Mean-Standard Deviation Descriptor and Line Matching
WANG Zhi-Heng1,2, WU Fu-Chao1
1.National Laboratory of Pattern Recognition, Institute of Automation,Chinese Academy of Sciences, Beijing 100190 2.College of Computer Science and Technology, Henan Polytechnic University, Jiaozue 454003
Abstract:An idea is put forward for automatic line matching based on line descriptor. Firstly, a parallel neighborhood for a line segment is defined and it is decomposed into several parallel line segments. Next, a line description matrix (DM) is formed by selecting an image feature. Finally, the descriptor of line descriptor is obtained by computing the mean and standard deviation of column vectors of DM. Thus, the line descriptor construction is accomplished. Based on different image features (gray, gradient and gradient magnitude), three line descriptors are proposed for line matching and all of them are invariant to image shift, rotation, and linear illumination changes. Experimental results show that these descriptors have good performance in line matching.
[1] Tang A W K, Ng T P, Hung Y S, et al. Projective Reconstruction from Line-Correspondences in Multiple Uncalibrated Images. Pattern Recognition, 2006, 39(5): 889-896 [2] Aider O A, Hoppenot P, Colle E. A Model-Based Method for Indoor Mobile Robot Localization Using Monocular Vision and Straight-Line Correspondences. Robotics and Autonomous Systems, 2005, 52(2/3): 229-246 [3] Shi Fanhuai, Wang Jianhua, Zhang Jing, et al. Motion Segmentation of Multiple Translation Objects from Line Correspondences. Pattern Recognition, 2005, 38(10): 1775-1778 [4] Bartoli A, Sturm P. Multiple-View Structure and Motion from Line Correspondences // Proc of the IEEE International Conference on Computer Vision. Madison, USA, 2003: 207-212 [5] Belongie S, Malik J, Puzicha J. Shape Matching and Object Recognition Using Shape Contexts. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509-522 [6] Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110 [7] Ke Yan, Sukthankar R. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Washington, USA, 2004, Ⅱ: 506-513 [8] Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(10):1615-1630 [9] Mikolajczyk K, Zisserman A, Schmid C. Shape Recognition with Edge-Based Features // Proc of the British Machine Vision Conference. Norwich, UK, 2003, Ⅱ: 779-788 [10] Matas J, Chum O, Urban M, et al. Robust Wide Baseline Stereo from Maximally Stable Extremal Regions // Proc of the British Machine Vision Conference. Cardiff, UK, 2002, Ⅰ: 384-396 [11] Hartley R. A Linear Method for Reconstruction from Lines and Points // Proc of the 5th IEEE International Conference on Computer Vision. Cambridge, USA, 1995: 882-887 [12] Lourakis M I A, Halkidis S T, Orphanoudakis S C. Matching Disparate Views of Planar Surfaces Using Projective Invariants. Image and Vision Computing, 2000, 18(9): 673-683 [13] Schmid C, Zisserman A. Automatic Line Matching across Views // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. San Juan, USA, 1997: 666-671 [14] Schmid C, Zisserman A. The Geometry and Matching of Lines and Curves over Multiple Views. International Journal of Computer Vision, 2000, 40(3): 199-233 [15] Herbert B, Vittorio F, Luc V G. Wide-Baseline Stereo Matching with Line Segments // Proc of the 14th International Conference on Pattern Recognition. San Diego, USA, 2005, Ⅰ: 329-336 [16] Deng Y, Lin Xueyin. A Fast Line Segment Based Dense Stereo Algorithm Using Tree Dynamic Programming // Proc of the 9th European Conference on Computer Vision. Graz, Austria, 2006: 201-212 [17] Goedem T, Tuytelaars T, Gool L V. Fast Wide Baseline Matching with Constrained Camera Position // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Washington, USA, 2004: 24-29 [18] Egnal G, Wildes R P. Detecting Binocular Half-Occlusions: Empirical Comparisons of Four Approaches // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA, 2000: 466-473