The Experts below are selected from a list of 30231 Experts worldwide ranked by ideXlab platform
Yuan Yan Tang - One of the best experts on this subject based on the ideXlab platform.
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skeletonization of ribbon like shapes based on a new Wavelet Function
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003Co-Authors: Yuan Yan TangAbstract:A Wavelet-based scheme to extract skeleton of Ribbon-like shape is proposed in this paper, where a novel Wavelet Function plays a key role in this scheme, which possesses three significant characteristics, namely, 1) the position of the local maximum moduli of the Wavelet transform with respect to the Ribbon-like shape is independent of the gray-levels of the image. 2) When the appropriate scale of the Wavelet transform is selected, the local maximum moduli of the Wavelet transform of the Ribbon-like shape produce two new parallel contours, which are located symmetrically at two sides of the original one and have the same topological and geometric properties as that of the original shape. 3) The distance between these two parallel contours equals to the scale of the Wavelet transform, which is independent of the width of the shape. This new scheme consists of two phases: 1) Generation of Wavelet skeleton-based on the desirable properties of the new Wavelet Function, symmetry analyses of the maximum moduli of the Wavelet transform is described. Midpoints of all pairs of contour elements can be connected to generate a skeleton of the shape, which is defined as Wavelet skeleton. 2) Modification of the Wavelet skeleton. Thereafter, a set of techniques are utilized for modifying the artifacts of the primary Wavelet skeleton. The corresponding algorithm is also developed in this paper. Experimental results show that the proposed scheme is capable of extracting exactly the skeleton of the Ribbon-like shape with different width as well as different gray-levels. The skeleton representation is robust against noise and affine transformation.
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Skeletonization of Ribbon-like shapes based on a new Wavelet Function
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003Co-Authors: Yuan Yan Tang, Xinge YouAbstract:A Wavelet-based scheme to extract skeleton of Ribbon-like shape is proposed in this paper, where a novel Wavelet Function plays a key role in this scheme, which possesses three significant characteristics, namely, 1) the position of the local maximum moduli of the Wavelet transform with respect to the Ribbon-like shape is independent of the gray-levels of the image. 2) When the appropriate scale of the Wavelet transform is selected, the local maximum moduli of the Wavelet transform of the Ribbon-like shape produce two new parallel contours, which are located symmetrically at two sides of the original one and have the same topological and geometric properties as that of the original shape. 3) The distance between these two parallel contours equals to the scale of the Wavelet transform, which is independent of the width of the shape. This new scheme consists of two phases: 1) Generation of Wavelet skeleton-based on the desirable properties of the new Wavelet Function, symmetry analyses of the maximum moduli of the Wavelet transform is described. Midpoints of all pairs of contour elements can be connected to generate a skeleton of the shape, which is defined as Wavelet skeleton. 2) Modification of the Wavelet skeleton. Thereafter, a set of techniques are utilized for modifying the artifacts of the primary Wavelet skeleton. The corresponding algorithm is also developed in this paper. Experimental results show that the proposed scheme is capable of extracting exactly the skeleton of the Ribbon-like shape with different width as well as different gray-levels. The skeleton representation is robust against noise and affine transformation.
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A width-invariant property of curves based on Wavelet transform with a novel Wavelet Function
IEEE transactions on systems man and cybernetics. Part B Cybernetics : a publication of the IEEE Systems Man and Cybernetics Society, 2003Co-Authors: Lihua Yang, Ching Y. Suen, Yuan Yan TangAbstract:This paper is an improvement on the characterization of edges. Using a novel Wavelet Function, it is proven that the maximum moduli of the Wavelet transform (MMWT) of a curve produces two new symmetrical curves on both sides of the original with the same direction. The distance between the two curves is shown to be independent of the width d of the original curve if the scale s of the Wavelet transform satisfies s/spl ges/d. This property provides a novel method of obtaining the skeletons of the curves in an image.
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Wavelet Theory and Its Application to Pattern Recognition
2000Co-Authors: Yuan Yan TangAbstract:This 2nd edition is an update of the book "Wavelet Theory and Its Application to Pattern Recognition" published in 2000. Three new chapters, which are research results conducted during 2001-2008, will be added. The book consists of two parts - the first contains the basic theory of Wavelet analysis and the second includes applications of Wavelet theory to pattern recognition. The new book provides a bibliography of 170 references including the current state-of-the-art theory and applications of Wavelet analysis to pattern recognition. Continuous Wavelet Transforms Multiresolution Analysis and Wavelet Bases Some Typical Wavelet Bases Step-Edge Detection by Wavelet Transform Characterization of Dirac-Edges with Quadratic Spline Wavelet Transform Construction of New Wavelet Function and Application to Curve Analysis Skeletonization of Ribbon-like Shapes with New Wavelet Function Feature Extraction by Wavelet Sub-Patterns and Divider Dimensions Document Analysis by Reference Line Detection with 2-D Wavelet Transform Chinese Character Processing with B-Spline Wavelet Transform Classifier Design Based on Orthogonal Wavelet Series
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Skeletonization of ribbon-like shapes with new Wavelet Function
Proceedings. International Conference on Machine Learning and Cybernetics, 1Co-Authors: Xinge You, Yuan Yan Tang, Lu SunAbstract:In this paper we propose a new scheme to extract the skeleton of ribbon-like shape with a novel Wavelet Function. It consists of two phases based on these perfect properties of the new Wavelet Function; and symmetry analyses of maxima moduli of Wavelet transform are given. Midpoints of all pairs of contour elements are connected to generate a skeleton of the shape, which is defined as Wavelet skeleton. Four basic criteria for modifying the artifacts of Wavelet skeleton are presented. A corresponding algorithm is developed, and the experimental results are shown that this algorithm is capable of extracting exactly the skeleton of ribbon-like shape with different widths as well as different grey-levels.
Francisco Sepulveda - One of the best experts on this subject based on the ideXlab platform.
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Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions
Journal of Medical Systems, 2014Co-Authors: Mohammed R. Al-mulla, Francisco SepulvedaAbstract:In this research an algorithm was developed to classify muscle fatigue content from dynamic contractions, by using a genetic algorithm (GA) and a pseudo-Wavelet Function. Fatiguing dynamic contractions of the biceps brachii were recorded using Surface Electromyography (sEMG) from thirteen subjects. Labelling the signal into two classes (Fatigue and Non-Fatigue) aided in the training and testing phase. The genetic algorithm was used to develop a pseudo-Wavelet Function that can optimally decompose the sEMG signal and classify the fatigue content of the signal. The evolved pseudo Wavelet was tuned using the decomposition of 70 % of the sEMG trials. 28 independent pseudo-Wavelet evolution were run, after which the best run was selected and then tested on the remaining 30 % of the trials to measure the classification performance. Results show that the evolved pseudo-Wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.95 percentage points when compared to other standard Wavelet Functions ( p
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Novel pseudo-Wavelet Function for MMG signal extraction during dynamic fatiguing contractions.
Sensors (Basel Switzerland), 2014Co-Authors: Mohammed R. Al-mulla, Francisco SepulvedaAbstract:The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-Wavelet Function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-Wavelet Function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-Wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-Wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard Wavelet Functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05).
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Evolved pseudo-Wavelet Function to optimally decompose sEMG for automated classification of localized muscle fatigue.
Medical engineering & physics, 2011Co-Authors: Mohammed R. Al-mulla, Francisco Sepulveda, Martin ColleyAbstract:Abstract The purpose of this study was to develop an algorithm for automated muscle fatigue detection in sports related scenarios. Surface electromyography (sEMG) of the biceps muscle was recorded from ten subjects performing semi-isometric (i.e., attempted isometric) contraction until fatigue. For training and testing purposes, the signals were labelled in two classes (Non-Fatigue and Fatigue), with the labelling being determined by a fuzzy classifier using elbow angle and its standard deviation as inputs. A genetic algorithm was used for evolving a pseudo-Wavelet Function for optimising the detection of muscle fatigue on any unseen sEMG signals. Tuning of the generalised evolved pseudo-Wavelet Function was based on the decomposition of twenty sEMG trials. After completing twenty independent pseudo-Wavelet evolution runs, the best run was selected and then tested on ten previously unseen sEMG trials to measure the classification performance. Results show that an evolved pseudo-Wavelet improved the classification of muscle fatigue between 7.31% and 13.15% when compared to other Wavelet Functions, giving an average correct classification of 88.41%.
Mohammed R. Al-mulla - One of the best experts on this subject based on the ideXlab platform.
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Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions
Journal of Medical Systems, 2014Co-Authors: Mohammed R. Al-mulla, Francisco SepulvedaAbstract:In this research an algorithm was developed to classify muscle fatigue content from dynamic contractions, by using a genetic algorithm (GA) and a pseudo-Wavelet Function. Fatiguing dynamic contractions of the biceps brachii were recorded using Surface Electromyography (sEMG) from thirteen subjects. Labelling the signal into two classes (Fatigue and Non-Fatigue) aided in the training and testing phase. The genetic algorithm was used to develop a pseudo-Wavelet Function that can optimally decompose the sEMG signal and classify the fatigue content of the signal. The evolved pseudo Wavelet was tuned using the decomposition of 70 % of the sEMG trials. 28 independent pseudo-Wavelet evolution were run, after which the best run was selected and then tested on the remaining 30 % of the trials to measure the classification performance. Results show that the evolved pseudo-Wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.95 percentage points when compared to other standard Wavelet Functions ( p
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Novel pseudo-Wavelet Function for MMG signal extraction during dynamic fatiguing contractions.
Sensors (Basel Switzerland), 2014Co-Authors: Mohammed R. Al-mulla, Francisco SepulvedaAbstract:The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-Wavelet Function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-Wavelet Function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-Wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-Wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard Wavelet Functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05).
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Evolved pseudo-Wavelet Function to optimally decompose sEMG for automated classification of localized muscle fatigue.
Medical engineering & physics, 2011Co-Authors: Mohammed R. Al-mulla, Francisco Sepulveda, Martin ColleyAbstract:Abstract The purpose of this study was to develop an algorithm for automated muscle fatigue detection in sports related scenarios. Surface electromyography (sEMG) of the biceps muscle was recorded from ten subjects performing semi-isometric (i.e., attempted isometric) contraction until fatigue. For training and testing purposes, the signals were labelled in two classes (Non-Fatigue and Fatigue), with the labelling being determined by a fuzzy classifier using elbow angle and its standard deviation as inputs. A genetic algorithm was used for evolving a pseudo-Wavelet Function for optimising the detection of muscle fatigue on any unseen sEMG signals. Tuning of the generalised evolved pseudo-Wavelet Function was based on the decomposition of twenty sEMG trials. After completing twenty independent pseudo-Wavelet evolution runs, the best run was selected and then tested on ten previously unseen sEMG trials to measure the classification performance. Results show that an evolved pseudo-Wavelet improved the classification of muscle fatigue between 7.31% and 13.15% when compared to other Wavelet Functions, giving an average correct classification of 88.41%.
Xinge You - One of the best experts on this subject based on the ideXlab platform.
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Skeletonization of Ribbon-like shapes based on a new Wavelet Function
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003Co-Authors: Yuan Yan Tang, Xinge YouAbstract:A Wavelet-based scheme to extract skeleton of Ribbon-like shape is proposed in this paper, where a novel Wavelet Function plays a key role in this scheme, which possesses three significant characteristics, namely, 1) the position of the local maximum moduli of the Wavelet transform with respect to the Ribbon-like shape is independent of the gray-levels of the image. 2) When the appropriate scale of the Wavelet transform is selected, the local maximum moduli of the Wavelet transform of the Ribbon-like shape produce two new parallel contours, which are located symmetrically at two sides of the original one and have the same topological and geometric properties as that of the original shape. 3) The distance between these two parallel contours equals to the scale of the Wavelet transform, which is independent of the width of the shape. This new scheme consists of two phases: 1) Generation of Wavelet skeleton-based on the desirable properties of the new Wavelet Function, symmetry analyses of the maximum moduli of the Wavelet transform is described. Midpoints of all pairs of contour elements can be connected to generate a skeleton of the shape, which is defined as Wavelet skeleton. 2) Modification of the Wavelet skeleton. Thereafter, a set of techniques are utilized for modifying the artifacts of the primary Wavelet skeleton. The corresponding algorithm is also developed in this paper. Experimental results show that the proposed scheme is capable of extracting exactly the skeleton of the Ribbon-like shape with different width as well as different gray-levels. The skeleton representation is robust against noise and affine transformation.
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Skeletonization of ribbon-like shapes with new Wavelet Function
Proceedings. International Conference on Machine Learning and Cybernetics, 1Co-Authors: Xinge You, Yuan Yan Tang, Lu SunAbstract:In this paper we propose a new scheme to extract the skeleton of ribbon-like shape with a novel Wavelet Function. It consists of two phases based on these perfect properties of the new Wavelet Function; and symmetry analyses of maxima moduli of Wavelet transform are given. Midpoints of all pairs of contour elements are connected to generate a skeleton of the shape, which is defined as Wavelet skeleton. Four basic criteria for modifying the artifacts of Wavelet skeleton are presented. A corresponding algorithm is developed, and the experimental results are shown that this algorithm is capable of extracting exactly the skeleton of ribbon-like shape with different widths as well as different grey-levels.
Erkan Zeki Engin - One of the best experts on this subject based on the ideXlab platform.
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Selection of Optimum Mother Wavelet Function for Turkish Phonemes
International Journal of Applied Mathematics Electronics and Computers, 2019Co-Authors: Erkan Zeki Engin, Özkan ArslanAbstract:In this paper, we propose the selection of most suitable mother Wavelet Function for Turkish phonemes using discrete Wavelet transform. The determination of most similar mother Wavelet Function to the signal has been a challenge in speech processing. The optimum mother Wavelet Function for Turkish phonemes have been determined by using quantitative measures which are energy and Shannon entropy, information theoretic measures which are joint entropy, conditional entropy, mutual information, and relative entropy from Wavelet coefficients of the phonemes. In this study, 101 potential Functions were investigated to determine the most appropriate mother Wavelet. Experimental results show that the most appropriate Wavelet Functions for /c/ and /s/ phonemes which are unvoiced fricatives have been found as Bi-orthogonal 3.9 and Bi-orthogonal 5.5, respectively. By considering all the results, it is seen that the Bi-orthogonal 3.1 and Discrete Meyer Wavelet Functions are the most suitable mother Wavelets for all other phonemes.
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Wavelet Transformation Based Watermarking Technique for Human Electrocardiogram (ECG)
Journal of Medical Systems, 2005Co-Authors: Mehmet Engin, Oğuz Çıdam, Erkan Zeki EnginAbstract:Nowadays, watermarking has become a technology of choice for a broad range of multimedia copyright protection applications. Watermarks have also been used to embed prespecified data in biomedical signals. Thus, the watermarked biomedical signals being transmitted through communication are resistant to some attacks. This paper investigates discrete Wavelet transform based watermarking technique for signal integrity verification in an Electrocardiogram (ECG) coming from four ECG classes for monitoring application of cardiovascular diseases. The proposed technique is evaluated under different noisy conditions for different Wavelet Functions. Daubechies (db2) Wavelet Function based technique performs better than those of Biorthogonal (bior5.5) Wavelet Function. For the beat-to-beat applications, all performance results belonging to four ECG classes are highly moderate.
