Robust corner detection using altitude to chord ratio accumulation

Lin, Xinyu; Zhu, Ce; Liu, Yipeng; Zhang, Qian

doi:10.1007/s11042-017-5606-9

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…As for contours or boundaries, the curvature extreme points or the intersection points of two line contours are usually considered as corners [4]. Generally, there exists three categories of approaches for corner detection in the current literatures [5]: Model-based algorithms [6][7][8], intensity-based algorithms [9][10][11][12][13], contour-based algorithms [4,5,[14][15][16][17][18][19][20][21][22][23][24][25][26]. Compared with another two types of methods, contour-based algorithms show good characteristics considering their low rate of detection error [14].…”

Section: Introductionmentioning

confidence: 99%

“…As for contour‐based corner detection algorithms, corner response function (CRF) at each pixel point on the contour is calculated to reflect the sharpness of the curve changes at that position. Accurately measuring the CRF is the key step for contour‐based corner detectors, since it directly affects the performance and computational complexity [14]. Mokhtarian first proposes employing the curvature scale‐space (CSS) [15] technology for corner detection which has become the landmark method in this filed.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, there exists three categories of approaches for corner detection in the current literatures [5]: Model‐based algorithms [6–8], intensity‐based algorithms [9–13], contour‐based algorithms [4, 5, 14–26]. Compared with another two types of methods, contour‐based algorithms show good characteristics considering their low rate of detection error [14].…”

Section: Introductionmentioning

confidence: 99%

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An efficient corner detector based on ratio of parallelogram diagonal

Zhang

Liu

Wang

et al. 2021

The Journal of Engineering

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Corner is an important local feature of image which has been widely applied on various computer vision and image processing tasks. Here, a contour-based corner detector is developed by using the ratio of parallelogram diagonals (RPD) to estimate the curve curvature. The main advantage of RPD detector is that only once square root operation is required to calculate the curvature value at each point on a contour while maintaining good noise robustness. The contributions of this paper include the following three aspects: First, the motivation of the proposed RPD curvature is illustrated by means of parallelogram theory; second, a complete corner detector is proposed based on RPD curvature; third, comprehensive experiments are carried out and the experimental results show superior performances of the proposed method against another five strong baselines. In these experiments, RPD runs 100% faster than the prior works. Moreover, a mean accuracy of 83.87% is reported on GCM dataset which is an improvement of about 0.9% and a mean accuracy of 74.21% is reported on CPDA dataset which is an improvement of about 0.2%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An efficient corner detector based on ratio of parallelogram diagonal

Zhang

Liu

Wang

et al. 2021

The Journal of Engineering

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“…To overcome the above‐mentioned problems, Awrangjeb and Lu [23] proposed a corner detection technique named chord‐to‐point distance accumulation (CPDA), which they improved to a faster version, F‐CPDA [24]. Nevertheless, both have some drawbacks, so several corner detectors, such as chord to triangular arms ratio (CTAR) [31] and altitude‐to‐chord ratio accumulation (ACRA) [32], have been proposed to overcome their weaknesses. Meanwhile, many state‐of‐the‐art‐ and contour‐based corner detectors have been proposed, including laplacian of gaussian (LoG) [33], weighted eigenvector‐based angle estimator (WEAE) [34] and second‐order difference of contour (SODC) [35].…”

Section: Introductionmentioning

confidence: 99%

Corner detection using the point‐to‐centroid distance technique

et al. 2020

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“…For example, the curvature values estimated of CPDA technique to measure the sharpness of the corners may not be proportional to the angle of the corners [23] and meanwhile some weak corners would be missed due to the larger radius of the RoS. To address this issue in CPDA, Teng et al [23] proposed to utilize simple triangular theory and distance calculation for effective and efficient corner detection (CTAR) and Lin et al [28] proposed to use the altitude-to-chord ratio accumulation (ACRA) as the curvature significance. In recent years, many other impressive contourbased corner detectors have also been proposed [21]- [29].…”

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confidence: 99%

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

Zhang

et al. 2020

IEEE Access

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Corner is widely utilized in computer vision and image processing. As a representative contourbased 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. INDEX TERMS Corner detection, image processing, multi-scale product, average repeatability. I. INTRODUCTION Corners are the outstanding local features of image and have played important roles on many applications in image processing and computer vision, such as robot navigation, video retrieval and intelligent transportation systems and so on. By now, plenty of corner detectors have been proposed, which can be broadly separated into two kinds: intensity-based detectors [1]-[9] and contour-based detectors [11]-[29]. With respect to contour-based methods, corners are mainly detected on the boundaries extracted from images among which estimating the digital curvature of the boundaries is the critical step. Rosenfeld and Johnston (RJ) [11] first considered angle as the discrete curvature to find corners for digital curves and later they presented its improved version which used average angle instead [12]. In RJ algorithm and its improved version, the angles of a digital curve are calculated by K-cosine, where K is the radius of the region of support (RoS). Since the angle The associate editor coordinating the review of this manuscript and approving it for publication was Zhihan Lv .

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Robust corner detection using altitude to chord ratio accumulation

Cited by 11 publications

References 28 publications

An efficient corner detector based on ratio of parallelogram diagonal

An efficient corner detector based on ratio of parallelogram diagonal

Corner detection using the point‐to‐centroid distance technique

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

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