Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Digital Image Processing
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Non-parametric Local Transforms for Computing Visual Correspondence
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A versatile stereo implementation on commodity graphics hardware
Real-Time Imaging
Progressive Refinement of Raster Images
IEEE Transactions on Computers
Comparison of nonparametric transformations and bit vector matching for stereo correlation
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Adaptive window growing technique for efficient image matching
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Road-signs recognition system for intelligent vehicles
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Hi-index | 0.00 |
This paper extends the concepts of image matching in the non-parametric space and binary distance measures. Matching in the nonparametric domain exhibits many desirable properties at relatively small computation complexity: It concentrates on capturing mutual relation among pixels in a small neighbourhoods rather than bare intensity values, thus improving matching discrimination. It is also more resistive against noise and uneven lighting conditions of the matched images. Last but not least, the matching algorithms operate in the integer domain and can be easily implemented in hardware what benefits in dramatic improvement of their run times. In this paper we extend the concept of nonparametric image transformation into the realm of colour images taking into consideration different colour spaces and different distances defined in these spaces. We propose significant bit reduction for aggregated block matching in the Census domain. We propose also the sparse sampling model for the Census transformation that increase the discriminative power of this representation and allows even further reduction of bits necessary for matching. The presented techniques have been applied to matching of the stereo images but can be employed in any computer vision task that requires comparison of images, such as image registration, object detection and recognition, etc. Presented experiments exhibit interesting properties of the described techniques.