Angle Detection on Digital Curves

Rosenfeld, Azriel; Johnston, Emily

doi:10.1109/tc.1973.5009188

Cited by 446 publications

(185 citation statements)

References 6 publications

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“…According to Lee et al, the determination of the parameter q in Equation (7) depends on the orientation resolution and the corner discrimination ability. Previous work in digital curves analysis and representation [26,27] have pioneered the use of edge gradient direction to represent shape contour. Rosenfeld and Johnston (1973) argument that the choice of the smoothing parameter q can be challenging, and they suggested q > 1 to provide a smoothed slope measurement in the corner profile represented by the angulation signal.…”

Section: Lee Et Al Techniquementioning

confidence: 99%

Multiscale Corner Detection in Planar Shapes

Paula

Medeiros

Bezerra³

et al. 2012

J Math Imaging Vis

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This paper presents a multiscale corner detection method in planar shapes, which applies an undecimated Mexican hat wavelet decomposition of the angulation signal to identify significant points on a shape contour. The advantage of using this wavelet is that it is well suited for detecting singularities as corners and contours due to its excellent selectivity in position. Thus, this wavelet plays an important role in our approach because it identifies changes in non-stationary angulation signals, and it can be extended to multidimensional approaches in an efficient way when approximating this wavelet by difference of Gaussian. The proposed algorithm detects peaks on a correlation signal which is generated from different wavelet scales and retains relevant points on the decomposed angulation signal while discards poor information. Our approach assumes that only peaks which persist through several scales correspond to corners. Furthermore, we introduce a novel procedure to tune parameters for the corner detection algorithms that corresponds to the best relation between Precision and Recall measures. This technique guides the parameter adjustment of the algorithms according to the image database and it improves their performance with regard to true corner detection. Concerning the performance assessment of the algorithms, we compare the proposed one to other corner detectors by using Precision and Recall measures which are based on ground-truth information. Tests were carried out using more than a hundred images from a non-homogenous database that contains noisy and non-noisy binary shapes 1

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Section: Lee Et Al Techniquementioning

confidence: 99%

Multiscale Corner Detection in Planar Shapes

Paula

Medeiros

Bezerra³

et al. 2012

J Math Imaging Vis

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show abstract

“…The fact that studies have shown that slightly concave curvature may be better suited [15] is left as a possible extension to the work. For this task a combination of the Canny edge detector and the k-angular bending algorithm [16] was used. First, the projection images described above are preprocessed by erosion and dilation steps.…”

Section: A Finding Good Regions To Graspmentioning

confidence: 99%

“…The curvature part is handled by the k-angular bending algorithm [16], that considers k neighbours in each direction of a point to determine its curvature by calculating the angles to these neighbours. Let C be the ordered list of points on the contour, c i = (x i , y i ) the i th point in this list, a i,k = c i+k − c i and b i,k = c i−k − c i .…”

Section: A Finding Good Regions To Graspmentioning

confidence: 99%

Learning of 2D grasping strategies from box-based 3D object approximations

Geidenstam

Huebner

Banksell

et al. 2009

Robotics: Science and Systems V

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Abstract-In this paper, we bridge and extend the approaches of 3D shape approximation and 2D grasping strategies. We begin by applying a shape decomposition to an object, i.e. its extracted 3D point data, using a flexible hierarchy of minimum volume bounding boxes. From this representation, we use the projections of points onto each of the valid faces as a basis for finding planar grasps. These grasp hypotheses are evaluated using a set of 2D and 3D heuristic quality measures. Finally on this set of quality measures, we use a neural network to learn good grasps and the relevance of each quality measure for a good grasp. We test and evaluate the algorithm in the GraspIt! simulator.

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“…Many authors have proposed variety of algorithms in the existing literature [3][4][5][6][7][8][9][10][11][12][13]. Rosenfeld and Johnston [8] use k-cosine angle measurement to find corners. Rosenfeld and Weszka [9] introduce some modifications of [8] in which the maxima of the difference between successive of the averaged k-cosine angle on a digital curve between the k backward and forward vector were used.…”

Section: Introductionmentioning

confidence: 99%

“…Rosenfeld and Johnston [8] use k-cosine angle measurement to find corners. Rosenfeld and Weszka [9] introduce some modifications of [8] in which the maxima of the difference between successive of the averaged k-cosine angle on a digital curve between the k backward and forward vector were used. Freeman and Davis [6] corner detector contains the chain scanned [8] with line sliding along the contour.…”

Section: Introductionmentioning

confidence: 99%

On REE and EER Methods for Mining Corner Points on the Images

Sarfraz¹,

Swati²

2014

JCC

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This paper reviews, implements and compares two corner detection algorithms for mining corner points on the generic shapes. These corner detectors detect corners by using combination of one rectangle (R) and two ellipses (EE). These algorithms have been used with different combinations: REE and EER together with different parameter settings in their descriptions. REE and EER combinations slide along the boundary of the shape and record number of boundary points in each rectangle and ellipses. REE and EER setup represent both local and global views of the image outlines and present natural corner detection methodologies to detect and mine all true corners accurately. A comparative study demonstrates the superiority of the REE and EER over some of the existing algorithms.

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Angle Detection on Digital Curves

Cited by 446 publications

References 6 publications

Multiscale Corner Detection in Planar Shapes

Multiscale Corner Detection in Planar Shapes

Learning of 2D grasping strategies from box-based 3D object approximations

On REE and EER Methods for Mining Corner Points on the Images

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