3D Neural Model-Based Stopped Object Detection
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Image segmentation by automatic histogram thresholding
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
A 3D Neural Model for Video Analysis
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
Fast segmentation of porcelain images based on texture features
Journal of Visual Communication and Image Representation
Exploiting intensity inhomogeneity to extract textured objects from natural scenes
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed epsiv-neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets of coherent neighboring pixels which maximize the probability of being a single image content (including kinds of complex backgrounds). In practice, each set of coherent neighboring pixels corresponds to a coherence class (CC). The fact that each CC just contains a single equivalence class (EC) ensures the separability of an arbitrary image theoretically. In addition, the resultant CCs are represented by tree-based data structures, named connected coherence tree (CCT)s. In this sense, CCTA is a graph-based image analysis algorithm, which expresses three advantages: (1) its fundamental idea, epsiv-neighbor coherence segmentation criterion, is easy to interpret and comprehend; (2) it is efficient due to a linear computational complexity in the number of image pixels; (3) both subjective comparisons and objective evaluation have shown that it is effective for the tasks of semantic object segmentation and figure-ground separation in a wide variety of images. Those images either contain tiny, long and thin objects or are severely degraded by noise, uneven lighting, occlusion, poor illumination, and shadow.