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This dissertation focuses on energy minimizing techniques customized to two industrial applications and conceptualized in order to develop a hybrid interactive segmentation tool box. The first industrial application focuses on developing a machine vision system for simultaneous and objective evaluation of an important functional attribute of a fabric; namely, soil/stain release. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. A customized adaptive statistical snake, which evolves based on region statistics, is employed in order to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. A sizeable data set is employed to test the efficacy of the proposed approach. The second application comprises of a machine vision system for automatic identification of the class of firearms by extracting and analyzing two significant properties from spent cartridge cases, namely the Firing Pin Impression (FPI) and the Firing Pin Aperture Outline (FPAO). Within the framework of the proposed machine vision system, a white light interferometer is employed to image the head of the spent cartridge cases. As a first step of the algorithmic procedure, the Primer Surface Area (PSA) is detected using a circular Hough transform. Once the PSA is detected, a customized statistical region-based parametric active contour model is initialized around the center of the PSA and evolved to segment the FPI. Subsequently, the scaled version of the segmented FPI is used to initialize a customized Mumford-Shah based level set model in order to segment the FPAO. Once the shapes of FPI and FPAO are extracted, a shape-based level set method is used in order to compare these extracted shapes to an annotated dataset of FPIs and FPAOs from varied firearm types. A total of 74 cartridge case images non-uniformly distributed over five different firearms are processed using the aforementioned scheme and the promising nature of the results (95% classification accuracy) demonstrate the efficacy of the proposed approach. For the interactive segmentation, two intrinsic properties namely shape and symmetry is integrated into the intelligent scissors framework. Currently, all the interactive segmentation methods are driven by the lower level image features, whereas the higher level knowledge of the application is imparted by the user. The burden on the user increases significantly during instances of occlusions, broken edges, noise and spurious boundaries. As a first step towards incorporating shape feature, an offline training procedure is performed in which a mean shape and the corresponding shape variance is computed by registering training shapes up to a rigid transform in a level-set framework. The user starts the interactive segmentation procedure by providing a training segment, which is a part of the target boundary. A partial shape matching scheme based on a scale-invariant curvature signature is employed in order to extract shape correspondences and subsequently predict the shape of the unsegmented target boundary. A ‘zone of confidence’ is generated for the predicted boundary to accommodate shape variations. The method is evaluated on segmentation of digital chest x-ray images for lung annotation which is a crucial step in developing algorithms for screening for Tuberculosis. Symmetry feature is incorporated into the intelligent scissors framework in order to predict one symmetric half of a bilaterally symmetric object distorted by a projective transform, from the other symmetric half. Accordingly, the proposed work utilizes the fundamental relationship between a distorted symmetrical object and its symmetrical counterpart in order to establish a mathematical relationship between the two symmetric halves. The user starts the segmentation procedure by providing training segments from the symmetric halves of the target object. Based on the provided training segments, a relationship is established between the two symmetric halves by incorporating a collinearity based parameterization method. Subsequently, based on this established relationship and the user generated segment, the other symmetric half is predicted. This predicted segment is used to generate a ‘zone of confidence’ which mitigates the influence of spurious edges during the segmentation procedure, thus reducing the burden on the user. In addition, the predicted segment is also used to fill up the occluded parts of the target object, thus mitigating the burden and the subjectivity involved. Synthetic examples are employed to prove the efficacy of the proposed work.

This thesis explains, in detail, the various kinds of active contour models that have attracted the attention of many in the computer vision community in the recent years. It gives a detailed description of the energy formulations and the derivation of force equations using a calculus of variations method. These snake models are combined and customized for two applications: (1) detection of double edges in x-ray images of lumbar vertebrae using pressurized open DGVF snakes, and (2) fabric stain detection using statistical balloons. The detection of double edges in x-ray images of lumbar vertebrae is of prime importance in the assessment of injury or vertebral collapse, possibly due to osteoporosis or other spine pathology. Manual segmentation is prone to errors due to subjective judgment and, hence, computer vision methods, such as snakes, are an attractive alternative to providing an automatic means of segmenting the double edges. The proposed algorithm uses a pressurized open model of DGVF snakes, customized to this application. This algorithm is applied to a set of over 30 lumbar images thus far, and the double-edge detection results have been deemed promising enough to set up a quantitative measurement for the assessment of injury or vertebral collapse. The goal in the second application is the automatic quantification of stain release in fabrics, which is an important property, impacting the fabrics’ pricing in the marketplace. Of course, to quantify stain release, one must first detect and segment the stains. This thesis proposes a balloon model with embedded statistical information in order to detect and segment the stains. A set of 15 stain images are used thus far to test the algorithm with near perfect detection and segmentation results.