The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Maylor K H Leung - One of the best experts on this subject based on the ideXlab platform.
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ellifit an unconstrained non iterative least squares based geometric Ellipse fitting method
Pattern Recognition, 2013Co-Authors: Dilip K Prasad, Maylor K H Leung, Chai QuekAbstract:A novel Ellipse fitting method which is selective for digital and noisy elliptic curves is proposed in this paper. The method aims at fitting an Ellipse only when the data points are highly likely belong to an Ellipse. This is achieved using the geometric distances of the Ellipse from the data points. The proposed method models the non-linear problem of Ellipse fitting as a combination of two operators, with one being linear, numerically stable, and easily invertible, while the other being non-linear but unique and easily invertible operator. As a consequence, the proposed Ellipse fitting method has several salient properties like unconstrained, stable, non-iterative, and computationally inexpensive. The efficacy of the method is compared against six contemporary and recent algorithms based on the least squares formulation using five experiments of diverse practical challenges, like digitization, incomplete Ellipses, and Gaussian noise (up to 30%). Three of the experiments comprise of a total of 44,400 Ellipses (positive test data) while the other two are tested on 320,000 non-elliptic conics (negative test data). The results show that the proposed method is quite selective to elliptic shapes only and provides accurate fitting results, indicating potential application in medical, robotics, object detection, and other image processing industrial applications.
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edge curvature and convexity based Ellipse detection method
Pattern Recognition, 2012Co-Authors: Dilip K Prasad, Maylor K H LeungAbstract:In this paper, we propose a novel Ellipse detection method for real images. The proposed method uses the information of edge curvature and their convexity in relation to other edge contours as clues for identifying edge contours that can be grouped together. A search region is computed for every edge contour that contains other edge contours eligible for grouping with the current edge contour. A two-dimensional Hough transform is performed in an intermediate step, in which we use a new 'relationship score' for ranking the edge contours in a group, instead of the conventional histogram count. The score is found to be more selective and thus more efficient. In addition, we use three novel saliency criteria, that are non-heuristic and consider various aspects for quantifying the goodness of the detected elliptic hypotheses and finally selecting good elliptic hypotheses. The thresholds for selection of elliptic hypotheses are determined by the detected hypotheses themselves, such that the selection is free from human intervention. The method requires a few seconds in most cases. So, it is suitable for practical applications. The performance of the proposed Ellipse detection method has been tested on a dataset containing 1200 synthetic images and the Caltech 256 dataset containing real images. In both cases, the results show that the proposed Ellipse detection method performs far better than existing methods and is close to the ideal results, with precision, recall, and F-measure, all very close to 1. Further, the method is robust to the increase in the complexity of the images (such as overlapping Ellipses, occluded Ellipses), while the performance of the contemporary methods deteriorates significantly.
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a split and merge based Ellipse detector with self correcting capability
IEEE Transactions on Image Processing, 2011Co-Authors: Ys A Chia, Susanto Rahardja, Deepu Rajan, Maylor K H LeungAbstract:A novel Ellipse detector based upon edge following is proposed in this paper. The detector models edge connectivity by line segments and exploits these line segments to construct a set of elliptical-arcs. Disconnected elliptical-arcs which describe the same Ellipse are identified and grouped together by incrementally finding optimal pairings of elliptical-arcs. We extract hypothetical Ellipses of an image by fitting an Ellipse to the elliptical-arcs of each group. Finally, a feedback loop is developed to sieve out low confidence hypothetical Ellipses and to regenerate a better set of hypothetical Ellipses. In this aspect, the proposed algorithm performs self-correction and homes in on “difficult” Ellipses. Detailed evaluation on synthetic images shows that the algorithm outperforms existing methods substantially in terms of recall and precision scores under the scenarios of image cluttering, salt-and-pepper noise and partial occlusion. Additionally, we apply the detector on a set of challenging real-world images. Successful detection of Ellipses present in these images is demonstrated. We are not aware of any other work that can detect Ellipses from such difficult images. Therefore, this work presents a significant contribution towards Ellipse detection.
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Ellipse detection with hough transform in one dimensional parametric space
International Conference on Image Processing, 2007Co-Authors: Alex Yongsang Chia, Maylor K H Leung, Howlung Eng, Susanto RahardjaAbstract:The main advantage of using the Hough Transform to detect Ellipses is its robustness against missing data points. However, the storage and computational requirements of the Hough Transform preclude practical applications. Although there are many modifications to the Hough Transform, these modifications still demand significant storage requirement. In this paper, we present a novel Ellipse detection algorithm which retains the original advantages of the Hough Transform while minimizing the storage and computation complexity. More specifically, we use an accumulator that is only one dimensional. As such, our algorithm is more effective in terms of storage requirement. In addition, our algorithm can be easily parallelized to achieve good execution time. Experimental results on both synthetic and real images demonstrate the robustness and effectiveness of our algorithm in which both complete and incomplete Ellipses can be extracted.
Shiqi Duan - One of the best experts on this subject based on the ideXlab platform.
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MLICOM (2) - Obtaining Ellipse Common Tangent Line Equations by the Rolling Tangent Line Method
Machine Learning and Intelligent Communications, 2018Co-Authors: Naizhang Feng, Teng Jiang, Shiqi DuanAbstract:In the field of image processing and machine vision, it is sometimes necessary to obtain common tangent line equations and tangent point coordinates from Ellipses. A rolling tangent line method was proposed to obtain the 4 common tangent line equations and 8 tangent point coordinates from two Ellipses in this paper. The principle of this method is simple and it is easy to program on a computer. Use this method to process two Ellipse targets in an image and the experiment results show that the 4 common tangent equations and 8 tangent point coordinates can be obtained in high precision and the maximum execution time is less than 0.1 s.
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obtaining Ellipse common tangent line equations by the rolling tangent line method
International Conference on Machine Learning, 2017Co-Authors: Naizhang Feng, Teng Jiang, Shiqi DuanAbstract:In the field of image processing and machine vision, it is sometimes necessary to obtain common tangent line equations and tangent point coordinates from Ellipses. A rolling tangent line method was proposed to obtain the 4 common tangent line equations and 8 tangent point coordinates from two Ellipses in this paper. The principle of this method is simple and it is easy to program on a computer. Use this method to process two Ellipse targets in an image and the experiment results show that the 4 common tangent equations and 8 tangent point coordinates can be obtained in high precision and the maximum execution time is less than 0.1 s.
Jinglu Tan - One of the best experts on this subject based on the ideXlab platform.
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detection of incomplete Ellipse in images with strong noise by iterative randomized hough transform irht
Pattern Recognition, 2008Co-Authors: Wei Lu, Jinglu TanAbstract:An iterative randomized Hough transform (IRHT) is developed for detection of incomplete Ellipses in images with strong noise. The IRHT iteratively applies the randomized Hough transform (RHT) to a region of interest in the image space. The region of interest is determined from the latest estimation of Ellipse parameters. The IRHT ''zooms in'' on the target curve by iterative parameter adjustments and reciprocating use of the image and parameter spaces. During the iteration process, noise pixels are gradually excluded from the region of interest, and the estimation becomes progressively close to the target. The IRHT retains the advantages of RHT of high parameter resolution, computational simplicity and small storage while overcoming the noise susceptibility of RHT. Indivisible, multiple instances of Ellipse can be sequentially detected. The IRHT was first tested for Ellipse detection with synthesized images. It was then applied to fetal head detection in medical ultrasound images. The results demonstrate that the IRHT is a robust and efficient Ellipse detection method for real-world applications.
Agnès Roby-brami - One of the best experts on this subject based on the ideXlab platform.
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Modulation of Ellipses drawing by sonification
Experimental Brain Research, 2020Co-Authors: Éric Boyer, Frédéric Bevilacqua, Emmanuel Guigon, Sylvain Hanneton, Agnès Roby-bramiAbstract:Most studies on the regulation of speed and trajectory during Ellipse drawing have used visual feedback. We used on-line auditory feedback (sonification) to induce implicit movement changes independently from vision. The sound was produced by filtering a pink noise with a bandpass filter proportional to movement speed. The first experiment was performed in 2D. Healthy participants were asked to repetitively draw Ellipses during 45 second-trials whilst maintaining a constant sonification pattern (involving pitch variations during the cycle). Perturbations were produced by modifying the slope of the mapping without informing the participants. All participants adapted spontaneously their speed: they went faster if the slope decreased and slower if it increased. Higher velocities were achieved by increasing both the frequency of the movements and the perimeter of the Ellipses, but slower velocities were achieved only by decreasing the perimeter of the Ellipses. The shape and the orientation of the Ellipses were not significantly altered. The analysis of the speed-curvature power-law parameters showed consistent modulations of the speed gain factor, while the exponent remained stable. The second experiment was performed in 3D and showed similar results, except that the main orientation of the Ellipse also varied with the changes in speed. In conclusion this study demonstrated implicit modulation of movement speed by sonification and robust stability of the Ellipse geometry. Participants appeared to limit the decrease in movement frequency during slowing down in order to maintain a rhythmic and not discrete motor regimen.
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Modulation of Ellipses drawing by sonification.
Experimental brain research, 2020Co-Authors: Eric O. Boyer, Frédéric Bevilacqua, Emmanuel Guigon, Sylvain Hanneton, Agnès Roby-bramiAbstract:Most studies on the regulation of speed and trajectory during Ellipse drawing have used visual feedback. We used online auditory feedback (sonification) to induce implicit movement changes independently from vision. The sound was produced by filtering a pink noise with a band-pass filter proportional to movement speed. The first experiment was performed in 2D. Healthy participants were asked to repetitively draw Ellipses during 45 s trials whilst maintaining a constant sonification pattern (involving pitch variations during the cycle). Perturbations were produced by modifying the slope of the mapping without informing the participants. All participants adapted spontaneously their speed: they went faster if the slope decreased and slower if it increased. Higher velocities were achieved by increasing both the frequency of the movements and the perimeter of the Ellipses, but slower velocities were achieved mainly by decreasing the perimeter of the Ellipses. The shape and the orientation of the Ellipses were not significantly altered. The analysis of the speed-curvature power law parameters showed consistent modulations of the speed gain factor, while the exponent remained stable. The second experiment was performed in 3D and showed similar results, except that the main orientation of the Ellipse also varied with the changes in speed. In conclusion, this study demonstrated implicit modulation of movement speed by sonification and robust stability of the Ellipse geometry. Participants appeared to limit the decrease in movement frequency during slowing down to maintain a rhythmic and not discrete motor regimen.
Naizhang Feng - One of the best experts on this subject based on the ideXlab platform.
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MLICOM (2) - Obtaining Ellipse Common Tangent Line Equations by the Rolling Tangent Line Method
Machine Learning and Intelligent Communications, 2018Co-Authors: Naizhang Feng, Teng Jiang, Shiqi DuanAbstract:In the field of image processing and machine vision, it is sometimes necessary to obtain common tangent line equations and tangent point coordinates from Ellipses. A rolling tangent line method was proposed to obtain the 4 common tangent line equations and 8 tangent point coordinates from two Ellipses in this paper. The principle of this method is simple and it is easy to program on a computer. Use this method to process two Ellipse targets in an image and the experiment results show that the 4 common tangent equations and 8 tangent point coordinates can be obtained in high precision and the maximum execution time is less than 0.1 s.
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obtaining Ellipse common tangent line equations by the rolling tangent line method
International Conference on Machine Learning, 2017Co-Authors: Naizhang Feng, Teng Jiang, Shiqi DuanAbstract:In the field of image processing and machine vision, it is sometimes necessary to obtain common tangent line equations and tangent point coordinates from Ellipses. A rolling tangent line method was proposed to obtain the 4 common tangent line equations and 8 tangent point coordinates from two Ellipses in this paper. The principle of this method is simple and it is easy to program on a computer. Use this method to process two Ellipse targets in an image and the experiment results show that the 4 common tangent equations and 8 tangent point coordinates can be obtained in high precision and the maximum execution time is less than 0.1 s.
