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Capturing Data Through Image Segmentation 2010

The approach, Multiresolution Adaptive and Progressive Gradient-based color image SEGmentation (MAPGSEG), integrates color, texture, and gradient information in a multiresolution framework, and is based on the principle that the segmentation results of images at low resolution can be used to efficiently segment their corresponding high-resolution counterparts. The algorithm, which is implemented entirely in a MATLAB—an interactive numerical computing environment—is comprised of six modules.

The first module performs dyadic wavelet decomposition to obtain high- quality approximations of the input RGB (red, green, blue color space) image at different resolution levels. The number of decomposition levels is automatically determined based on the smallest workable image dimension specified by a user or constrained by an application.

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The second module converts the input image from RGB to the CIE L*a*b*, a device independent color space developed in 1976 by the Commission Internationale de l’Eclairage to achieve approximate perceptual consistency of colors. Consequently, the CIE L*a*b* color space facilitates improved color differentiation and separation of luminance-chrominance information, which enables the efficient handling of images with illumination variations. The color conversion scheme is followed by the computation of gradient information used to automatically and adaptively generate the thresholds required to process regions at different resolutions.

The third module performs a computationally efficient progressive and dynamic multiresolution region-growth procedure to create an initial estimate of regions in the image. In the fourth module, the MAPGSEG algorithm executes a texture characterization process, which differentiates textures at various resolutions. This module is an important aspect of the algorithm, as texture frequently manifests itself as multiple color shades or intensity variations due to material properties like density, gradient, and coarseness, impairing the process of image segmentation.

Next, the initially identified regions are integrated with the acquired texture model using a statistical region-merging procedure to obtain an interim segmentation. In the final phase, the acquired interim result is up-scaled and regions of high confidence are determined. These regions are passed on as a priori information to the next stage of the algorithm to process the remaining unsegmented areas with minimal computational costs. The algorithm progresses from one resolution level to another of the image pyramid until a segmentation result is achieved at the highest/original resolution and is semantically consistent with the input image.

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