Full Object Boundary Detection by Applying Scale Invariant Features in a Region Merging Segmentation Algorithm
This is an incremental improvement for computer vision applications, enhancing object boundary detection in image processing.
The paper tackled object detection in images by integrating scale invariant feature transform (SIFT) keypoints into a region merging segmentation algorithm to extract full object boundaries, showing reliable performance in boundary extraction.
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.