Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
On Selecting an Appropriate Colour Space for Skin Detection
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
A color neuromorphic approach for motion estimation
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Fourier fractal descriptors for colored texture analysis
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Colour descriptors for tracking in spatial augmented reality
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
A novel background subtraction method based on color invariants
Computer Vision and Image Understanding
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For grey-value images, it is well accepted that the neighborhood rather than the pixel carries the geometrical interpretation. Interestingly the spatial configuration of the neighborhood is the basis for the perception of humans. Common practise in color image processing, is to use the color information without considering the spatial structure. We aim at a physical basis for the local interpretation of color images. We propose a framework for spatial color measurement, based on the Gaussian scale-space theory. We consider a Gaussian color model, which inherently uses the spatial and color information in an integrated model. The framework is well-founded in physics as well as in measurement science. The framework delivers sound and robust spatial color invariant features. The usefulness of the proposed measurement framework is illustrated by edge detection, where edges are discriminated as shadow, highlight, or object boundary. Other applications of the framework include color invariant image retrieval and color constant edge detection.