We present an approach for illumination and affine-invariant point matching using ordinal features. Ordinal measures for matching only consider the order between pixels and not the absolute intensity values which enables them to be invariant to a monotonic change in intensities. The utility of such measures has been demonstrated in the past for point matching for some applications such as background subtraction and stereo matching. However, invariance of such methods to geometric transformations has been limited leading to their inapplicability for more generic matching applications such as object recognition, wide-baseline stereo matching, mosaicing or tracking points on moving objects. In this paper, we extend such methods for use in such applications. The method is invariant to an affine transformation in the patch, which makes it applicable to a variety of applications. At the same time, our method is robust to different types of noise processes possible in a real scene. Experiments indicate favorable performance when compared to other state-of-the-art methods for affine-invariant point matching. ©2007 IEEE.

Anurag Mittal

Department of Computer Science and Engineering

Artificial Intelligence

Computer networks

Computer vision

Image processing

Object Recognition

POINT MATCHING

applications

Fulltext

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IIT Madras

Illumination and Affine- Invariant Point Matching using an Ordinal Approach

Proceedings of the IEEE International Conference on Computer Vision

Determining the correspondence of image patches is one of the most important problems in Computer Vision. When the intensity space is variant due to several factors such as the camera gain or gamma correction, one needs methods that are robust to such transformations. While the most common assumption is that of a linear transformation, a more general assumption is that the change is monotonic. Therefore, methods have been developed previously that work on the rankings between different pixels as opposed to the intensities themselves. In this paper, we develop a new matching method that improves upon existing methods by using a combination of intensity and rank information. The method considers the difference in the intensities of the changed pixels in order to achieve greater robustness to Gaussian noise. Furthermore, only uncorrelated order changes are considered, which makes the method robust to changes in a single or a few pixels. These properties make the algorithm quite robust to different types of noise and other artifacts such as camera shake or image compression. Experiments illustrate the potential of the approach in several different applications such as change detection and feature matching. © 2006 IEEE.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Journal	Proceedings of the IEEE International Conference on Computer Vision
Open Access	Yes