Object Recognition Based on Sift Features and A Novel Feature Matching Algorithm

Vision based object recognition is the task of finding object in an image or video sequence by comparing them with an image of that object. Images can be taken from different viewpoints, different scales or even when they are translated or rotated. In this paper, a new algorithm is proposed for object recognition. First the feature points are extracted using scale invariant feature transform (SIFT), then an initial matching is established using photometrical features of the points. A novel geometric based method is used for optimizing matching points. After removing false matches, by using proposed algorithm, remaining key points can be categorized and each distinctive group is assigned to a separated object. The group with maximum likelihood is considered as desirable object. Important innovation in our method is the use of geometrical constraints of feature points between two matching images to find correct matching.