Paper Info
Title
Non-parametric image transforms for sparse disparity maps
Abstract
In this paper two image transforms are proposed for the calculation of sparse disparity maps. We present a new variation of the Census Transform, which we call the Thesholded Census Transform. This allows the calculation of the pixels around the edges without a separate edge-detection stage. Then we propose a new application of the Complete Rank Transform (which has so far only been used to calculate optical flow) to solve the Stereo-Matching problem. The utilization of both image transforms represents an improvement in error rates and computational cost against the Census Transform, which is the state of the art image transform used for Stereo-Matching.
Year
DOI
Venue
2015
10.1109/MVA.2015.7153188
Machine Vision Applications
Keywords
Field
DocType
error analysis,image matching,stereo image processing,transforms,complete rank transform,error rates,nonparametric image transforms,sparse disparity maps,stereo-matching problem,thesholded census transform
Top-hat transform,Template matching,Computer vision,Feature detection (computer vision),Pattern recognition,Image processing,Digital image correlation,Pixel,Artificial intelligence,Image restoration,Optical flow,Mathematics
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Dexmont Peña122.10
Alistair Sutherland210114.36