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A standard approach to extending gray-scale filters to color is the use of gray-scale filters on each of the red, green, and blue bands separately. This RGB component method, when applied to connected filters, causes the introduction of new colors into the image, which could lead to incorrect classification or analysis. We develop a method of applying connected filters to color images using rotation in the hue band of the hue-saturation-value (HSV) color space. The rotation shifts the discontinuity in hue such that the distance to the nearest k-means cluster mean is maximized and the histogram value is simultaneously minimized. When applied to the sample image set, the error from the RGB component approach is of the order of 10 times greater than that of our hue rotation approach. Additionally, our approach yields qualitatively improved images, with fewer new colors introduced than the RGB component method. © 2004 SPIE and IS&T.

We propose a method that uses projection models in conjunction with a sequential Monte Carlo approach to track rigid targets. We specifically address the problems associated with tracking objects in scenarios characterized by cluttered images and high variability in target scale. The projection model snake is introduced in order to track a target boundary over a variety of scales by geometrically transforming the boundary to account for three-dimensional relative motion between the target and camera. The complete solution is a potent synergism of the projection model snake and a sequential Monte Carlo method. The projection model Monte Carlo method randomly generates the parameters of target motion and pose from empirically derived distributions. The resultant “particles” are then weighted according to a likelihood determined by the integration of the mean gradient magnitude around the target contour, yielding the expected target path and pose. We demonstrate the effectiveness of this approach for tracking dynamic targets in sequences with noise, clutter, occlusion, and scale variability. © 2004 SPIE and IS&T.

We propose a skin-color-based face location algorithm that can locate the human face in a complex background including nonface skin color regions, such as the hands. The algorithm is divided into three steps. The first step involves the use of color components to segment the skin colors from the complex background. The second step detects a curve called the division curve that delineates the boundary between the hair region and the face region. In the last step, the division curve is used to estimate the face region. The proposed algorithm has wide application to problems where the detection of both hands and face is requisite and to problems in which the distribution of skin/hair color can be known a priori. © 2003 SPIE and IS&T.