System and method for visual recognition
A method for visual recognition of an object in an electronic image includes extracting unique points of an object to be learned and/or a target object. The unique points are obtained by cross-correlating the image with a structure. Generally, the structure and/or the size of the structure may vary to detect extremum information associated with the learned object and/or target object. An icon corresponding to each of the unique points is extracted. The size of the icon corresponds to the scale of the unique point. After extraction of the various icons, an object becomes a collection of icons. Each of these icons is un-rotated and normalized or resized to a constant size so it can be compared with other icons. One of the unique properties of these icons is their stability over scale and angle. Thus, this invention allows the recognition of an image(s) or object(s) from large number of trained images or objects very quickly.
1. A method for matching a learned object with a target object, the method comprising:
extracting unique points from the target object, wherein the unique points are generated from extremum information obtained from a target image by cross-correlating at least one structure across the target image;
extracting an icon from the target image corresponding to each of the unique points;
normalizing the extracted icon; and
determining if the extracted icon from the target image matches a learned object.
2. The method of claim 1 , wherein the step of determining if the extracted icon from the target image matches a learned object includes searching a database of learned objects to determine a match.
3. The method of claim 1 further including detecting objects in the target image, and ranking the objects detected in the target image based on a match score.
4. The method of claim 3 , wherein objects having a higher matching score are processed prior to objects having a lower matching score.
5. The method of claim 1 further including detecting objects in the target image and localizing the detected objects to determine the location of the objects in the target image.
6. The method of claim 5 further including finding one or more corresponding icons in the extracted icon from one or more learned icons associated with at least one learned object.
7. The method of claim 6 further including computing a perspective transform for all of the unique points.
8. The method of claim 7 further including transforming object contour points with the perspective transform.
9. The method of claim 8 further including verifying the transformed contour points by superimposing the transformed contour points onto the image.
10. The method of claim 8 further including generating a redundancy measure.
11. The method of claim 5 further including selecting a predetermined number of unique points and computing a perspective transform for the predetermined number of unique points.
12. The method of claim 11 further including transforming object contour points with the perspective transform for the predetermined number of unique points.
13. The method of claim 12 further including verifying the transformed contour points by superimposing the transformed contour points onto the image.
14. The method of claim 5 further including computing a perspective transform for every set of 4 unique points.
15. The method of claim 1 , wherein the extracted icon comprises a plurality of extracted icons, and at least one of the plurality of icons at least partially overlaps with another icon, where each of the extracted icons have a different scale.
16. The method of claim 15 , wherein the extracted icons having a larger scale than the another icon.
17. The method of claim 16 , wherein the icon having a larger scale has a higher ranking than the another icon have a smaller scale.