1,720,978 research outputs found
2D hierarchical fuzzy clustering using kernel-based membership functions
No full text2D clustering aims at solving problems concerning bi-dimensional datasets in several application fields, such as medical imaging, image retrieval, computer vision and so on. A novel approach for 2D hierarchical fuzzy clustering is proposed, which relies on the use of kernel-based membership functions. This new metric allows to obtain unconstrained structures for data modelling. The performed tests show that the proposed approach can overcome well-known hierarchical clustering algorithms against different benchmarks, also having the chance to be deployed on parallel computing architectures.2D clustering aims at solving problems concerning bi-dimensional datasets in several application fields, such as medical imaging, image retrieval, computer vision and so on. A novel approach for 2D hierarchical fuzzy clustering is proposed, which relies on the use of kernel-based membership functions. This new metric allows to obtain unconstrained structures for data modelling. The performed tests show that the proposed approach can overcome well-known hierarchical clustering algorithms against different benchmarks, also having the chance to be deployed on parallel computing architectures
FUZZY CLUSTERING PER L’ANALISI DI DATI TRAMITE MODELLI GEOMETRICI NON VINCOLATI E FUNZIONI DI APPARTENENZA BASATE SU KERNEL
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An accurate algorithm for the identification of fingertips using an RGB-D camera
No full textRGB-D cameras and depth sensors have made possible the development of an uncountable number of applications in the field of human-computer interactions. Such applications, varying from gaming to medical, have made possible because of the capability of such sensors of elaborating depth maps of the placed ambient. In this context, aiming to realize a sound basis for future applications relevant to the movement and to the pose of hands, we propose a new approach to recognize fingertips and to identify their position by means of the Microsoft Kinect technology. The experimental results exhibit a really good identification rate, an execution speed faster than the frame rate with no meaningful latencies, thus allowing the use of the proposed system in real time applications. Furthermore, the scored identification accuracy confirms the excellent capability of following also little movements of the hand and it encourages the real possibility of successive implementations in more complex gesture recognition systems. © 2011 IEEE
TECNICHE DI PATTERN RECOGNITION PER L’ELABORAZIONE DI DATI PROVENIENTI DA SENSORI INERZIALI (IMU)
No full textShapes classification of dust deposition using fuzzy kernel-based approaches
No full textDust deposition and pollution are relevant issues in indoor environments, especially concerning human health and conservation of things and works. In this framework, several tools have been proposed in the last years in order to analyze dust deposition and extract useful information for addressing the phenomenon. In this paper, a novel approach for dust analysis and classification is proposed, employing machine learning and fuzzy logic to set up a simple and actual tool. The proposed approach is tested and compared with other already introduced similar techniques, in order to evaluate its performance.Dust deposition and pollution are relevant issues in indoor environments, especially concerning human health and conservation of things and works. In this framework, several tools have been proposed in the last years in order to analyze dust deposition and extract useful information for addressing the phenomenon. In this paper, a novel approach for dust analysis and classification is proposed, employing machine learning and fuzzy logic to set up a simple and actual tool. The proposed approach is tested and compared with other already introduced similar techniques, in order to evaluate its performance
A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography
No full textClinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients’ impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery. The correlation between stroke-induced motor impairment and sEMG features on both time and frequency domain is investigated, and a specifically designed fuzzy kernel classifier based on geometrically unconstrained membership function is introduced in the study to tackle the challenges in discriminating data classes with complex separating surfaces. Experiments using sEMG data collected from stroke patients have been carried out to examine the validity and feasibility of the proposed method. In order to ensure the generalization capability of the classifier, a cross-validation test has been performed. The results, verified using the evaluation decisions provided by an expert panel, have reached a rate of success of the 92.47%. The proposed fuzzy classifier is also compared with other pattern recognition techniques to demonstrate its superior performance in this application
TECNICHE DI INTELLIGENZA COMPUTAZIONALE PER RILEVAZIONE E ANALISI DI DEPOSITI DI POLVERE IN AMBIENTI MUSEALI
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TECNICHE DI FUZZY PATTERN RECOGNITION PER L’ELABORAZIONE DI DATI PROVENIENTI DA ELETTROMIOGRAFIA DI SUPERFICIE (sEMG)
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