Kernel learning for visual perception
Chen Wang · 2019
The visual perceptual system in animals allows them to assimilate information from their surroundings. In artificial intelligence, the objective of visual perception is to enable the capability of a computer system to interpret the surrounding environment using data acquired from cameras and other aided sensors. Since the last century, researchers in visual perception have delivered many marvelous technologies and algorithms for various applications, such as object detection and image recognition, etc. Despite the technological progresses, human beings are still confronted by the unsatisfactory performance of artificial visual perceptual systems. One of the main reasons is that the traditional methods usually rely on large amount of training data, powerful processors, and require great efforts and time for process modeling. The research goal of this thesis is to develop visual perceptual systems that requires less computational resources but with higher performance. To this end, the novel kernel learning methods for several basic visual perceptual tasks, including object tracking, localization, mapping, and image recognition, are proposed and demonstrated both theoretically and practically.