 8”);
capturing a plurality of ROIs with a particular zoom factors, e.g. so that the images of the ROIs or of particular OOIs which are formed on the image sensor may have a particular image size (“SE 9”);
a particular spectroscopic or colour composition range (“SE 10”);
a particular brightness range (“SE 11”); a particular scene characteristics which may be visual data (“SE 12”) such as texture;
including not only the OOI, but also a certain amount of background wherein the T camera settings may be selected so that the OOI may be in focus and the background may have some particular degree of optical bokeh (“SE 13”) or may have a minimal or maximal degree of optical bokeh (“SE 14”);
capturing with a higher preference specific types of OOIs, e.g. a user may be able to select whether e.g. animals or plants or buildings or humans may be captured by the Tele with a higher preference (“SE 15”); or
capturing a preferred type of OOI with higher preference in some particular state or condition, e.g. a human may be captured with open eyes with a higher preference or a bird may be captured with open wings with higher preference (“SE 16”) etc., or other criteria known in photography may be considered for best user experience.
The Tele capture strategies are respectively defined for providing a best user experience. According to the Tele capture strategy, camera controller 444 may adjust the settings of the T camera, e.g. with respect to a selected zoom factor or to a selected f number or to a POV that the scanning camera may be directed to etc. Other techniques described herein such as the calculation of a saliency map or the application of a motion model or the use of an object tracking algorithm etc. may be used or adapted e.g. by modifying settings to implement a particular Tele capture strategy.
In another embodiment, camera controller 444 may decide to capture a ROI that is a sub-region of an OOI that exceeds the native FOVT boundaries. Such objects will be referred to as “large” objects. When a “large” object is selected, salient ROIs extractor 438 may calculate an additional saliency map on the segment of the Wide FOV that contains the large object. The saliency map may be analysed, and the most visually attentive (salient) sub-region of the large object may be selected to be captured by the T camera. For example, the sub-region may replace the large object data in following calculation steps. Camera controller 444 may direct a scanning T camera towards the sub-region for capturing it.
Smart panorama module 450 may decide whether to save (capture) or discard a T image, e.g. smart panorama module 464 may save only the “best” images out of all T images captured. The best images may be defined as images that contain the largest amount of salient information. In other embodiments, the best images may include particular objects that may be of high value for the individual user, e.g. particular persons or animals Smart panorama module 450 may e.g. be taught automatically (e.g. by a machine learning procedure) or manually by the user which ROIs are to be considered best images. In yet other embodiments, the best images may be an image captured with a particular zoom factor, or a plurality of images including a ROI each, wherein each ROI may be captured with a particular zoom factor or some other property, e.g. so that the images of the ROIs which are formed on the image sensor may have a particular size, or a particular spectroscopic or colour composition range, or with a minimum degree of focus or defocus, or a particular brightness range, or a particular scene characteristics that may be visual data such as texture. In some embodiments, smart panorama module 450 may verify that newly captured images have non-overlapping FOVs with previously saved (i.e. already selected) images.
In some embodiments, object tracker 442 may track a selected ROI across consecutive W images. Different tracking methods may be used, e.g. Henriques et al. “High-speed tracking with kernelized correlation filters”. The object tracking may proceed until the ROI is captured by the T camera or until the object tracking process fails. In some embodiments, object tracker 442 may be configured as well for predicting a future position of the ROI, e.g. based on a current camera position and some motion model. For this prediction, an extension of a Kalman filter or any other motion estimation as known in the art may be used. Examples to Kalman filter methods can be found in the article “An Introduction to the Kalman Filter”, published by Welch and Bishop in 1995. The position prediction may be used for directing the scanning T camera to an expected future ROI position. In some embodiment, also the estimated velocity of an ROI may be considered. The velocity may refer to the velocity of e.g. an OOI with respect to other objects in the scene or to the velocity of e.g. an OOI with respect to the movement of electronic device 400.
In other embodiments, camera controller 444 may be configured to perform fault detection. The fault detection may for example raise an error in case that a particular threshold in terms of image quality or scene content may not be met. For example, an error may be raised if a certain threshold of (a) motion blur, (b) electronic noise, (c) defocus blur, obstructions in the scene or other undesired effects may be detected in the image. In some examples, in case a ROI image raised an error, this image will not be considered for a smart panorama image, and a scanning T camera may be instructed to re-direct to the scene segment comprising the ROI and to re-capture the ROI.
In other embodiments, camera controller 444 may consider further user inputs for a capture decision. User inputs may be intentional or unintentional. For example, eye tracking may be used to make a capture decision. For example, a user-facing camera may be used to automatically observe the eye movement of a user when watching on a screen of a camera hosting device or on the scene itself. For example, in case a user's eyes stay a significantly longer time on a particular scene segment than they stay on other scene segments, the given segment may be considered important to the user and may be captured with increased priority.
In other embodiments and for example for capturing objects that are large with respect to the Tele FOV or for capturing objects with very high resolution, camera controller 444 may be configured to capture a ROI not by a single T image, but by a plurality of T images that include different segments of an ROI. The plurality of T images may be stitched together to one image that may display the ROI in its entirety.
A final selection of best images may be performed by smart panorama module 450. Smart panorama module 450 may e.g. consider (i) the maximal storage capacity, (ii) FOV overlap across saved images, and (iii) the spatial distribution of the ROIs on a panorama FOV.
Smart panorama module 450 additionally includes a cropping module (not shown) that aims to find the cropping window that satisfies criteria such as providing best user experience as described above, as well as criteria from aesthetic image cropping, e.g. as described by Wang et al in the article “A deep network solution for attention and aesthetics aware photo cropping”, 2018.
In some embodiments, smart panorama module 450 may perform an additional saliency calculation on a stitched image with a FOV wider than the Wide FOV. For example, saliency information can be calculated by applying a saliency or SOD model on a segment of, or on the entire the panorama FOV.
In other embodiments, smart panorama module 450 may use semantic information to select T images to be used in the smart panorama image, e.g. by applying a detection algorithm. The chances of selecting a T image to be used in the smart panorama image may e.g. be elevated if human faces were detected by a face-detection algorithm.
The selected T images may be exemplarily displayed to the user via a tap on a rectangle marked on the smart panorama image, or with zoom transition from the smart panorama FOV to the native Tele FOV via zoom pinching.
In some examples, image data of the T images captured in step 510 may be used for the regular panorama image.
In another embodiment with a centered FOVT camera, the processing unit may determine the right timing for capturing the T image during the panorama capture.
determine the right timing for capturing the T image during the panorama capture.
In
While this disclosure has been described in terms of certain embodiments and generally associated methods, alterations and permutations of the embodiments and methods will be apparent to those skilled in the art. The disclosure is to be understood as not limited by the specific embodiments described herein, but only by the scope of the appended claims.
All references mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual reference was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present application.
This is a 371 of international patent application PCT/IB2020/061461 filed Dec. 3, 2020, and claims priority from U.S. Provisional Patent Application No. 62/945,519 filed Dec. 9, 2019, which is expressly incorporated herein by reference in its entirety.
Filing Document | Filing Date | Country | Kind |
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PCT/IB2020/061461 | 12/3/2020 | WO |
Publishing Document | Publishing Date | Country | Kind |
---|---|---|---|
WO2021/116851 | 6/17/2021 | WO | A |
Number | Name | Date | Kind |
---|---|---|---|
4199785 | McCullough et al. | Apr 1980 | A |
5005083 | Grage et al. | Apr 1991 | A |
5032917 | Aschwanden | Jul 1991 | A |
5041852 | Misawa et al. | Aug 1991 | A |
5051830 | von Hoessle | Sep 1991 | A |
5099263 | Matsumoto et al. | Mar 1992 | A |
5248971 | Mandl | Sep 1993 | A |
5287093 | Amano et al. | Feb 1994 | A |
5394520 | Hall | Feb 1995 | A |
5436660 | Sakamoto | Jul 1995 | A |
5444478 | Lelong et al. | Aug 1995 | A |
5459520 | Sasaki | Oct 1995 | A |
5502537 | Utagawa | Mar 1996 | A |
5657402 | Bender et al. | Aug 1997 | A |
5682198 | Katayama et al. | Oct 1997 | A |
5768443 | Michael et al. | Jun 1998 | A |
5926190 | Furkowski et al. | Jul 1999 | A |
5940641 | McIntyre et al. | Aug 1999 | A |
5982951 | Katayama et al. | Nov 1999 | A |
6101334 | Fantone | Aug 2000 | A |
6128416 | Oura | Oct 2000 | A |
6148120 | Sussman | Nov 2000 | A |
6208765 | Bergen | Mar 2001 | B1 |
6268611 | Pettersson et al. | Jul 2001 | B1 |
6549215 | Jouppi | Apr 2003 | B2 |
6611289 | Yu et al. | Aug 2003 | B1 |
6643416 | Daniels et al. | Nov 2003 | B1 |
6650368 | Doron | Nov 2003 | B1 |
6680748 | Monti | Jan 2004 | B1 |
6714665 | Hanna et al. | Mar 2004 | B1 |
6724421 | Glatt | Apr 2004 | B1 |
6738073 | Park et al. | May 2004 | B2 |
6741250 | Furlan et al. | May 2004 | B1 |
6750903 | Miyatake et al. | Jun 2004 | B1 |
6778207 | Lee et al. | Aug 2004 | B1 |
7002583 | Rabb, III | Feb 2006 | B2 |
7015954 | Foote et al. | Mar 2006 | B1 |
7038716 | Klein et al. | May 2006 | B2 |
7199348 | Olsen et al. | Apr 2007 | B2 |
7206136 | Labaziewicz et al. | Apr 2007 | B2 |
7248294 | Slatter | Jul 2007 | B2 |
7256944 | Labaziewicz et al. | Aug 2007 | B2 |
7305180 | Labaziewicz et al. | Dec 2007 | B2 |
7339621 | Fortier | Mar 2008 | B2 |
7346217 | Gold, Jr. | Mar 2008 | B1 |
7365793 | Cheatle et al. | Apr 2008 | B2 |
7411610 | Doyle | Aug 2008 | B2 |
7424218 | Baudisch et al. | Sep 2008 | B2 |
7509041 | Hosono | Mar 2009 | B2 |
7533819 | Barkan et al. | May 2009 | B2 |
7619683 | Davis | Nov 2009 | B2 |
7738016 | Toyofuku | Jun 2010 | B2 |
7773121 | Huntsberger et al. | Aug 2010 | B1 |
7809256 | Kuroda et al. | Oct 2010 | B2 |
7880776 | LeGall et al. | Feb 2011 | B2 |
7918398 | Li et al. | Apr 2011 | B2 |
7964835 | Olsen et al. | Jun 2011 | B2 |
7978239 | Deever et al. | Jul 2011 | B2 |
8115825 | Culbert et al. | Feb 2012 | B2 |
8149327 | Lin et al. | Apr 2012 | B2 |
8154610 | Jo et al. | Apr 2012 | B2 |
8238695 | Davey et al. | Aug 2012 | B1 |
8274552 | Dahi et al. | Sep 2012 | B2 |
8390729 | Long et al. | Mar 2013 | B2 |
8391697 | Cho et al. | Mar 2013 | B2 |
8400555 | Georgiev et al. | Mar 2013 | B1 |
8439265 | Ferren et al. | May 2013 | B2 |
8446484 | Muukki et al. | May 2013 | B2 |
8483452 | Ueda et al. | Jul 2013 | B2 |
8514491 | Duparre | Aug 2013 | B2 |
8547389 | Hoppe et al. | Oct 2013 | B2 |
8553106 | Scarff | Oct 2013 | B2 |
8587691 | Takane | Nov 2013 | B2 |
8619148 | Watts et al. | Dec 2013 | B1 |
8803990 | Smith | Aug 2014 | B2 |
8896655 | Mauchly et al. | Nov 2014 | B2 |
8976255 | Matsuoto et al. | Mar 2015 | B2 |
9019387 | Nakano | Apr 2015 | B2 |
9025073 | Attar et al. | May 2015 | B2 |
9025077 | Attar et al. | May 2015 | B2 |
9041835 | Honda | May 2015 | B2 |
9137447 | Shibuno | Sep 2015 | B2 |
9185291 | Shabtay et al. | Nov 2015 | B1 |
9215377 | Sokeila et al. | Dec 2015 | B2 |
9215385 | Luo | Dec 2015 | B2 |
9270875 | Brisedoux et al. | Feb 2016 | B2 |
9286680 | Jiang et al. | Mar 2016 | B1 |
9344626 | Silverstein et al. | May 2016 | B2 |
9360671 | Zhou | Jun 2016 | B1 |
9369621 | Malone et al. | Jun 2016 | B2 |
9413930 | Geerds | Aug 2016 | B2 |
9413984 | Attar et al. | Aug 2016 | B2 |
9420180 | Jin | Aug 2016 | B2 |
9438792 | Nakada et al. | Sep 2016 | B2 |
9485432 | Medasani et al. | Nov 2016 | B1 |
9578257 | Attar et al. | Feb 2017 | B2 |
9618748 | Munger et al. | Apr 2017 | B2 |
9681057 | Attar et al. | Jun 2017 | B2 |
9723220 | Sugie | Aug 2017 | B2 |
9736365 | Laroia | Aug 2017 | B2 |
9736391 | Du et al. | Aug 2017 | B2 |
9768310 | Ahn et al. | Sep 2017 | B2 |
9800798 | Ravirala et al. | Oct 2017 | B2 |
9851803 | Fisher et al. | Dec 2017 | B2 |
9894287 | Qian et al. | Feb 2018 | B2 |
9900522 | Lu | Feb 2018 | B2 |
9927600 | Goldenberg et al. | Mar 2018 | B2 |
20020005902 | Yuen | Jan 2002 | A1 |
20020030163 | Zhang | Mar 2002 | A1 |
20020054214 | Yoshikawa | May 2002 | A1 |
20020063711 | Park et al. | May 2002 | A1 |
20020075258 | Park et al. | Jun 2002 | A1 |
20020122113 | Foote | Sep 2002 | A1 |
20020167741 | Koiwai et al. | Nov 2002 | A1 |
20030030729 | Prentice et al. | Feb 2003 | A1 |
20030093805 | Gin | May 2003 | A1 |
20030160886 | Misawa et al. | Aug 2003 | A1 |
20030202113 | Yoshikawa | Oct 2003 | A1 |
20040008773 | Itokawa | Jan 2004 | A1 |
20040012683 | Yamasaki et al. | Jan 2004 | A1 |
20040017386 | Liu et al. | Jan 2004 | A1 |
20040027367 | Pilu | Feb 2004 | A1 |
20040061788 | Bateman | Apr 2004 | A1 |
20040141065 | Hara et al. | Jul 2004 | A1 |
20040141086 | Mihara | Jul 2004 | A1 |
20040240052 | Minefuji et al. | Dec 2004 | A1 |
20050013509 | Samadani | Jan 2005 | A1 |
20050046740 | Davis | Mar 2005 | A1 |
20050157184 | Nakanishi et al. | Jul 2005 | A1 |
20050168834 | Matsumoto et al. | Aug 2005 | A1 |
20050185049 | Iwai et al. | Aug 2005 | A1 |
20050200718 | Lee | Sep 2005 | A1 |
20060054782 | Olsen et al. | Mar 2006 | A1 |
20060056056 | Ahiska et al. | Mar 2006 | A1 |
20060067672 | Washisu et al. | Mar 2006 | A1 |
20060102907 | Lee et al. | May 2006 | A1 |
20060125937 | LeGall et al. | Jun 2006 | A1 |
20060170793 | Pasquarette et al. | Aug 2006 | A1 |
20060175549 | Miller et al. | Aug 2006 | A1 |
20060187310 | Janson et al. | Aug 2006 | A1 |
20060187322 | Janson et al. | Aug 2006 | A1 |
20060187338 | May et al. | Aug 2006 | A1 |
20060227236 | Pak | Oct 2006 | A1 |
20070024737 | Nakamura et al. | Feb 2007 | A1 |
20070126911 | Nanjo | Jun 2007 | A1 |
20070177025 | Kopet et al. | Aug 2007 | A1 |
20070188653 | Pollock et al. | Aug 2007 | A1 |
20070189386 | Imagawa et al. | Aug 2007 | A1 |
20070257184 | Olsen et al. | Nov 2007 | A1 |
20070285550 | Son | Dec 2007 | A1 |
20080017557 | Witdouck | Jan 2008 | A1 |
20080024614 | Li et al. | Jan 2008 | A1 |
20080025634 | Border et al. | Jan 2008 | A1 |
20080030592 | Border et al. | Feb 2008 | A1 |
20080030611 | Jenkins | Feb 2008 | A1 |
20080084484 | Ochi et al. | Apr 2008 | A1 |
20080106629 | Kurtz et al. | May 2008 | A1 |
20080117316 | Orimoto | May 2008 | A1 |
20080129831 | Cho et al. | Jun 2008 | A1 |
20080218611 | Parulski et al. | Sep 2008 | A1 |
20080218612 | Border et al. | Sep 2008 | A1 |
20080218613 | Janson et al. | Sep 2008 | A1 |
20080219654 | Border et al. | Sep 2008 | A1 |
20090086074 | Li et al. | Apr 2009 | A1 |
20090109556 | Shimizu et al. | Apr 2009 | A1 |
20090122195 | Van Baar et al. | May 2009 | A1 |
20090122406 | Rouvinen et al. | May 2009 | A1 |
20090128644 | Camp et al. | May 2009 | A1 |
20090219547 | Kauhanen et al. | Sep 2009 | A1 |
20090252484 | Hasuda et al. | Oct 2009 | A1 |
20090295949 | Ojala | Dec 2009 | A1 |
20090313267 | Girgensohn | Dec 2009 | A1 |
20090324135 | Kondo et al. | Dec 2009 | A1 |
20100013906 | Border et al. | Jan 2010 | A1 |
20100020221 | Tupman et al. | Jan 2010 | A1 |
20100060746 | Olsen et al. | Mar 2010 | A9 |
20100097444 | Lablans | Apr 2010 | A1 |
20100103194 | Chen et al. | Apr 2010 | A1 |
20100165131 | Makimoto et al. | Jul 2010 | A1 |
20100196001 | Ryynänen et al. | Aug 2010 | A1 |
20100238327 | Griffith et al. | Sep 2010 | A1 |
20100259836 | Kang et al. | Oct 2010 | A1 |
20100283842 | Guissin et al. | Nov 2010 | A1 |
20100321494 | Peterson et al. | Dec 2010 | A1 |
20110058320 | Kim et al. | Mar 2011 | A1 |
20110063417 | Peters et al. | Mar 2011 | A1 |
20110063446 | McMordie et al. | Mar 2011 | A1 |
20110064327 | Dagher et al. | Mar 2011 | A1 |
20110080487 | Venkataraman et al. | Apr 2011 | A1 |
20110128288 | Petrou et al. | Jun 2011 | A1 |
20110164172 | Shintani et al. | Jul 2011 | A1 |
20110229054 | Weston et al. | Sep 2011 | A1 |
20110234798 | Chou | Sep 2011 | A1 |
20110234853 | Hayashi et al. | Sep 2011 | A1 |
20110234881 | Wakabayashi et al. | Sep 2011 | A1 |
20110242286 | Pace et al. | Oct 2011 | A1 |
20110242355 | Goma et al. | Oct 2011 | A1 |
20110298966 | Kirschstein et al. | Dec 2011 | A1 |
20120026366 | Golan et al. | Feb 2012 | A1 |
20120044372 | Cote et al. | Feb 2012 | A1 |
20120062780 | Morihisa | Mar 2012 | A1 |
20120069235 | Imai | Mar 2012 | A1 |
20120075489 | Nishihara | Mar 2012 | A1 |
20120105579 | Jeon et al. | May 2012 | A1 |
20120124525 | Kang | May 2012 | A1 |
20120154547 | Aizawa | Jun 2012 | A1 |
20120154614 | Moriya et al. | Jun 2012 | A1 |
20120196648 | Havens et al. | Aug 2012 | A1 |
20120229663 | Nelson et al. | Sep 2012 | A1 |
20120249815 | Bohn et al. | Oct 2012 | A1 |
20120287315 | Huang et al. | Nov 2012 | A1 |
20120320467 | Baik et al. | Dec 2012 | A1 |
20130002928 | Imai | Jan 2013 | A1 |
20130016427 | Sugawara | Jan 2013 | A1 |
20130063629 | Webster et al. | Mar 2013 | A1 |
20130076922 | Shihoh et al. | Mar 2013 | A1 |
20130093842 | Yahata | Apr 2013 | A1 |
20130094126 | Rappoport et al. | Apr 2013 | A1 |
20130113894 | Mirlay | May 2013 | A1 |
20130135445 | Dahi et al. | May 2013 | A1 |
20130155176 | Paripally et al. | Jun 2013 | A1 |
20130182150 | Asakura | Jul 2013 | A1 |
20130201360 | Song | Aug 2013 | A1 |
20130202273 | Ouedraogo et al. | Aug 2013 | A1 |
20130235224 | Park et al. | Sep 2013 | A1 |
20130250150 | Malone et al. | Sep 2013 | A1 |
20130258044 | Betts-Lacroix | Oct 2013 | A1 |
20130270419 | Singh et al. | Oct 2013 | A1 |
20130278785 | Nomura et al. | Oct 2013 | A1 |
20130321668 | Kamath | Dec 2013 | A1 |
20140009631 | Topliss | Jan 2014 | A1 |
20140049615 | Uwagawa | Feb 2014 | A1 |
20140118584 | Lee et al. | May 2014 | A1 |
20140160311 | Hwang et al. | Jun 2014 | A1 |
20140192238 | Attar et al. | Jul 2014 | A1 |
20140192253 | Laroia | Jul 2014 | A1 |
20140218587 | Shah | Aug 2014 | A1 |
20140313316 | Olsson et al. | Oct 2014 | A1 |
20140362242 | Takizawa | Dec 2014 | A1 |
20150002683 | Hu et al. | Jan 2015 | A1 |
20150042870 | Chan et al. | Feb 2015 | A1 |
20150070781 | Cheng et al. | Mar 2015 | A1 |
20150092066 | Geiss et al. | Apr 2015 | A1 |
20150103147 | Ho et al. | Apr 2015 | A1 |
20150138381 | Ahn | May 2015 | A1 |
20150154776 | Zhang et al. | Jun 2015 | A1 |
20150162048 | Hirata et al. | Jun 2015 | A1 |
20150195458 | Nakayama et al. | Jul 2015 | A1 |
20150215516 | Dolgin | Jul 2015 | A1 |
20150237280 | Choi et al. | Aug 2015 | A1 |
20150242994 | Shen | Aug 2015 | A1 |
20150244906 | Wu et al. | Aug 2015 | A1 |
20150253543 | Mercado | Sep 2015 | A1 |
20150253647 | Mercado | Sep 2015 | A1 |
20150261299 | Wajs | Sep 2015 | A1 |
20150271471 | Hsieh et al. | Sep 2015 | A1 |
20150281678 | Park et al. | Oct 2015 | A1 |
20150286033 | Osborne | Oct 2015 | A1 |
20150316744 | Chen | Nov 2015 | A1 |
20150334309 | Peng et al. | Nov 2015 | A1 |
20160044250 | Shabtay et al. | Feb 2016 | A1 |
20160070088 | Koguchi | Mar 2016 | A1 |
20160154202 | Wippermann et al. | Jun 2016 | A1 |
20160154204 | Lim et al. | Jun 2016 | A1 |
20160212358 | Shikata | Jul 2016 | A1 |
20160212418 | Demirdjian et al. | Jul 2016 | A1 |
20160241751 | Park | Aug 2016 | A1 |
20160291295 | Shabtay et al. | Oct 2016 | A1 |
20160295112 | Georgiev et al. | Oct 2016 | A1 |
20160301840 | Du et al. | Oct 2016 | A1 |
20160353008 | Osborne | Dec 2016 | A1 |
20160353012 | Kao et al. | Dec 2016 | A1 |
20170019616 | Zhu et al. | Jan 2017 | A1 |
20170070731 | Darling et al. | Mar 2017 | A1 |
20170187962 | Lee et al. | Jun 2017 | A1 |
20170214846 | Du et al. | Jul 2017 | A1 |
20170214866 | Zhu et al. | Jul 2017 | A1 |
20170242225 | Fiske | Aug 2017 | A1 |
20170289458 | Song et al. | Oct 2017 | A1 |
20180013944 | Evans, V et al. | Jan 2018 | A1 |
20180017844 | Yu et al. | Jan 2018 | A1 |
20180024329 | Goldenberg et al. | Jan 2018 | A1 |
20180059379 | Chou | Mar 2018 | A1 |
20180120674 | Avivi et al. | May 2018 | A1 |
20180150973 | Tang et al. | May 2018 | A1 |
20180176426 | Wei et al. | Jun 2018 | A1 |
20180198897 | Tang et al. | Jul 2018 | A1 |
20180241922 | Baldwin et al. | Aug 2018 | A1 |
20180295292 | Lee et al. | Oct 2018 | A1 |
20180300901 | Wakai et al. | Oct 2018 | A1 |
20190121103 | Bachar et al. | Apr 2019 | A1 |
20200020085 | Pekkucuksen | Jan 2020 | A1 |
20220277463 | Schlattmann | Sep 2022 | A1 |
Number | Date | Country |
---|---|---|
101276415 | Oct 2008 | CN |
201514511 | Jun 2010 | CN |
102739949 | Oct 2012 | CN |
103024272 | Apr 2013 | CN |
103841404 | Jun 2014 | CN |
1536633 | Jun 2005 | EP |
1780567 | May 2007 | EP |
2523450 | Nov 2012 | EP |
S59191146 | Oct 1984 | JP |
04211230 | Aug 1992 | JP |
H07318864 | Dec 1995 | JP |
08271976 | Oct 1996 | JP |
2002010276 | Jan 2002 | JP |
2003298920 | Oct 2003 | JP |
2004133054 | Apr 2004 | JP |
2004245982 | Sep 2004 | JP |
2005099265 | Apr 2005 | JP |
2006238325 | Sep 2006 | JP |
2007228006 | Sep 2007 | JP |
2007306282 | Nov 2007 | JP |
2008076485 | Apr 2008 | JP |
2010204341 | Sep 2010 | JP |
2011085666 | Apr 2011 | JP |
2013106289 | May 2013 | JP |
20070005946 | Jan 2007 | KR |
20090058229 | Jun 2009 | KR |
20100008936 | Jan 2010 | KR |
20140014787 | Feb 2014 | KR |
101477178 | Dec 2014 | KR |
20140144126 | Dec 2014 | KR |
20150118012 | Oct 2015 | KR |
2000027131 | May 2000 | WO |
2004084542 | Sep 2004 | WO |
2006008805 | Jan 2006 | WO |
2010122841 | Oct 2010 | WO |
2014072818 | May 2014 | WO |
2017025822 | Feb 2017 | WO |
2017037688 | Mar 2017 | WO |
2018130898 | Jul 2018 | WO |
Entry |
---|
Statistical Modeling and Performance Characterization of a Real-Time Dual Camera Surveillance System, Greienhagen et al., Publisher: IEEE, 2000, 8 pages. |
A 3MPixel Multi-Aperture Image Sensor with 0.7μm Pixels in 0.11μm CMOS, Fife et al., Stanford University, 2008, 3 pages. |
Dual camera intelligent sensor for high definition 360 degrees surveillance, Scotti et al., Publisher: IET, May 9, 2000, 8 pages. |
Dual-sensor foveated imaging system, Hua et al., Publisher: Optical Society of America, Jan. 14, 2008, 11 pages. |
Defocus Video Matting, McGuire et al., Publisher: ACM SIGGRAPH, Jul. 31, 2005, 11 pages. |
Compact multi-aperture imaging with high angular resolution, Santacana et al., Publisher: Optical Society of America, 2015, 10 pages. |
Multi-Aperture Photography, Green et al., Publisher: Mitsubishi Electric Research Laboratories, Inc., Jul. 2007, 10 pages. |
Multispectral Bilateral Video Fusion, Bennett et al., Publisher: IEEE, May 2007, 10 pages. |
Super-resolution imaging using a camera array, Santacana et al., Publisher: Optical Society of America, 2014, 6 pages. |
Optical Splitting Trees for High-Precision Monocular Imaging, McGuire et al., Publisher: IEEE, 2007, 11 pages. |
High Performance Imaging Using Large Camera Arrays, Wilburn et al., Publisher: Association for Computing Machinery, Inc., 2005, 12 pages. |
Real-time Edge-Aware Image Processing with the Bilateral Grid, Chen et al., Publisher: ACM SIGGRAPH, 2007, 9 pages. |
Superimposed multi-resolution imaging, Carles et al., Publisher: Optical Society of America, 2017, 13 pages. |
Viewfinder Alignment, Adams et al., Publisher: EUROGRAPHICS, 2008, 10 pages. |
Dual-Camera System for Multi-Level Activity Recognition, Bodor et al., Publisher: IEEE, Oct. 2014, 6 pages. |
Engineered to the task: Why camera-phone cameras are different, Giles Humpston, Publisher: Solid State Technology, Jun. 2009, 3 pages. |
European Search Report in related EP patent application 20897934.4, dated Oct. 14, 2022. |
Office Action in related EP patent application 20897934.4, dated Oct. 26, 2022. |
Number | Date | Country | |
---|---|---|---|
20220303464 A1 | Sep 2022 | US |
Number | Date | Country | |
---|---|---|---|
62945519 | Dec 2019 | US |