A Study on Deformed Pattern Identification and Image Registration

• Main Authors: Yi-Sheng Liu, 劉宜生
• Other Authors: Ru-Sheng Liu
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/38736920595944182735

博士 === 元智大學 === 資訊工程學系 === 96 === Pattern identification and image registration are essential tasks in the applications of using image processing. Both of their purposes are to find a match or mismatch between two or more images through the detection and matching stages. Since the images may be taken from different sensors, at different time or under different conditions, from different viewpoints, or for different objects, the methodologies used in these tasks should be heavily adapted to the objectives of applications and the characteristics of images. Moreover, the deformation problems will be encountered in the design of these tasks and invoke the difficulties to achieve robustness. Design of robust pattern identification for road signs and image registration for two-dimensional electrophoresis (2-DE) gel analysis is the goal of this study. In the first part, a fast and robust pattern identification method for scaled and skewed road signs is proposed. In the detection stage, the input color image is first quantized in HSV color model. Border tracing those regions with the same colors as road signs is adopted to find the regions of interest (ROI). ROIs are then automatically adjusted to fit road sign shape models to facilitate detection verification even for scaled and skewed road signs in complicated scenes. Since the ROI adjustment and verification are both performed only on border pixels, the proposed road sign detector is fast. In the recognition stage, the detected road sign is normalized first. Histogram matching based on layout context is then used to measure the similarity between the scene and model road signs to accomplish recognition. Since histogram matching is fast and has high tolerance to distortion and deformation, our method has high recognition accuracy and fast execution speed. In the second part, a robust image registration for 2-DE gel analysis is proposed. In the spot detection stage, the proposed method takes slices of a gel image in the gray level direction and builds them into a slice tree, which in turn is used to perform spot detection. More specifically, a series of slices of spots can be obtained in the intensity direction. Each slice of a spot has its own features such as size, shape, central point and boundary smoothness. If the central points of slices are projected onto the co-plane, the projected points belonging to the same spot will fall in a neighborhood. The distribution of these projected points vary according to the shape and appearance of the spots in a gel image. Since the information of slices, namely slice context, can be embedded into a slice tree based on which the proposed spot detection can resolve the over-segmentation problem. Over-segmentation is a well-known drawback of Watershed adopted by most commercial spot detection software. In the spot matching stage, an iterative approach based on layout context and relaxation labeling is proposed to cope with non-linear deformation of gel images. The proposed matching uses an estimation-refinement-transformation strategy. In the estimation phase, layout context for each spot is used to calculate matching cost between each pair of spots. The matching cost is then used to initialize a matching probability matrix for consequent relaxation labeling. In the refinement phase, relaxation labeling is used to iteratively update the matching probability matrix to achieve global consistency. In the transformation phase, control points are subtly selected to calculate the thin-plate spline (TPS) parameters between the gel images. The TPS parameters are then used to transform the spots in a gel image closer to the corresponding spots in the other. The estimation-refinement-transformation loop is executed iteratively until the matching correspondence between spots reach a stable state. Since subtle selection of landmark points for matching probability computation in the refinement phase and control points for transformation parameter determination in the transformation phase is involved in the proposed method, optimal matching results are obtained. Experimental results show that the detection rate and recognition accuracy of the proposed road sign identification can reach 94.2% and 91.7%, respectively. On an average, it takes only 4-50 and 10 ms for detection and recognition, respectively. Thus, our method is effective, yet efficient. On the other hand, experimental results show that spots can be accurately detected and the spot matching pairs can be accurately identified. Moreover, for the fully automatic mode, the proposed method has higher accuracy than the commercial software ImageMaster 2D both in spot detection and spot matching.