{'fraction_non_alphanumeric': 0.015852047556142668, 'fraction_numerical': 0.0, 'mean_word_length': 5.162601626016261, '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}}
A Global Sparse Stereo Matching Method under Structure Tensor Constraint

In this paper, a global algorithm based on graph cuts theory is proposed to solve the sparse stereo matching problem. The sparse feature points are extracted by the Harris corner detector. The matching problem is transformed into a labeling problem in the sparse graph which can be solved by energy minimization. In this algorithm, the graph is constructed by sparse feature points instead of pixels, which can lead to simple graph structure. In addition, a structure tensor descriptor, which is invariant to varying illumination, is used as similarity measurement to obtain more accurate result. The experimental results show that this algorithm can obtain accurate matching result.