Browsing by Author "Upadhyay, Sanat Kumar 1988-"
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Item Extraction and Normalization of Directional Characteristics of Images and Textures using Multiscale Transforms(2014-12) Upadhyay, Sanat Kumar 1988-; Papadakis, Emanuel I.; Azencott, Robert; Gladish, Gregory W.; Kakadiaris, Ioannis A.; Labate, DemetrioThis dissertation consists of two projects, one of which is on illumination normalization in monochromatic images that form Chapter 1 of this dissertation. The second project is on Texture analysis and application in cancer detection, which is given in Chapter 2. Illumination normalization is an important problem in the field of computer vision and pattern recognition. Often we require to build a system that could match an image of some object against another image of the same object, but acquired under different lighting conditions. In such applications, it becomes necessary to obtain light neutral surrogate of original images, for better comparison. The problem is known to be ill-posed, and therefore existing methods have to make a compromise between speed and output quality. We present a new wavelet based technique for normalizing illuminations in monochromatic images. We give a mathematical definition for Illumination normalization operator and show that this operator preserves structures of a scene while neutralizing changes in illumination. Practical implementation inherits high speed due to fast DWT algorithms. We demonstrate theoretically and experimentally that edges are preserved by preserving singularities at each point. The mathematical signature of structures is given by an ensemble of descriptors, in the form of various singularity exponents. In the second project, we implement a method for the 3D-rigid motion invariant texture discrimination and binary classification for discrete 3D-textures by modeling them as stationary Gaussian markov random fields. This method was first proposed by S. Jain, et al. The 'distance' between 3D-textures that remains invariant under all 3D-rigid motions of the texture to develop rules for 3D-rigid motion invariant texture discrimination and binary classification of textures. We experimentally establish that when they are combined with mean attenuation intensity differences the new augmented features are capable of discriminating between normal and abnormal liver tissue in arterial phase contrast enhanced X-ray CT–scans with high sensitivity and specificity. To extract these features CT-scans are processed in their native dimensionality. We experimentally observe that the 3D-rotational invariance of the proposed features improves the clustering of the feature vectors extracted from normal liver tissue samples.