Conventional stereo matching methods provide the unsatisfactory results for stereo pairs under uncontrolled environments such as illumination distortions and camera device changes. A majority of efforts to address this problem has devoted to develop robust cost function. However, the stereo matching results by cost function cannot be liberated from a false correspondence when radiometric distortions exist. This paper presents a robust stereo matching approach based on probabilistic Laplacian propagation. In the proposed method, reliable ground control points are selected using weighted mutual information and reliability check. The ground control points are then propagated with probabilistic Laplacian. Since only reliable matching is propagated with the reliability of GCP, the proposed approach is robust to a false initial matching. Experimental results demonstrate the effectiveness of the proposed method in stereo matching for image pairs taken under illumination and exposure distortions.
Bag-of-words (BoW) is one of the most successful methods for object categorization. This paper proposes a statistical
codeword selection algorithm where the best subset is selected from the initial codewords based on the statistical
characteristics of codewords. For this purpose, we defined two types of codeword-confidences: cross- and within-category
confidences. The cross- and within-category confidences eliminate indistinctive codewords across categories and
inconsistent codewords within each category, respectively. An informative subset of codewords is then selected based on
these two codeword-confidences. The experimental evaluation for a scene categorization dataset and a Caltech-101 dataset
shows that the proposed method improves the categorization performance up to 10% in terms of error rate reduction when
cooperated with BoW, sparse coding (SC), and locality-constrained liner coding (LLC). Furthermore, the codeword size
is reduced by 50% leading a low computational complexity.