Widely Separated View Matching


Lab

Abstract

A new feature point detector is proposed which uses a wedge model to characterize corners by their orientation and angular width. This detector is compared to two popular feature point detectors: the Harris and SUSAN detectors, on the basis of some defined quality attributes. It is also shown how feature points between widely separated views can be matched by using information provided by the detector to approximate local affine transformations.

• Accuracy , or the ability to consistently detect a given image pattern, at the exact same location, in spite of minor variability in intensity values, in orientation, or in scale. This property is significant when the detector is tuned to detect well-defined structures, such as specific types of junctions or corners. Accuracy can be assessed by measuring the alignment of extracted features located on a straight line, or by measuring the distance between a detected junction and the point of intersection of the two lines defining it.
• Robustness, or insensitivity to noise. Detection on noisy images can produce false positives, corresponding to noise patterns rather than true feature points. Also, in the presence of noise, some feature points can be lost or not localized properly. Robustness can be evaluated empirically, or theoretically.
• Sensitivity of the detection, that is the ability to detect feature points in low contrast conditions. Usually, there are some parameters which control the sensitivity of a detector, and most often, a tradeoff exists between sensitivity and robustness.
• Stability of the detection. A detected feature point should continue to be detected after an image undergoes some geometrical transformation (especially the perspective deformation due to a change in viewpoint), and under different conditions of illumination. This is essential in the context of multi-view matching. A good measure of stability is the repeatability of detected features across several views.
• Controllability of a detector. This is mainly determined by the number of parameters which control its behavior, and their relative sensitivity. It is certainly useful to be able to control the number of feature points which will be selected in an image, ideally using a single control parameter. Other parameters might also be used to filter out certain kinds of features, not of interest in a specific application. However, the effect of each parameter should be specific and predictable enough to allow an easy tuning.
• Richness of the information provided about the detected feature points. When a detector returns various characteristics of feature points, and not just a strength measure, the additional information can be exploited in the task to follow. For example, it could be used to classify feature points into categories (e.g. type of junction), or in matching, as a tool to normalize the patterns being matched.
• Variability in the characteristics of detected feature points. A high variability ensures that several feature points are detected, regardless of the nature of the image under analysis. Good variability is also critical for matching, where the feature points must be easily distinguishable from each other.
• Complexity of the detector, or the speed at which it identifies corners in an image. In many applications, feature point detection is a preprocessing operation that must be performed at frame rate. However, a comparison of detection speeds is difficult to achieve, since the efficiency of a given feature point detector depends on its implementation. Nevertheless, the complexity of the operations which must be applied to each image point must remains sufficiently low.

Here (non-optimized version)

Publications

Robert Laganière and Étienne Vincent,
Detecting and matching feature points ,
in Journal of Visual Communication and Image Representation , vol. 16, issue 1, pp. 38-54, Febuary 2005.
Elsevier Science Direct

Robert Laganière and Étienne Vincent,
Wedge-based Corner Model for Widely Separated Views Matching,
in Proc. 16th International Conference on Pattern Recognition, vol. 3, Quebec City, Canada, August 2002.
PDF [1.2 mb]

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