顯著區域偵測及影像套合之先進影像分析技術及應用

Abstract

In my dissertation, there are two main applications of computer vision. The first one is salient region detection improved by PCA and boundary information, and the second one is banknote reconstruction from fragments by image registration and convex quadratic programming. Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. In this dissertation, we propose a novel method to determine the salient regions in images. The L0 smoothing filter and a Principal Component Analysis (PCA) play important roles in our framework. The L0 filter is greatly helpful in characterizing fundamental image constituents, i.e., salient edges, and for simultaneously diminishing insignificant details. Therefore, we can derive more accurate boundary information for background merging and boundary scoring. A PCA can reduce the computational complexity, as well as attenuate noises and translation errors. A local-global contrast is then used to calculate the distinctiveness. Finally, we take advantage of image segmentation to achieve full-resolution saliency maps. Our proposed method is compared with other state-of-the-art saliency detection methods, and is shown to yield higher precision-recall rates and F-measures. Due to a variety of accidents, banknotes may be broken into several fragments. These fragments are usually stained, burned, partially lost, and twisted, which makes banknote reconstruction a hard problem. Since the fragments are always not intact, the traditional edge and texture based fragment assembling methods cannot be applied here. In this dissertation, we develop a framework for banknote reconstruction using registration and optimization. We applied the image registration using the SIFT and RANSAC. Moreover, convex quadratic optimization based on maximizing the reconstructed area and avoiding overlapping is adopted. Simulations are given to demonstrate the effectiveness of our framework.