Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time texture synthesis by patch-based sampling
ACM Transactions on Graphics (TOG)
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields
Pattern Recognition Letters
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
Detecting regions of dynamic texture
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Simultaneous structure and texture image inpainting
IEEE Transactions on Image Processing
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The innovation of this paper is that we put forward a new algorithm of object detection form military infrared images with texture background according to the Mean-shift smooth and segmentation method combined with eight directions difference clustering. According to the texture characteristics of background, smoothing and clustering is carried out to extract the characteristics of object. The experimental results show that the algorithm is able to extract the object information form complex infrared texture background with better self-adapting and robustness. Future research particularly lies in raising the accuracy of object extracted.