278,841 research outputs found

    Selecting features for object detection using an AdaBoost-compatible evaluation function

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    This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates

    Exercise Training in Duchenne Muscular Dystrophy: A Systematic Review and Meta-Analysis

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    Objective: To evaluate the effects and safety of exercise training, and to determine the most effective exercise intervention for people with Duchenne muscular dystrophy. Exercise training was compared with no training, placebo or alternative exercise training. Primary outcomes were functioning and health-related quality of life. Secondary outcomes were muscular strength, endurance and lung function. Data sources: A systematic literature search was conducted in Medline, EMBASE, CINAHL, Cochrane Central, PEDro and Scopus. Study selection and data extraction: Screening, data extraction, risk of bias and quality assessment were carried out. Risk of bias was assessed using the Cochrane Collaborations risk of bias tools. The certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. Data synthesis: Twelve studies with 282 participants were included. A narrative synthesis showed limited or no improvements in functioning compared with controls. Health-related quality of life was assessed in only 1 study. A meta-analysis showed a significant difference in muscular strength and endurance in favour of exercise training compared with no training and placebo. However, the certainty of evidence was very low. Conclusion: Exercise training may be beneficial in Duchenne muscular dystrophy, but the evidence remains uncertain. Further research is needed on exercise training to promote functioning and health-related quality of life in Duchenne muscular dystrophy.publishedVersio

    The evidence-base for conceptual approaches and additional therapies targeting lower limb function in children with cerebral palsy : a systematic review using the International Classification of Functioning, Disability and Health as a framework

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    Objective: This systematic review provides an overview of the effectiveness of conceptual approaches and additional therapies used in lower limb physical therapy of children with cerebral palsy and supports the development of clinical guidelines. Data sources and study selection: A literature search in 5 electronic databases was performed, extracting literature published between 1995 and 2009. Studies were evaluated using the framework recommended by the American Academy for Cerebral Palsy and Developmental Medicine (AACPDM), which classifies outcomes according to the International Classification of Functioning, Disability and Health (ICF). Data extraction: Three evaluators rated the strength of evidence of the effects according to the AACPDM levels of evidence classification, and the quality of the studies according to the AACPDM conduct score system. Data synthesis: A total of 37 studies used conceptual approaches (neurodevelopmental treatment (NDT), conductive education, Vojta therapy, sensory integration, functional training and goal-oriented therapy) and 21 studies focused on additional therapies (aquatic therapy and therapeutic horse-riding). Conclusion: Level II evidence was found for the effectiveness of therapeutic horse-riding on posture and for NDT and functional training on gross motor function. Goal-oriented therapy and functional training were effective on the attainment of functional goals and participation. With level IV evidence, NDT was effective on all levels of the ICF

    Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

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    In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient
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