Quantization-free parameter space reduction in ellipse detection

Chen, Kuang Chung; Bouguila, Nizar; Ziou, Djemel

doi:10.1016/j.eswa.2010.12.098

Cited by 13 publications

(6 citation statements)

References 24 publications

(32 reference statements)

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“…There for, the embryo detection task is simplified to detection to known or predictable number of ellipses in time-lapse image. Ellipse detection can be established using reduced quantization-free parameters space [4] or by analyzing of principal components [5]. Basically, three stable points are needed to fit an ellipse on the object contour [6][7][8].…”

Section: Computer Vision Methodsmentioning

confidence: 99%

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Jonaitis¹,

Raudonis²,

Lipnickas³

2016

IJC

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In vitro fertilization – a procedure which aims to get the embryo to adapt the methods of "oocyte" fertilized sperm outside the human body. At the end of this procedure there are several embryos. This paper represents overview of tracking-free and tracking-based methods for detection of important embryo development stages. Tracking-based method represents well known classical object tracking techniques. For tracking-free method were selected statistical feature extraction techniques and classification methods: Classification with training and classification without training. For the feature extraction proposed statistical methods: entropy, invariant moments and principal components analyses. For the classification are used neural networks, support vector machine and K-nearest neighbor method. Data collected consist of 500 images for each class. 70 percent of images are dedicated for training, and 30 percent for testing. The proposed method is checked by experiment. It is expected that this method will work well in video sequences.

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Section: Computer Vision Methodsmentioning

confidence: 99%

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Jonaitis¹,

Raudonis²,

Lipnickas³

2016

IJC

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“…Let us consider an image

I (x, y)

that may contain an area bordered by two concentric ellipses [17]. The image support is considered infinite so that we do not have to be concerned about its size.…”

Section: Scale Space Radon Transform For Ellipsementioning

confidence: 99%

Scale space Radon transform

Ziou

Nacereddine

Goumeidane

2021

IET Image Processing

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An extension of Radon transform by using a measure function capturing the user need is proposed. The new transform, called scale space Radon transform, is devoted to the case where the embedded shape in the image is not filiform. A case study is brought on a straight line and an ellipse where the SSRT behaviour in the scale space and in the presence of noise is deeply analyzed. In order to show the effectiveness of the proposed transform, the experiments have been carried out, first, on linear and elliptical structures generated synthetically subjected to strong altering conditions such blur and noise and then on structures images issued from real-world applications such as road traffic, satellite imagery and weld X-ray imaging. Comparisons in terms of detection accuracy and computational time with wellknown transforms and recent work dedicated to this purpose are conducted, where the proposed transform shows an outstanding performance in detecting the above-mentioned structures and targeting accurately their spatial locations even in low-quality images. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The geometric characteristics used for retrieving the centres of the ellipses are presented here. The geometric properties of points and tangents in ellipses are used for finding elliptic centres [37][38][39][40][41]. The elliptic centre is the intersection of the lines connecting the midpoints of chords and the intersection formed by the tangents through the endpoints of arcs.…”

Section: Confirming the Elliptic Centersmentioning

confidence: 99%

“…But for most practical applications, where a good balance of detection effectiveness and speed is sought, the proposed methods provide a good solution Here we compare the proposed strategy of determining the centre of ellipse with the existing works to verify the effect on the detection performance. In experiments, we adopt the proposed detectors with the determination of ellipse's ellipse based on the mean shift algorithm and with that of confirming the ellipse's center provided by [37][38][39][40][41] for comparing the detection performances. As shown in TableⅠ, our method with the proposed strategy of confirming ellipse's centre costs more time than the other one.…”

Section: Performance Comparisonmentioning

confidence: 99%