@proceedings {5508, title = {Binary Adaptive Semi-Global Matching Based on Image Edges}, volume = {9631}, year = {2015}, month = {April 9 -10}, publisher = {SPIE}, address = {Los Angeles, CA}, abstract = {

Image-based modeling and rendering is currently one of the most challenging topics in Computer Vision and Photogrammetry. The key issue here is building a set of dense correspondence points between two images, namely dense matching or stereo matching. Among all dense matching algorithms, Semi-Global Matching (SGM) is arguably one of the most promising algorithms for real-time stereo vision. Compared with other global matching algorithms, SGM aggregates matching cost from several (eight or sixteen) directions rather than only the epipolar line using dynamic programming approach. Thus, SGM eliminates the classical \“streaking problem\” and greatly improves the accuracy and efficiency. In this paper, we aim at further improvement of SGM about its accuracy without increasing the computational cost. We propose setting the penalty parameters adaptively according to image edges extracted by edge detectors. We have carried out experiments on the standard Middlebury stereo dataset and evaluated the performance of our modified method with the ground truth. The results have shown a noticeable accuracy improvement compared with the results using fixed penalty parameters while the runtime computational cost was not increased.

}, keywords = {3D reconstruction, Canny Edges, Computer Vision, Dense Matching, Semi-Global Matching}, doi = {10.1117/12.2196960}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2389114}, author = {Han Hu and Yuri Rzhanov and Philip J. Hatcher and R. Daniel Bergeron} } @mastersthesis {5720, title = {Euclidean Reconstruction of Natural Underwater Scenes Using Optic Imagery Sequence}, volume = {Ocean Engineering/Ocean Mapping}, year = {2015}, month = {05/2015}, pages = {74}, school = {University of New Hampshire}, address = {Durham, NH}, abstract = {

The development of maritime applications require monitoring, studying and preserving of detailed and close observation on the underwater seafloor and objects. Stereo vision offers advanced technologies to build 3D models from 2D still overlapping images in a relatively inexpensive way. However, while image stereo matching is a necessary step in 3D reconstruction procedure, even the most robust dense matching techniques are not guaranteed to work for underwater images due to the challenging aquatic environment. In this thesis, in addition to a detailed introduction and research on the key components of building 3D models from optic images, a robust modified quasi-dense matching algorithm based on correspondence propagation and adaptive least square matching for underwater images is proposed and applied to some typical underwater image datasets. The experiments demonstrate the robustness and good performance of the proposed matching approach.

}, keywords = {3D reconstruction, Affine transformation, Dense Matching, euclidean reconstruction, Match propagation, optic imagery, Underwater image}, author = {Han Hu} } @article {5258, title = {Dense reconstruction of underwater scenes from monocular sequences of images}, year = {2014}, keywords = {monocular, sequences}, author = {Yuri Rzhanov and Han Hu and Boyen, Thierry} } @article {5255, title = {Euclidean reconstruction of natural underwater scenes}, year = {2014}, month = {March 23-27}, address = {Louisville, KY}, author = {Han Hu and Yuri Rzhanov and Boyen, Thierry} }