Browsing by Author "Ndili, Unoma"

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    Coding Theoretic Approach to Image Segmentation
    (2001-05-20) Ndili, Unoma; Digital Signal Processing (http://dsp.rice.edu/)
    Using a coding theoretic approach, we achieve unsupervised image segmentation by implementing Rissanen's concept of Minimum Description Length for estimating piecewise homogeneous regions in images. MDL offers a mathematical foundation for balancing brevity of descriptions against their fidelity to the data. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant. Our model is aimed at identifying regions of constant intensity (mean) and texture(variance). Based on a multi-scale encoding approach, we develop two different segmentation schemes. One algorithm is based on an adaptive (greedy) rectangular partitioning, while the second algorithm is an optimally-pruned wedgelet-decorated dyadic partitioning scheme. We compare the two algorithms with the more common signal plus constant noise schemes, which accounts for variations in mean only. We explore applications of our algorithms on Synthetic Aperture Radar (SAR) imagery. Based on our segmentation scheme, we implement a robust Constant False Alarm Rate (CFAR) detector towards Automatic Target Recognition (ATR) on Laser Radar (LADAR) and Infra-Red (IR) images.
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    Coding Theoretic Approach to Image Segmentation
    (2001-10-20) Ndili, Unoma; Nowak, Robert David; Figueiredo, Mario; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper, using a coding theoretic approach, we implement Rissanen's concept of minimum description length (MDL) for segmenting an image into piecewise homogeneous regions. Our image model is a Gaussian random field whose mean and variance functions are piecewise constant across the image. The image pixels are (conditionally) independent and Gaussian, given the mean and variance functions. The model is intended to capture variations in both intensity (mean value) and texture (variance). We adopt a multi-scale tree based approach to develop two segmentation algorithms, using MDL to penalize overly complex segmentations. One algorithm is based on an adaptive (greedy) rectangular partitioning scheme. The second algorithm is an optimally-pruned wedgelet decorated dyadic partitioning. We compare the two schemes with an alternative constant variance dyadic CART (classification and regression tree) scheme which accounts only for variations in mean, and demonstrate their performance with SAR image segmentation problems.