Texture Object Segmentation Based on Affine Invariant Texture Detection

TL;DR

This paper introduces a novel texture object segmentation method that leverages affine invariant detection, combining affine transforms, KLT verification, and an improved LBP approach with edge detection for robust segmentation.

Contribution

It presents a new segmentation technique based on affine invariance and improved LBP, enhancing accuracy and user interaction in complex textured images.

Findings

Effective segmentation of rich texture images.

Robust handling of various affine transformations.

User-friendly human-computer interaction.

Abstract

To solve the issue of segmenting rich texture images, a novel detection methods based on the affine invariable principle is proposed. Considering the similarity between the texture areas, we first take the affine transform to get numerous shapes, and utilize the KLT algorithm to verify the similarity. The transforms include rotation, proportional transformation and perspective deformation to cope with a variety of situations. Then we propose an improved LBP method combining canny edge detection to handle the boundary in the segmentation process. Moreover, human-computer interaction of this method which helps splitting the matched texture area from the original images is user-friendly.