| Digital images and videos are widely used in many areas, such as digital TV broadcasting, space imagery and aerial photography, magnetic resonance imaging, traffic monitoring and video surveillance. In this dissertation, we study two important areas, namely, image compression and video analysis.;In the first part of this dissertation, we study compressive sensing (CS) and its application to image/video representation and compression. CS theory states that it is possible to recover certain signals and images from far fewer samples or measurements than those required by traditional approaches. We use a CS technique to represent visual data and propose a new image representation scheme in visual sensor networks. Different from the previous works on compressive imaging, which treat the input image as a whole signal, we decompose the visual data into two components before sampling: a dense component and a sparse component. We represent the dense component by the traditional approach and represent the sparse component by compressive sensing. The advantage of our scheme is that we use the correlation of the two components to recover the signal, which helps to reduce the number of measurements and computation time required for reconstruction with the same accuracy. We propose and implement a projection onto convex sets based optimization algorithm to recover the signal. We also propose a new image/video compression system, which combines CS with traditional block based image/video compression schemes, such as JPEG and H.264.;In the second part of this dissertation, we study video analysis. There are a lot of image processing areas that employ video analysis. In this dissertation, we attack three problems in video analysis, i.e., image registration, motion analysis, and object tracking. Firstly, we propose a new strategy of image registration by leveraging the depth information via 3D reconstruction. One novel idea is to recover the depth in the image region with high-rise objects to build accurate transform function. The traditional image registration algorithms suffer from the parallax problem due to their underlying assumption that the scene can be regarded approximately planar. Our method overcomes this weakness and achieves more accurate registration performance. Secondly, we propose a new method for motion segmentation based scene interpretation. The segmentation of optical motion field is based on the minimal coding length criterion. The experimental results show that our proposed scheme could greatly improve the performance of motion field segmentation. Finally, to overcome the limitations of the traditional KLT feature tracker, we propose a novel object tracking algorithm. For each object to be tracked, we use a set of KLT features to represent and a weighting function to balance the contribution of different features, according to their position, quality and consistency. The algorithm could adequately track multiple objects of arbitrary shapes in an image sequence with partial occlusion. |
