The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

/pdf/faster-and-better-a-machine-learning-approach-to-corner-3thqerwqfu.pdf

Faster and Better: A Machine Learning Approach to Corner Detection

In this survey, we give an overview of invariant interest point detectors, how they evolvd over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions.

https://www.nowpublishers.com/article/DownloadSummary/CGV-017

Local Invariant Feature Detectors: A Survey

A new algorithm for dominant points detection and polygonization of digital curves

https://hal.inria.fr/inria-00526714/document

A discrete geometry approach for dominant point detection

Image analysis and computer vision: 1999

The problem of corner detection on planar curves is examined based on human perception of local graphic features. First, a set of fuzzy patterns of contour points are established. Then, corner detection is characterized as a fuzzy classification problem that contains three stages: evaluation, classification, and location. Compared with existing methods, the proposed approach is superior in that it explains the curve, instead of simple labeling, and it performs based on human perception. Experimental results on shapes of various complexities are presented. The performance with respect to noise is also addressed.

Weinan Chen

Papers

Corner detection and interpretation on planar curves using fuzzy reasoning