Development of color density concept with color difference formulas in respect to human vision system

The aims of this study are to develop the color density concept and to propose the color density based color difference formulas.The color density is defined using the metric coefficients that are based on the discrimination ellipses and the locations of the colors in the color space.The ellipse sets are the MacAdam ellipses in the CIE 1931 xy-chromaticity diagram and the chromaticity-discrimination ellipses in the CIELAB space.The latter set was originally used to develop the CIEDE2000 color difference formula.The color difference can be calculated from the color density for the two colors under consideration.As a result,the color density represents the perceived color difference more accurately,and it could be used to characterize a color by a quantity attribute matching better to the perceived color difference from this color.Resulting from this,the color density concept provides simply a correction term for the estimation of the color differences.In the experiments,the line element formula and the CIEDE2000 color difference formula performed better than the color density based difference measures.The reason behind this is in the current modeling of the color density concept.The discrimination ellipses are typically described with three-dimensional data consisting of two axes,the major and the minor,and the inclination angle.The proposed color density is only a one-dimensional corrector for color differences;thus,it cannot capture all the details of the ellipse information.Still,the color density gives clearly more correct estimations to perceived color differences than Euclidean distances using directly the coordinates of the color space.

Real-time Follow-up Head Tracking in Dynamic Complex Environments

In the modes of both object motion and camera motion,an enhanced Camshift algorithm,which is based on suppressing similar color features of background and on joint color probability density distribution image,is proposed to real-time track head in dynamic complex environment.The system consists of face detection module,head tracking module and camera control module.When tracking fails,a self-recovery mechanism is introduced.At first the Adaboost face detector based on Haar-like features is implemented to find frontal faces,the false positive is filtered according to the skin color criterion,and the true face is used to initialize the tracking module.In hue saturation value(HSV) colorspace,the hue-saturation(H-S) histogram of face skin and the saturation-value(S-V) histogram of hair are built to produce the joint color probability density distribution image,and this is intended to realize the head tracking with arbitrary pose.During tracking,region of interest(ROI) is introduced,and the color probability density distribution of a specified background area outside the ROI is learned,similar color features in the head are suppressed according to the learning result.The background suppression step is intended to resolve the problem that the tracker maybe fails when the head is distracted by backgrounds having similar colors with the head.A closed loop control model based on speed regulation is applied to drive an active camera to center the head.Once tracking drift or failure is detected,the system stops tracking and returns to the face detection module.Our experimental results show that the presented system is well suitable for tracking head with arbitrary pose in dynamic complex environments,also the active camera can track moving head smoothly and stably.The system is computationally efficient and can run in real-time completely.