Browsing by Subject "Imaging systems -- Image quality"
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Item A CCD image sensor frame grabber and conditioner(Texas Tech University, 1987-12) Mau, Kim-YouNot availableItem A study of noise effects in phase reconstruction from phase differences(Texas Tech University, 1996-12) Fox, James LNot availableItem A vision system for a small image CCD array(Texas Tech University, 1988-08) Young, MingNot availableItem Item Image recovery and segmentation using competitive learning in a computational network(Texas Tech University, 1992-12) Phoha, Vir ViranderIn this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation, within the framework of Markov Random Fields (MRF). the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonens update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results are compared with recently published results using the mean field approximation. Segmentation results using the proposed approach are compared with edge detection using fractal dimension, edge detection using mean field approximation, and edge detection using the Sobel operator. Edge points obtained by using these techniques are combined to produce edge maps which include both hard and soft edges.Item Image recovery and segmentation using competitive learning in a neighborhood system(Texas Tech University, 2002-12) Li, ChengchengImage restoration and segmentation are important image processing techniques. In recent years, many researchers in the image restoration field have based their research methods on calculus of variation and mathematical statistics and do not directly incorporate the observed principles of a low-level animal vision. In a previous work, based on the principle of low-level mammalian visual system that deals with image restoration and segmentation problems from a more direct and easily understandable and acceptable aspect, a new algorithm incorporating competitive leaming method was developed. This algorithm yields improved performance over previous studies in synthetic image restoration. This paper furthers the development and application of this algorithm. This paper has purpose that is threefold. First, this paper presents results for reconstruction and estimation of uncorrupted images from a distorted or a noisy image by using competitive leaming method. This paper evaluates the CLRS (Competitive Leaming in image Restoration and Segmentation) method by experimenting with this algorithm on a variety of images and a wide range of parameters, both based on practices and theories. The meaning and value range of some parameters are discussed in detail. Second, we enlarged the size of the neighborhood used in CLRS to see the influence of neighborhood range. Third, we reviewed the current methods both in image restoration and edge detection, then we compared the restoration and segmentation results obtained from CLRS and all the other methods. The results showed that CLRS algorithm performances were consistently better or equal in edged preservation and comparable performance in enhancing within the boundaries. These results are based on simulation experiments on a set of synthetic and real images corrupted by Gaussian noise. We concluded that an interactive algorithm for image reconstruction and segmentation, CLRS, has been developed. This algorithm is based on the principle of competitive leaming.