An Efficient Ellipse-Drawing Algorithm

Aken, Jerry R. Van

doi:10.1109/mcg.1984.275994

Cited by 86 publications

(31 citation statements)

References 6 publications

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“…The background mean ( , , and ) and background standard deviation ( , , and ) are computed from values of pixels on an ellipsoidal patch traced by the midpoint ellipse algorithm to achieve computational efficiency [72,73]. Moreover, the object mean ( , , and ) is computed from values of pixels within a rectangular patch whenever the inequalities ( , ) < − and ( , ) > + are concomitantly satisfied, where = , , are the image channels, ( , ) is a given pixel value,…”

Section: Salient Pixel Computationmentioning

confidence: 99%

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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Section: Salient Pixel Computationmentioning

confidence: 99%

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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“…This incremental technique can be applied to integer computation of circles as well.In [5]a slightly different formulation of Bresenham's algorithm known as the midpoint technique was proposed, which was later adapted by Van Aken [6] and other researchers. Wu and Rokne [7] modified Bresenham's algorithm so that a single decision variable works for 2 pixels and consequently the line drawing time becomes half.…”

Section: Previous Workmentioning

confidence: 99%

An Analysis of Scan Converting a Line with Multi Symmetry

Khairullah¹

2013

IJCA

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Line is a very important primitive in computer graphics. In this paper we analyze and discussan algorithm that exploits the multi symmetry present in certain line segments during scan conversion.This feature is implemented with the simple technique of direct line equation; digital differentiation analyzer (DDA) algorithm and the floating-point operation free Bresenham's Algorithm. The benefit of exploiting this feature is clearly seen in the test results. Test results also show that by exploiting this feature, execution times of all these algorithms are very close, as the variations in these algorithms work for very small fraction of the line and the rest of the line is simply replicated from this pre-computation.

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“…However, both methods require a compulsory evidence-collecting step for future iterations. The test i S is generated by the midpoint circle algorithm (MCA) [26] which is a well-known algorithm to determine the required points for drawing a circle. MCA requires as inputs only the radius r and the center point 0 0 ( , ) x y considering only the first octant over the circle equation x 2 + y 2 = r 2 .…”

Section: Considered the Allowed Range Is Defined Asmentioning

confidence: 99%

Circle detection on images using learning automata

et al. 2012

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Circle detection over digital images has received considerable attention from the computer vision community over the last few years devoting a tremendous amount of research seeking for an optimal detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of conventional Hough transform principles. The proposed algorithm is based on Learning Automata (LA) which is a probabilistic optimization method that explores an unknown random environment by progressively improving the performance via a reinforcement signal (objective function). The approach uses the encoding of three non-collinear points as a candidate circle over the edge image. A reinforcement signal (matching function) indicates if such candidate circles are actually present in the edge map. Guided by the values of such reinforcement signal, the probability set of the encoded candidate circles is modified through the LA algorithm so that they can fit to the actual circles on the edge map. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.

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An Efficient Ellipse-Drawing Algorithm

Cited by 86 publications

References 6 publications

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

An Analysis of Scan Converting a Line with Multi Symmetry

Circle detection on images using learning automata

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