Evaluating Object Recognition in Real-time Process

Abstract

Object recognition is one of the problems in computer vision and so many techniques have come up to solve. All of them employ machine learning, because the computer has to learn first and use it in future to say whether the query image matched or not. These proposes approaches for object recognition by applying scale and rotation invariant feature transform in an automatic segmentation algorithms like FAST,SURF, SIFT,ORB etc. The features should be discrete and stable so that it can be used for matching an object in different views. At first, an object is trained to find best features. The object can be recognized in the other images by using achieved feature points. The results should show that the proposed approach is reliable for object detection and should be robust to the noise.