An Automatic Algorithm for Object Recognition and Detection Based on ASIFT Keypoints

TL;DR

This paper introduces an automatic object recognition method combining ASIFT keypoints with a region merging algorithm, achieving high accuracy in detecting objects with full boundary detection across varied images.

Contribution

The paper presents a novel combination of affine-invariant feature detection and region merging for improved object recognition and boundary detection.

Findings

High recognition accuracy demonstrated in experiments

Effective boundary detection across different viewpoints

Robustness to affine transformations

Abstract

Object recognition is an important task in image processing and computer vision. This paper presents a perfect method for object recognition with full boundary detection by combining affine scale invariant feature transform (ASIFT) and a region merging algorithm. ASIFT is a fully affine invariant algorithm that means features are invariant to six affine parameters namely translation (2 parameters), zoom, rotation and two camera axis orientations. The features are very reliable and give us strong keypoints that can be used for matching between different images of an object. We trained an object in several images with different aspects for finding best keypoints of it. Then, a robust region merging algorithm is used to recognize and detect the object with full boundary in the other images based on ASIFT keypoints and a similarity measure for merging regions in the image. Experimental results show that the presented method is very efficient and powerful to recognize the object and detect it with high accuracy.