Vectorial scale-based fuzzy-connected image segmentation
Computer Vision and Image Understanding
Unsupervised multiscale segmentation of color images
Pattern Recognition Letters
Graph regularization for color image processing
Computer Vision and Image Understanding
Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia
WaterBalloons: A hybrid watershed Balloon Snake segmentation
Image and Vision Computing
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Proceedings of the 30th DAGM symposium on Pattern Recognition
A Multicomponent Image Segmentation Framework
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Region-Oriented Visual Attention Framework for Activity Detection
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images
International Journal of Computer Vision
Validation of Watershed Regions by Scale-Space Statistics
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Vectorial scale-based fuzzy-connected image segmentation
Computer Vision and Image Understanding
Scale selection for compact scale-space representation of vector-valued images
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Unsupervised colour image segmentation using dual-tree complex wavelet transform
Computer Vision and Image Understanding
Segmentation of nerve fibers using multi-level gradient watershed and fuzzy systems
Artificial Intelligence in Medicine
Unsupervised segmentation and classification of cervical cell images
Pattern Recognition
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We present a new framework for the hierarchical segmentation of color images. The proposed scheme comprises a nonlinear scale-space with vector-valued gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image using three image components of distinct perceptual significance for a human observer, namely strong edges, smooth segments and detailed segments. The scale-space is based on a vector-valued diffusion that uses the Additive Operator Splitting numerical scheme. Furthermore, we introduce the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of images including natural and artificial scenes.