Hello
I am working on recognizing a given image in the world (through a photo). That is, a list of specific images is stored in the database, and when processing a photo with one of these images, it (the image) should be recognized.
I use SURF + flannBasedMatcher to recognize images, and Lowe's ratio test to sift out incorrect matches. But when comparing photos with many images stored in the database, there are still situations when the wrong image is recognized, since the number of keypoints is sufficient to find the image suitable.
I also wanted to tie the recognition of objects in the image in order to limit both database search and improve image recognition, but unfortunately, there are no suitable objects on my images that can be detected.
I think that comparing the contours of the necessary objects in the image and photo can help me, as an additional sign of recognition, but I don’t know yet how I can correctly implement this.
Help me please)
I use OpenCV 4 and python 3.6