Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
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This pater describes a new method for real-time and robust object tracking using a Gaussian-cylindroid color model and an adaptive mean shift. Color information has been widely used for characterizing an object from others. However, sensitiveness to illumination changes limits their flexibility and applicability under various illuminating conditions. We present a robust color model against irregular illumination changes where chrominance is fitted with respect to intensity using B-spline. A target for tracking is expressed by the joint probabilistic density function of the proposed color model and 2-D positional information in image lattice. And tracking is performed using the mean-shift algorithm incorporating the joint probabilistic density function where the bandwidth selection is essential to tracking performance. We present a simple and effective method to find the optimal bandwidth that maximizes the lower bound of the log-likelihood of the target represented by the joint probabilistic density function. The robustness and capability of the presented method are demonstrated for several image sequences.