IEEE Trans Pattern Anal Mach Intell - Local Difference Binary for Ultra-fast and Distinctive Feature Description.

Tópicos

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Resumo

The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, existing descriptors are either too computation-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 pairwise 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 comparing to existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieves a greater accuracy and faster speed for mobile object recognition and tracking tasks.

Resumo Limpo

effici qualiti featur descriptor critic user experi mani comput vision applic howev exist descriptor either computationexpens achiev realtim perform suffici distinct identifi correct match larg databas various transform paper propos high effici distinct binari descriptor call local differ binari ldb ldb direct comput binari string imag patch use simpl intens gradient differ test pairwis grid cell within patch multipl grid strategi salient bit select method appli captur distinct pattern patch differ spatial granular experiment result demonstr compar exist stateoftheart binari descriptor primarili design speed ldb similar construct effici achiev greater accuraci faster speed mobil object recognit track task

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