Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Colour Model Selection and Adaption in Dynamic Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Color constancy under varying illumination
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
Adaptive Subclass Discriminant Analysis Color Space Learning for Visual Tracking
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
CCIW'11 Proceedings of the Third international conference on Computational color imaging
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Hi-index | 0.01 |
In this paper, we propose a new technique to perform figure-ground segmentation in image sequences of moving objects under varying illumination conditions. Unlike most of the algorithms that adapt color, there is not the assumption of smooth change of the viewing conditions. To cope with this, we propose the use of a new colorspace that maximizes the foreground/background class separability based on the 'Linear Discriminant Analysis' method. Moreover, we introduce a technique that formulates multiple hypotheses about the next state of the color distribution (some of these hypotheses take into account small and gradual changes in the color model and others consider more abrupt and unexpected variations) and the hypothesis that generates the best object segmentation is used to remove noisy edges from the image. This simplifies considerably the final step of fitting a deformable contour to the object boundary, thus allowing a standard snake formulation to successfully track non-rigid contours. In the same manner, the contour estimate is used to correct the color model. The integration of color and shape is done in a stage called 'sample concentration', introduced as a final step to the well-known CONDENSATION algorithm