July 30, 2010: Workshop program posted
Color is one of the most important and beautiful aspects of the world surrounding us. From a historical perspective, covering more than 400 years, many prominent researchers contributed to our present understanding of light and color. Over the last decades, with the advances of image sensors, printers, image displays and digital cameras, an explosive growth in the diversity of needs in the field of color computer vision has been witnessed. More and more, the traditional gray value pictorial analysis is replaced by color through nontrivial extensions of gray value for the modeling and processing of color images. Moreover, today, with the growth and popularity of the World Wide Web, a tremendous amount of visual information, such as images and videos, has become freely available. Color information is a powerful tool for image and video understanding.
This workshop will provide new insights for the understanding of color in imaging and computer vision. As color is shared among various research fields, this workshop places color at the junctions of different areas such as color science, vision, computer, and machine learning using area specific expertise and cross-understanding to provide deeper and important new insights. We encourage the researchers to formulate innovative color theories, computational methods, design suitable representations as well as data structures for them, and evaluate effectiveness. Invariant and color constant feature sets are demanded as computational methods in computer vision. Further, the focus is on deriving semantically rich color indices for visual content analysis and access. Novel theoretical models are invited to express semantics from both a physical as well as a perceptual point of view. We are soliciting original contributions that address a wide range of theoretical and application issues including, but not limited to:
Theory Color science, colorimetry, color spaces, color difference, complex reflection models, shading modeling, color appearance models.
Physics-based image formation models Image dehazing, underwater illumination modeling, deflaring, complex reflection models.
Object, Scene and Video Recognition Color invariance, color saliency, color constancy, color features (salient points), color descriptors, matching, machine learning, color image processing of video and still images, color in motion and tracking.
Image/Video Processing Pre-processing, filtering, enhancement, specularity and shadow removal, feature detection, color texture, image segmentation, feature grouping, image sequence processing, color compression, spectral color processing, colorization.
Vision Color perception, color psychophysics, color constancy, color discrimination, psychophysical studies and human studies of colour perception, color memory, color cognition, spatial and temporal color vision.
Multispectral Methods Spectral appearance models, spectral imaging systems, spectral sensor design, active illumination methods, spectral image analysis.
Applications Industrial inspection, color in food, color in human computer interaction, medical, and biological applications.