Texture Object Segmentation Based on Affine Invariant Texture Detection
This addresses texture segmentation for computer vision applications, but appears incremental as it builds on existing techniques like LBP and KLT.
The paper tackles texture object segmentation in rich texture images by proposing an affine invariant texture detection method that uses affine transforms and KLT algorithm for similarity verification, combined with an improved LBP method and canny edge detection for boundary handling, resulting in a user-friendly human-computer interaction system.
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.