Improved census transform for noise robust stereo matching

Jongchul Lee,Hyunki Hong,Changkyoung Eem,Daeyoon Jun

doi:10.1117/1.oe.55.6.063107

Abstract

Census transform (CT), a stereo matching algorithm, has a strong advantage in radial distortion and brightness changes. However, CT is noise-sensitive because it compares the brightness of a single central pixel based on the brightness values of neighborhood pixels within a matching window. Star-census transform, which compares the brightness of pixels separated by a certain distance along a symmetrical pattern within the matching window, is presented. The proposed method can select the distance between the pixels for comparison and comparison patterns. The experiment results show that the proposed method yields a better performance than the previous CT methods.

Highlights

Stereo vision establishes the correspondence between both views as stereo matching and calculates the dense disparity and three-dimensional (3-D) depth information
This paper introduces star-census transform (SCT), which compares the pixels separated by a certain distance symmetrically
A final disparity map is obtained using a median filter (Figs. 7 and 8). This experiment framework focuses on comparing the performance of the previous Census transform (CT), mini-census transform (MCT), and generalized-census transform (GCT) methods with the proposed SCT method

Summary

Introduction

Stereo vision establishes the correspondence between both views as stereo matching and calculates the dense disparity and three-dimensional (3-D) depth information. The stereo matching method is largely divided into two parts: local and global. The local method applies a matching window to find the correspondence between the reference image and the target image. It is more efficient than the global method because it searches only the designated area, whereas the global method explores the entire image area including the neighborhood area. The global method defines an energy model by using various conditions, such as uniqueness and continuity, and determines the matching information by minimizing the energy function of an entire image. The global method can obtain more exact difference values than the local method because it processes a search of the entire image repeatedly. Representative examples of the global method include belief propagation, graph-cut, and dynamic programming.

Methods

Results

Conclusion