Full Object Boundary Detection by Applying Scale Invariant Features in a Region Merging Segmentation Algorithm

TL;DR

This paper introduces a novel object detection method that combines scale-invariant features with a region merging segmentation algorithm to accurately detect objects and their boundaries in images.

Contribution

It presents a new approach integrating SIFT features into segmentation for improved object boundary detection, which is robust to scale, translation, and rotation.

Findings

Reliable object detection demonstrated

Effective full boundary extraction achieved

Robustness to scale, translation, and rotation

Abstract

Object detection is a fundamental task in computer vision and has many applications in image processing. This paper proposes a new approach for object detection by applying scale invariant feature transform (SIFT) in an automatic segmentation algorithm. SIFT is an invariant algorithm respect to scale, translation and rotation. The features are very distinct and provide stable keypoints that can be used for matching an object in different images. At first, an object is trained with different aspects for finding best keypoints. The object can be recognized in the other images by using achieved keypoints. Then, a robust segmentation algorithm is used to detect the object with full boundary based on SIFT keypoints. In segmentation algorithm, a merging role is defined to merge the regions in image with the assistance of keypoints. The results show that the proposed approach is reliable for object detection and can extract object boundary well.