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IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Modeling Light Reflection for Computer Color Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Identification Using the Dichromatic Reflection Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Colour image segmentation and labeling through multiedit-condensing
Pattern Recognition Letters
A physical approach to color image understanding
A physical approach to color image understanding
Color region tracking for vehicle guidance
Active vision
Region-based strategies for active contour models
International Journal of Computer Vision
Generalization of the Lambertian model and implications for machine vision
International Journal of Computer Vision
Statistical snakes: active region models
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Illumination invariant colour recognition
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Reflectance based object recognition
International Journal of Computer Vision
International Journal of Computer Vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Constrained active region models for fast tracking in color image sequences
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Modelling of single mode distributions of colour data using directional statistics
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Adaptive B-Splines and Boundary Estimation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Region saliency as a measure for colour segmentation stability
Image and Vision Computing
Hands-free vision-based interface for computer accessibility
Journal of Network and Computer Applications
Scene context modeling for foreground detection from a scene in remote monitoring
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Towards hands-free interfaces based on real-time robust facial gesture recognition
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
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In this paper we investigate how best to model naturally arising distributions of colour camera data. It has become standard to model single mode distributions of colour data by ignoring the intensity component and constructing a Gaussian model of the chromaticity. This approach is appealing, because the intensity of data can change arbitrarily due to shadowing and shading, whereas the chromaticity is more robust to these effects. However, it is unclear how best to construct such a model, since there are many domains in which the chromaticity can be represented. Furthermore, the applicability of this kind of model is questionable in all but the most basic lighting environments.We begin with a review of the reflection processes that give rise to distributions of colour data. Several candidate models are then presented; some are from the existing literature and some are novel. Properties of the different models are compared analytically and the models are empirically compared within a region tracking application over two separate sets of data. Results show that chromaticity based models perform well in constrained environments where the physical model upon which they are based applies. It is further found that models based on spherical representations of the chromaticity data provide better performance than those based on more common planar representations, such as the chromaticity plane or the normalised colour space. In less constrained environments, however, such as daylight, chromaticity based models do not perform well, because of the effects of additional illumination components, which violate the physical model upon which they are based.