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

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research in edge detection: edge definition, properties of detectors, the methodology of edge detection, the mutual influence between edges and detectors, and existing edge detectors and their implementation.

Edge Detection Techniques-An Overview

This paper describes a novel method for image corner detection based on the curvature scale-space (CSS) representation. The first step is to extract edges from the original image using a Canny detector (1986). The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS and tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors An improvement to Canny edge detector's response to 45/spl deg/ and 135/spl deg/ edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zero-crossings of image edge contours) in addition to the traditional corners.

/pdf/robust-image-corner-detection-through-curvature-scale-space-4y8gb64f1t.pdf

Robust image corner detection through curvature scale space

A vision-based vehicle guidance system for road vehicles can have three main roles: (1) road detection; (2) obstacle detection; and (3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. The authors think that traffic signs most play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown.

/pdf/road-traffic-sign-detection-and-classification-27yjg9agbp.pdf

Road traffic sign detection and classification

A corner is defined as the junction point of two or more stright line edges. Corners are special features in a image. They are of great use in computing the optical flow and structure from motion. In this paper, we report an optimal corner detector which uses a mathematical model for a corner. An optimal gray tone corner detector is derived for a restricted case of corners, i.e., corners made by lines which are symmetric about a horizontal axis. The resultant corner detector is described by the product of the sine in x and an exponential in the y direction in a portion of the mask and by the product of two sines in x and y directions in the remaining portion. It is then generalized to include any corner of an arbitrary angle and orientation. This results in an approximation of all corners by a total of twelve major types. It is observed that all the twelve masks can actually be configured with four smaller sub-masks, and this results in a significant reduction in the computations. The computations are further reduced by using the separability of masks. Results for synthetic and real scenes are reported.

Optimal corner detector

A corner is defined as the junction point of two or more straight line edges Corners are special features in a image They are of great use in computing the optical flow and structure from motion In this paper, we report an optimal corner detector which uses a mathematical model for a corner An optimal gray tone corner detector is derived for a restricted case of corners, ie corners made by lines which are symmetric about a horizontal axis The resultant corner detector is described by product of sine in x and exponential in y direction in a portion of the mask and by the product of two sines in x and y directions in the remaining portion of it It is then generalized to include any corner of an arbitrary angle and orientation This results in an approximation of all corners by a total of twelve major types It is observed that all the twelve masks can actually be configured with four smaller sub-masks, and this results in a significant reduction in the computetions The computations are further reduced by using the the separability of masks Results for synthetic and real scenes are reported

D. van Brackle

Papers

Optimal corner detector

Optimal Corner Detector