Research On Approach To Extracting Object Contour

Image segmentation is a central problem of computer vision. Contour extraction is one of the most important aspects of image segmentation, extensively applied in image analysis, and has a great significance in both theoretical research and industrial applications. This dissertation focuses on contour extraction of objects and concentrates on the following aspects: (1) the approach of contour extraction based on partial differential equations (PDE); (2) contour extraction based on particle motion in quantum mechanics; (3) automatic extraction of affine invariant contour; (4) battle damage assessment (BDA) of naval vessel based on image understanding. Approaches of contour extraction based on PDE, e.g. active contour models, also known as snakes, are being extensively used to solve the problem of image segmentation. In this thesis, the contour extraction approaches based on PDE are studied systematically in theory. First, we compared the typical contour extraction approaches based on PDE such as active contour models, global minimum for active contour models, topologically adaptable sankes, and level set methods, etc., and their merits and demerits were pointed out. Secondly, the approach of closed contour extraction based on global minimum optimization and detection of saddle points is improved. An approach of closed contour extraction based on surface of minimal action and dichotomy of image was presented. It divides the original image into two small images in the detection of closed contour so that a complicated detection of saddle points was avoided. Thirdly, computational complexity of our approach is theoretically analyzed, and it was proved that our improved approach has a runtime less than original approach. Many improvements in active contour models (also known as Snakes) have been made since they were introduced first by Kass et al. (1987). Active contour models are an energy-minimizing spline guided by external constraint forces and influenced by image forces, whose physical background is the principle of minimum action or the force equilibrium in classical mechanics. However, the contour of object in a noisy image is often blurred but this image statistical property is not considered in the classical mechanics model. The author explained that it is a logical extention that the law of particle motion in quantum mechanics is applied to contour extraction of objects. From this point of view, a new model, quantum contour model, is proposed. The probability for particle moving from a point to another point is estimated. The relationship between active contour model and quantum contour model is addressed. The concept of multiple particle quantum contour models is given. Furthmore, the extraction of boundary with branches, the convergence of contour extraction and the smoothing of the extracted contour were addressed, which are three problems faced when we apply quantum contour model to contour extraction of objects. Edge detection localization and computational complexity are analyzed. Experiments with simulated and real images demonstrated that the quantum contour based approach could extract a close boundary quickly, accurately and robustly with a single initial point close to the boundary of the object of interest. An important problem in object recognition is the fact that an object can be seen from different viewpoints, resulting in different images. For near planar object, these deformations can be modeled approximately by an affine transformation. In the thesis, automatic extraction of affine invariant contour of objects was dealed with. First, six parameters of affine transformation are normalized according to the practical problem in applications. Then, the concept of shape-specific line is given. The properties of shape-specific points and shape-specific lines are discussed. Using shape-specific lines, the parameters of scale, rotation and translation transforms are computed. The experiments showed that the contour registration method by shape-specific lines is better than the method by shape-specific points. Finally, the values of the parameters of affine transformations are computed using a Genetic Algorithm while the energy of contour is referred to as a fitness function. Automatic extraction of affine invariant contour is performed. The applications of contour extraction of objects are considered, such as image understanding based BDA of naval vessels. Two modes of BDA are presented. The building and automatic intelligent maintenance of BDA knowledge database on naval vessels and analysis of the attacked target properties were done.The contour extraction of naval vessel and recognition of attacked target in BDA were investigated. Tne mathematics model of BDA was established. The relationship between BDA of part and whole was discussed. By using statist ical decision-making method, the database maintenance of BDA knowledge database beccomes a simple quadratic programming problem.