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Non-orthogonal binary subspace and its applications in computer vision (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Tao, Hai Crabb, Ryan Tang, Feng |
| Description | In ICCV This paper presents a novel approach that represents an image or a set of images using a nonorthogonal binary subspace (NBS) spanned by boxlike base vectors. These base vectors possess the property that the inner product operation with them can be computed very efficiently. We investigate the optimized orthogonal matching pursuit method for finding the best NBS base vectors. It is demonstrated in this paper how the NBS based expansion can be applied to speed up several common computer vision algorithms, including normalized cross correlation (NCC), sum of squared difference (SSD) matching, appearance subspace projection and subspace-based object recognition. Promising experimental results on facial and natural images are demonstrated in this paper. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Non-orthogonal Binary Subspace Boxlike Base Vector Novel Approach Inner Product Operation Natural Image Appearance Subspace Projection Subspace-based Object Recognition Nb Base Vector Base Vector Nonorthogonal Binary Subspace Experimental Result Computer Vision Cross Correlation Several Common Computer Vision Algorithm Optimized Orthogonal Matching Pursuit Method |
| Content Type | Text |
| Resource Type | Article |
