Object Detection and Recognition: A Survey
Author(s):
Anjana Mittal , Shri Shankracharya college of engineering and technology, junwani,bhilai,C.G.; Dr. Siddhartha Choubey, Shri Shankracharya college of engineering and technology, junwani,bhilai,C.G.Keywords:
Object recognition, binary descriptor, spatial granularity, local difference binary and multiple - griddingAbstract:
The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pair wise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.
Other Details:
| Manuscript Id | : | J4RV2I6010 |
| Published in | : | Volume : 2, Issue : 6 |
| Publication Date | : | 01/09/2016 |
| Page(s) | : | 28-32 |
