Image Specific Color Representation: Line Segments in the RGB Histogram

Color representation is a problem of significant importance in the fields of computer vision and image processing. Traditionally, many computer vision and image processing tasks where developed for gray level images. In recent years, as both the means of obtaining digital color images and the computational power needed to process such images have become more available, these algorithms have been adapted for color images. Unlike gray level images, where the way of representing a pixel’s gray level is very intuitive, a good representation for a pixel’s color is a problem yet to be solved. Throughout the years, many color spaces have been suggested as most appropriate for computer vision tasks, yet none of them have proven to be as such. The major drawback of all of these methods is that they all hold an implicit assumption that color is preserved up to a linear transformation due the process of image capturing. In practice, this assumption is not valid, and different color sensors distort the color information in various non linear ways. Most images used for computer vision and image processing tasks are captured using digital cameras. In this work we investigated the color distortion created by digital camera sensors and attempt to compensate for it by using our color representation. Our research revealed that one general model for this distortion does not exist and that for each combination of camera, scene and illumination conditions a different distortion takes place. Nevertheless we did manage to model the nature of the color distortion, and given an image taken by digital camera, we created a method of recovering the color model best describing the image. First we describe the nature of the color distortion done by the CCD sensors and construct a method for recovering a color model given an image. Than the advantages of this image specific color model over traditional general color models is discussed in the context of image segmentation, image compression and other computer vision and image processing applications. Finally we have a summary of the work done and its contribution to computer vision, followed by a suggestion of ideas for further work in this area.