Contour-based Corner Detection Research Articles

A dedicated benchmark for contour-based corner detection evaluation

Numerous contour-based corner detection (CBCD) algorithms have been proposed recently, necessitating effective and practical evaluation. Most existing methods evaluate corner detection accuracy through metrics between the testing image and its attacked versions or rely on image-specific ground truth for corner evaluation. These methods use images as input, failing to solely evaluate the corner detection performance but combining it with contour extraction evaluation. Since contour extraction is another important research topic and existing CBCD algorithms almost have no contribution to contour extraction, this intertwining may negatively impact the evaluation results, hindering corner detection development. Furthermore, most evaluation methods directly provide simple statistical scores of evaluation metrics, such as the mean value, which are inadequate to reflect the overall performance distribution. This study presents a novel benchmark that is specifically designed for assessing CBCD methods, which includes two major contributions. Firstly, we design two dedicated datasets, one with the ground-truth corner and the other without them. The dedicated contours instead of images are employed as input to evaluate numerous CBCD methods, eliminating the impact of the extracted contour quality. When the ground-truth corners are unavailable, we employ additional contour attacks, including Gaussian noise, projective, and combined geometry on contours, to simulate real-world complex image processes compared with the attacks in existing evaluation methods. Secondly, we evaluate the performance of twelve CBCD methods using six distinct metrics based on the constructed contour datasets. To gain a deep insight into the overall performance distribution, the sign test method for hypothesis testing is utilized alongside some simple statistical measures for evaluation metric analysis. Experimental results demonstrated that no individual method performs the best across all six evaluation metrics, while different CBCD algorithms have their positive scenarios. The evaluation code will be publicly available at https://github.com/roylin1229/CBCD_eva.

Robust Corner Finding Based on Multi-Scale K-Cosine Angle Detection

Corner is widely utilized in computer vision and image processing. As a representative contour-based corner detection algorithm, RJ detector is first proposed to use the K-cosine to estimate curvature of digital curves for corner finding. However, such influential approach is quite sensitive to the geometric transformations and noise due to its dynamic smoothing scale. To overcome this drawback and enhance its performance further, this paper presents a multi-scale version of RJ detector. First, we adopt fixed region of radius (RoS) to avoid its sensitiveness to geometric transformations; second, the technique of scale product is employed to enhance curvature extreme peaks and suppress noise for improving localization. Extensive experiments on several corner detection datasets are conducted for evaluating its performances. And the experimental results demonstrate that such simple idea endows RJ an incredible improvement and MSRJ achieves the competitive performance compared with state-of-the-arts corner detectors under measure metrics of average repeatability and localization error.

Corner detection based on tangent-to-point distance accumulation technique

The core of the contour-based corner detection is essentially performing a good curvature estimation on planar curves. Inspired by intuitive observation that the curvature of a point on a contour is proportional to the distance accumulation of its neighbors to the tangent of the point, we present a novel curvature estimator named Relative Tangent-to-Point Distance Accumulation (RTPDA) for contour-based corner detection. In the approach, we fit the curve segments with quadratic polynomials by employing least square technique to derive the tangent of the target point, and then accumulate the distance of its neighbors to the tangent, which is a good approximation of the discrete curvature. Experiments verify the effectiveness and the efficiency of the proposed detector in comparison with several influential corner detectors under three commonly used evaluation metrics, namely, Average Repeatability (AR), Localization Error (LE) and Accuracy index (ACU).

Robust corner detection using altitude to chord ratio accumulation

As one of the most significant image local features, corner is widely utilized in many computer vision applications. A number of contour-based corner detection algorithms have been proposed over the last decades, among which the chord-to-point distance accumulation (CPDA) corner detector is reported to produce robust performance in corner detection, especially compared with curvature scale-space (CSS) based corner detectors, which are sensitive to local variation and noise on the contour. In this paper, we investigate the CPDA algorithm in terms of its limitations, and then propose the altitude-to-chord ratio accumulation (ACRA) corner detector based on CPDA approach. Altitude-to-chord ratio is insensitive to the selection of chord length compared with chord-to-point distance, which allows us utilize a single chord instead of the three chords used in CPDA algorithm. Besides, we replace the maximum normalization used in CPDA algorithm with the linear normalization to avoid the uneven data projection. Numerical experiments demonstrate that the proposed ACRA corner detection algorithm outperforms the CPDA approach and other seven state-of-the-art methods in terms of the repeatability and localization error evaluation metrics.

Corner detection using Chebyshev fitting‐based continuous curvature estimation

Extensive contour-based corner detection methods have been proposed to estimate the curvature of a planar curve and most of them estimated it in digital spaces. However, the curvature should be intuitively continuous. Presented is a novel approach which addresses the corner detection issue by employing the Chebyshev polynomial fitting to estimate the curvature in a continuous way. Experimental results demonstrate that the approach achiever a promising performance in comparison with three representative corner detectors based on discrete curvature estimation and two other state-of-the-art methods.

Contour-based corner detection via angle difference of principal directions of anisotropic Gaussian directional derivatives

This paper presents a contour-based corner detector using the angle difference of the principal directions of anisotropic Gaussian directional derivatives (ANDDs) on contours. The noise -robust ANDDs can characterize fine directional intensity variations around edge pixels and corners. The proposed corner detector consists of three steps: Extraction of edge map by the Canny edge detector, improvement of contours by the Douglas-Peucker (D-P) polygonal approximation and filling small gaps between contours, and finding corners from contours by using the angle difference of the principal directions of the ANDDs as the corner measure. Different from the existing contour-based detectors where corner measures correspond to the geometric properties of a contour, the new corner measure utilizes the directional intensity variations at the pixels on a contour and thus has high angular resolution, good localization, and noise-robustness. The proposed detector is compared with the three state-of-the-art detectors from two aspects. Two test images with ground truths are used to assess its detection capability and localization accuracy. Twenty-four test images with various scenes and without ground truths are used to evaluate its repeatability under affine transforms, JPEG compression, and noise degradation. The experimental results show that the proposed detector attains better overall performance.

Contour-Based Corner Detection and Classification by Using Mean Projection Transform

Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error (Le) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images.

Performance Comparisons of Contour-Based Corner Detectors

Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues-curve smoothing and curvature estimation, which have major impacts on the corner detection performance, are discussed. A number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.

ACCUMULATIVE INTERSECTION SPACE BASED CORNER DETECTION ALGORITHM

There is no parametric formulation of corner, so the conventional Hough transform cannot be employed to detect corners directly. A random corner detection method is developed in this paper based on a new concept "accumulative intersection space" under Monte Carlo scheme. This method transforms the corner detection in the image space into local maxima localization in the accumulative intersection space where the intersections are accumulated by random computations. The proposed algorithm has been demonstrated by both theory and experiments. The proposed algorithm is isotropic, robust to image rotation, insensitive to noise and false corners on diagonal edges. Unlike the other existing contour based corner detection methods, our algorithm can effectively avoid the influence of the edge detectors, such as rounding corners or line interceptions. Extensive comparisons among our approach and the other detectors including Harris operator, Fei Shen and Han Wang detector, Han Wang and Brady detector, Foveated Visual Search method and SIFT feature, have shown the effectiveness of our method.