Energy-based deformable contours in computer vision: Recent advances and customization for two applications
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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.