Parallelization for image processing algorithms based chain and mid-crack codes

Freeman chain code is a widely-used description for a contour image. Another mid-crack code algorithm was proposed as a more precise method for image representation. We have developed a coding algorithm which is suitable to generate either chain code description or mid-crack code description by switching between two different tables. Since there is a strong urge to use parallel processing in image related problems, a parallel coding algorithm is implemented. This algorithm is developed on a pyramid architecture and a N cube architecture. Using link-list data structure and neighbor identification, the algorithm gains efficiency because no sorting or neighborhood pairing is needed. In this dissertation, the local symmetry deficiency (LSD) computation to calculate the local k-symmetry is embedded in the coding algorithm. Therefore, we can finish the code extraction and the LSD computation in one pass. The embedding process is not limited to the k-symmetry algorithm and has the capability of parallelism. An adaptive quadtree to chain code conversion algorithm is also presented. This algorithm is designed for constructing the chain codes of the resulting quadtree from the boolean operation of two quadtrees by using the chain codes of the original one. The algorithm has the parallelism and is ready to be implemented on a pyramid architecture. Our parallel processing approach can be viewed as a parallelization paradigm - a template to embed image processing algorithms in the chain coding process and to implement them in a parallel approach

Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture