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      Chord Angle Deviation using Tangent (CADT), an Efficient and Robust Contour-based Corner Detector

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          Abstract

          Detection of corner is the most essential process in a large number of computer vision and image processing applications. We have mentioned a number of popular contour-based corner detectors in our paper. Among all these detectors chord to triangular arm angle (CTAA) has been demonstrated as the most dominant corner detector in terms of average repeatability. We introduce a new effective method to calculate the value of curvature in this paper. By demonstrating experimental results, our proposed technique outperforms CTAA and other detectors mentioned in this paper. The results exhibit that our proposed method is simple yet efficient at finding out corners more accurately and reliably.

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          Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique

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            Corner detection and classification using anisotropic directional derivative representations.

            This paper proposes a corner detector and classifier using anisotropic directional derivative (ANDD) representations. The ANDD representation at a pixel is a function of the oriented angle and characterizes the local directional grayscale variation around the pixel. The proposed corner detector fuses the ideas of the contour- and intensity-based detection. It consists of three cascaded blocks. First, the edge map of an image is obtained by the Canny detector and from which contours are extracted and patched. Next, the ANDD representation at each pixel on contours is calculated and normalized by its maximal magnitude. The area surrounded by the normalized ANDD representation forms a new corner measure. Finally, the nonmaximum suppression and thresholding are operated on each contour to find corners in terms of the corner measure. Moreover, a corner classifier based on the peak number of the ANDD representation is given. Experiments are made to evaluate the proposed detector and classifier. The proposed detector is competitive with the two recent state-of-the-art corner detectors, the He & Yung detector and CPDA detector, in detection capability and attains higher repeatability under affine transforms. The proposed classifier can discriminate effectively simple corners, Y-type corners, and higher order corners.
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              Curvature scale space corner detector with adaptive threshold and dynamic region of support

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                Author and article information

                Journal
                2017-02-15
                Article
                1702.04843
                cb866074-893f-49ab-9b82-182e11993872

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                Conference Name - 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR17); Conference Date - 13 Feb, 2017; Conference Venue - University of Dhaka, Dhaka, Bangladesh
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

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