{'fraction_non_alphanumeric': 0.020196191575302943, 'fraction_numerical': 0.0, 'mean_word_length': 5.351648351648351, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Investigations on Multi-Sensor Image System and its Surveillance Applications

T HIS thesis addresses the issues of multi-sensor image systems and its surveillance applications. The advanced surveillance systems incorporate multiple imaging modalities for an improved and more reliable performance under various conditions. The so-called image fusion technique plays an important role to process multi-modal images. The use of image fusion techniques has been found in a wide range of applications. The fusion operation is to integrate features from multiple inputs into the fused result. The image fusion process consists of four basic steps, i.e. preprocessing, registration, fusion, and post-processing or evaluation. This thesis focuses on the last three topics. The first topic is the image registration or alignment, which is to associate corresponding pixels in multiple images to the same physical point in the scene. The registration of infrared and electro-optic video sequences is investigated in this study. The initial registration parameters are derived from the match of head top points across the consecutive video frames. Further refinement is implemented with the maximum mutual information approach. Instead of doing the foreground detection, the frame difference, from which the head top point is detected, is found with an image structural similarity measurement. The second topic is the implementation of pixel-level fusion. In this study, a modified fusion algorithm is proposed to achieve context enhancement through fusing infrared and visual images or video sequences. Current available solutions include adaptive enhancement and direct pixel-level fusion. However, the adaptive enhancement algorithm should