A Novel Binary Feature Descriptor for Accelerated Robust Matching

Abstract

Stereo matching between images is at the base of many computer vision applications. Many traditional image features for matching focus on being robust to view-point changes with huge amount of calculation and memory storage, such as Scale Invariant Feature Transform (SIFT). Binary Robust Independent Elementary Features (BRIEF) is an efficient alternative to traditional SIFT-like features, but it is sensitive to rotation and noise. So BRIEF is not suitable for some natural scene with view-point changes. Nowadays, the deployment of computer vision algorithms on mobile devices with low power of computation and memory capacity has even upped the ante: the goal is to make descriptors efficient to extract, more compact while remaining invariant to scale, rotation and noise. In this paper, we propose a novel feature descriptor with binary strings that is very fast to compute and match with good performance of being invariant to scale, rotation and noise to address the current requirement. The binary descriptors will be directly extracted by comparing the responses of Gaussian filter of the sampling patches that effectively avoid being sensitive to noise. Our experiment results illustrate that our algorithm is fast to compute with low memory load and invariant to view-point change, blur change, brightness change, and JPEG compression.