This application claims the priority benefit of CN application serial No. 201310241893.4, tiled on Jun. 18, 2013. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of specification.
1. Field of the Invention
The invention relates to a recognition method and a system and more particularly to an image recognition method and an image recognition system.
2. Description of the Related Art
As technology develops, human-computer interface gradually become intuitive and human friendly. For example, input tools such as a keyboard or a mouse is used in computers, and a touch panel is used in tablets. Nowadays, a gesture recognition technique is developed for the interaction between a user and a computer which is more convenient and intuitive.
A single lens camera has low stability and captures less availability information in gesture recognition. Therefore, a twin lens camera or a single-lens cooperated with an infrared ray camera is currently used in the conventional gesture recognition technique for images capturing.
tIn addition, practically, the conventional gesture recognition method comprises steps of: captures images via a twin lens camera for a single-lens cooperated with an infrared camera) to analyze whether a user hand exists in the image recognizes a static gesture of the hand, and compares the static gesture with gestures in the database. It is time consuming, and the accuracy of the recognition is low.
A recognition method is provided, it includes the following steps:
capturing a plurality of images; analyzing the images to get a target object; analyzing the target object to get color information and characteristic information; calculating a current image according to the color information and the characteristic information to get a probability distribution map; comparing a difference between the current image and a previous image of the current image to get dynamic information; and recognizing the target object according to the probability distribution map and the dynamic information.
An image recognition system is also provided herein. The image recognition system includes an image acquiring device and a processor, the processor is electrically coupled to the image acquiring device for executing a plurality of instructions, and the instructions include:
analyzing the images to get a target object; analyzing the target object to get color information and characteristic information; calculating a current image according to the color information and the characteristic information to get a probability distribution map; comparing a difference between the current image and a previous image of the current image to get dynamic information; and recognizing the target object according to the probability distribution map and the dynamic information.
An image recognition method and an image recognition system are provided in low cost, time saving while analysis and comparison, and increase the accuracy rate of the recognition.
An image recognition method 100 is provided, the steps are shown in
step 110: capturing a plurality of images;
step 120: analyzing the images to get a target object;
step 130: analyzing the target object to get color information and characteristic information;
step 140: calculating a current image according to the color information and the characteristic information to get a probability distribution map;
step 150: comparing a difference between the current image and a. previous image of the current image to get dynamic information; and
step 160: recognizing the target object according to the probability distribution map and the dynamic information.
In detail, in the embodiment, the image recognition method 100 is used for recognizing gestures of users, however, the image recognition method 100 can also be adapted to recognize a human face, a car, etc., which is not limited herein.
In an embodiment, the beginning steps 110 to 130 of the above steps are pre-steps to obtain certain information of a user's hand for the subsequent steps, which makes the hand be recognized more simply and correctly.
In detail, a plurality of images are captured in the step 110; the images are analyzed to get the target object in the step 120, for example, movement information and shape information of the images are analyzed to get hand information; pixels of the hand are analyzed to get the color information and the characteristic information in step 130, for instance, the color information may be the color of the hand and the characteristic information may be the palm lines on the hand, further, the characteristic information may be the depth of palm lines, the direction of palm lines and the relative position between different palm lines.
The certain information of the hand is obtained after pre-steps, and the certain information represents the hand in the subsequent steps. In other words, when the color information and the characteristic information exist in the image, which represents that the band appears in the image. However, to recognize the hand in the image more quickly and accurately, please refer to the subsequent steps.
Practically, the images are continually captured, and the current image is recognized continuously as shown in the step 140. First, the current image is statistically computed according to the color information and the characteristic information to get the probability distribution map. The color information and the characteristic information in the image can represent the hand, therefore after the information current image calculated according to the color information and the characteristic information, the probability distribution map of the hand distribution in the image is obtained.
On the one hand, in the step 150, the difference between the current image and the previous image of the current image is compared to get the dynamic information. In detail, when a hand moves, the position of the hand in the current image is different from in that in the previous image, therefore, the difference between the current image and the previous image of the current image can be regarded as the difference of the hand movement, and the difference will be found and regarded as the dynamic information. In other words, the difference is most probably the position of the hand in the image, and the difference can provided as the dynamic information. Furthermore, to get more accurate dynamic information, the comparation can be executed between the current image and a plurality of pervious images (such as ten pervious images) to get the difference.
Then, after the probability distribution and the dynamic information are obtained at the steps 140 and 150, respectively, since they both record the information that the hand has high probability to appear in the image, the intersection of the probability distribution map and the dynamic information are used to recognize the target object in the step 160.
Comparing to the conventional technique, via the step 140, the position of the hand in the image can be preliminarily confirmed more quickly through the probability distribution map. In addition, since only the moving part in the two images is recognized in the step 150, the position of the hand in the image can be confirmed more quickly and accurately, consequently, the hand in the image can be recognized much faster and more accurately according to the image recognition method 100. Moreover, the image recognition method 100 in the embodiment only needs a single image acquiring device, which can further save the cost.
In an embodiment, as shown in the image 210 in
However, as shown in
The difference between the current image and the previous image is compared in step 150, furthermore, in an embodiment, the current image and the previous images are also compared with a background model to get dynamic information for more accuracy. The dynamic information can refer to the image 240 in
Moreover, in an embodiment, please refer to
In an embodiment, when the pattern change or the movement of the hand of the hand is recognized, a corresponding function is enabled accordingly.
In an embodiment, the image recognition method 100 further includes that the noise of the images is filtered out to increase the accuracy of the image recognition method 100.
The image recognition method 100 can be accomplished via an image recognition system 300 as shown in
analyzing the images to get a target object;
analyzing the target object to get color information and characteristic information;
calculating a current image according to the color information and the characteristic information to get a probability distribution map;
comparing a difference between the current image and a previous image of the current image to get dynamic information; and
recognizing the target object according to the probability distribution map and the dynamic information.
It should be noted that those instructions executed by the processor 320 have been described in the image recognition method 100, which are omitted herein for a concise purpose.
Further, the probability distribution map includes a plurality of high probability areas, and the processor 320 of the image recognition system 300 is used for executing the following instructions:
filtering out noise of the images;
statistically computing probability whether each pixel of the current image belongs to the target object according to the color information and the characteristic information to get the probability distribution map;
filtering the high probability areas in probability distribution map according to morphology;
comparing a difference among the current image, the previous image of the current image and a background model to get the dynamic information;
recognizing a pattern change and a movement of the target object according to the probability distribution map and the dynamic information; and
enabling a corresponding function in a computer according to the pattern change and the movement of the target object.
Similarly, the instructions executed by the processor 320 have been described in the image recognition method 100, which are omitted herein for a concise purpose.
The image recognition method 100 can be executed by software, hardware and/or firmware. For example, if considering the execution speed and accuracy first, the hardware and/or firmware can be chosen; if considering the design flexibility first, software can be chosen. Software, hardware and firmware also may be used in cooperation.
Further, the steps of the image recognition method 100 are named according to the function, which is not used for limiting the steps. The steps may be combined into one step, or a step is divided into multiple steps, or a step is replaced b another step, which is not limited herein.
Although the invention has been disclosed with reference to certain preferred embodiments thereof, the disclosure is not for limiting the scope. Persons having ordinary skill in the art may make various modifications and changes without departing from the spirit and the scope of the invention. Therefore, the scope of the appended claims should not be limited to the description of the preferred embodiments described above.
Number | Date | Country | Kind |
---|---|---|---|
201310241893.4 | Jun 2013 | CN | national |