Given a set of images from the same viewpoint, in which occlusions are present, background estimation is to output an image with stationary objects in the scene only. Background estimation is an important step in many computer vision problems such as object detection and recognition. With the growing interest in more sophisticated video surveillance systems, the requirement for the accuracy of background estimation increases as well.
In this thesis, we present two novel methods whose fundamental objectives are the same, namely, to estimate the background of a set of related images. In order to
make our methods more general, we assume that the input images can be taken either from the same viewpoint or from different viewpoints. Both methods combine information from multiple input images by selecting the appropriate pixels to construct the background. Our first method is a scanline energy optimization method, and our second method is based on graph cuts optimization. We apply these two methods to datasets with different feature and the results are encouraging. Furthermore, we use the CUDA (Compute Unified Device Architecture) programming language to make full use of the GPU processing power. GPU stands for Graphics
Processing Unit, which employs parallel processing and is more powerful than the CPU. In particular, we implement an efficient graph-based image segmentation algorithm as well as a linear blending method using the CUDA programming language for acceleration, both of which are used in our first method. The speedup of our GPU implementation can be 20 times faster.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/625 |
| Date | 11 1900 |
| Creators | Chen, Xida |
| Contributors | Yee-Hong Yang, Computing Science, Mario A. Nascimento, Computing Science, Arie Croitoru, Earth & Atmospheric Sciences |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 11855891 bytes, application/pdf |
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