This paper presents a supervised classification method of sonar image, which takes advantages of both multi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet d...This paper presents a supervised classification method of sonar image, which takes advantages of both multi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is obtained. In the part of classifier construction, the Learning Vector Quantization (LVQ) network is adopted as a classifier. Experiments of sonar image classification were carried out with satisfactory results, which verify the effectiveness of this method.展开更多
Muscle injuries remain one of the most common injuries in sport,yet despite this,there is little consensus on how to either effectively describe or determine the prognosis of a specific muscle injury.Numerous approach...Muscle injuries remain one of the most common injuries in sport,yet despite this,there is little consensus on how to either effectively describe or determine the prognosis of a specific muscle injury.Numerous approaches to muscle classification and grading of medicine have been applied over the last century,but over the last decade the limitations of historic approaches have been recognized.As a consequence,in the past 10 years,clinical research groups have begun to question the historic approaches and reconsider the way muscle injuries are classified and described.Using a narrative approach,this manuscript describes several of the most recent attempts to classify and grade muscle injuries and highlights the relative strengths and weaknesses of each system.While each of the new classification and grading systems have strengths,there remains little consensus on a system that is both comprehensive and evidence based.Few of the currently identified features within the grading systems have relevance to accurately determining prognosis.展开更多
文摘This paper presents a supervised classification method of sonar image, which takes advantages of both multi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is obtained. In the part of classifier construction, the Learning Vector Quantization (LVQ) network is adopted as a classifier. Experiments of sonar image classification were carried out with satisfactory results, which verify the effectiveness of this method.
文摘Muscle injuries remain one of the most common injuries in sport,yet despite this,there is little consensus on how to either effectively describe or determine the prognosis of a specific muscle injury.Numerous approaches to muscle classification and grading of medicine have been applied over the last century,but over the last decade the limitations of historic approaches have been recognized.As a consequence,in the past 10 years,clinical research groups have begun to question the historic approaches and reconsider the way muscle injuries are classified and described.Using a narrative approach,this manuscript describes several of the most recent attempts to classify and grade muscle injuries and highlights the relative strengths and weaknesses of each system.While each of the new classification and grading systems have strengths,there remains little consensus on a system that is both comprehensive and evidence based.Few of the currently identified features within the grading systems have relevance to accurately determining prognosis.
