A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

Abstract

We propose a novel Mean-Shift-based building approach in wide baseline. Initially, scale-invariance feature transform (SIFT) approach is used to extract relatively stable feature points. As to each matching SIFT feature point, it needs a reasonable neighborhood range so as to choose feature points set. Subsequently, in view of selecting repeatable and high robust feature points, Mean-Shift controls corresponding feature scale. At last, our approach is employed to depth image acquirement in wide baseline and Graph Cut algorithm optimizes disparity information. Compared with the existing methods such as SIFT, speeded up robust feature (SURF), and normalized cross-correlation (NCC), the presented approach has the advantages of higher robustness and accuracy rate. Experimental results on low resolution image and weak feature description in wide baseline confirm the validity of our approach.

EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function

“…Computation of filter (6) in an arbitrary orientation requires two additional basis filters. We have chosen g 60 ∘…”

“…Local image descriptors represent an important area of research in computer vision. Reliable local feature matching is required in numerous applications, for example, in emerging mobile visual search (MVS) [1], panorama stitching [2], image mosaicing [3], texture classification [4], partial-duplicate web image retrieval [5], wide-base stereo [6], and object recognition [7,8]. Computer vision researchers have proposed many types of descriptors.…”