Research On Color Constancy Computation

As one of the most basic and important visual functions, perceptual constancy plays a key role for the human beings stably perceiving the real world. In order to make computer vision system have the similar visual perception function, perceptual constancy computation has become one of the most important research topic in the computer vision field. This dissertation focuses on the color constancy computation, and studies this topic from four aspects: unsupervised color constancy, supervised color constancy, color constancy algorithms fusion and color invariant.For the unsupervised color constancy computation, the recently proposed color constancy algorithm GSI (Grey Surface Identification) is a camera-dependent solution and suffers from camera calibration requirement. In order to avoid calibrating a camera, we propose an iterative algorithm, which is called color constancy using achromatic surface(CCAS). The achromatic surfaces identified by the CCAS algorithm can be used to estimate illumination of an image, which can significantly improve accuracy of illumination estimation.For the supervised color constancy computation, by introducing a novel machine learning algorithm Extreme Learning Machine (ELM), we propose a new color constancy algorithm named color constancy using ELM, which can overcome the single output, complex parameter tuning and low training speed of the SVR based color constancy algorithm. In addition, discarding the traditional high dimensional binarized chromaticity histogram feature, a new low dimensional and effective feature vector is extracted based on the Grey Edge framework, which is used as the input for the ELM based algorithm.For the color constancy algorithms fusion, based on the image texture description with Weibull parameterization, we propose a new color constancy fusion scheme called color constancy based on texture similarity for natural images. By using the Grey Edge framework as the algorithm generator, the proposed fusing algorithm can select the best color constancy method (or best combination of the methods) for the certain image, according to its both global texture feature and local texuture feature.For the color invariant description, two new color spaces are defined based on the robust Diagonal-offset reflectance model. In the two color spaces, a color invariant descriptor computation framework is proposed, by introducing moment invariants theory. According to different color space selection, the descriptor computation framework can generate original image color invariant descriptor and edge image color invariant descriptor. The combination thereof can serve as combinational color invariant descriptor. All these descriptors are not only robust to illumination change, but also robust to affine transformation and image blurring.