{'fraction_non_alphanumeric': 0.029440628066732092, 'fraction_numerical': 0.0, 'mean_word_length': 4.573770491803279, '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}}
Real-time detection and pose estimation of low-textured and texture-less objects

Real-Time detection and pose estimation are two key components in various areas of 
computer vision, e.g. in industrial inspection, augmented reality and robotics. The task is to find the object of interest in an image and to recover its pose without having a priori knowledge of it. For many applications this has to work robustly and in real-time in order to be fully operational. While real-time detection of well textured objects has already reached a high level of maturity, its application on low-textured or texture-less objects is still an open issue. Unfortunately, those kinds of objects play an important role in man-made environments which makes it necessary to deal with them in an efficient manner. In this thesis, we therefore present four novel methods for the efficient and reliable detection of low-textured and texture-less objects which are superior to current state-of-the-art approaches w.r.t. robustness and speed.