The Geometry and Matching of Lines and Curves Over Multiple Views
International Journal of Computer Vision
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic line matching across views
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A linear method for reconstruction from lines and points
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Wide-Baseline Stereo Matching with Line Segments
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MSLD: A robust descriptor for line matching
Pattern Recognition
LSD: A Fast Line Segment Detector with a False Detection Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D line segment detection for unorganized point clouds from multi-view stereo
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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In the paper we propose a novel line segment matching method over multiple views based on sparse representation with geometric configuration constraint. The significant idea of the paper is that we transfer the issue of line correspondence into a sparsity based line recognition. At first, line segments are detected by a LSD (line segment detector) and clustered according to spatial proximity to form completed lines. For each point within a line, SIFT is extracted to represent the attribute of point and PHOG is also considered to describe the appearance of the patch centered at the point. SIFT and PHOG are simply concatenated as a single feature vector and then all these point features are put together by a max pooling function to form a distinctive line signature. Then, all line features extracted from training images are trained into a dictionary using sparse coding. Lines with the same similarity may fall together in the high-dimensional feature space. Finally, line segments in a test view are matched to their counterparts in other views by seeking maximal pulses from the coefficient vector. Under our framework, line segments are trained once and matched across all other views. Experimental results have validated the effectiveness of the approach for planar structured scenes under various transformations and degradation, such as viewpoint change, illumination, blur and compression corruption.