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We investigate several entropy based approaches to finding cut points for discretizing continuous ECG-based risk metrics. We describe two existing approaches, Shannon entropy and asymmetric entropy, and one new approach, warped entropy. The approaches are used to find cut points for the end point of cardiovascular death for three risk metrics: heart rate variability (HRV LF-HF), morphological variability...
This paper proposes an effective background subtraction technique in still camera videos, to track objects with high degree of sensitivity, accuracy and low false detections. The method involves applying a Bayesian learning technique to update parameters of clusters formed by pixel observations at a particular spatial position. The proposed method also overcomes the limitation of having a heuristically...
This paper presents a technique for performing unsupervised clustering of satellite images using a unique 'sampling-resampling' based Bayesian learning method. The multi-band pixel values of the satellite image are expected to form a certain number of clusters. The parameters of these clusters are learnt using a Bayesian approach. This technique is unsupervised in the sense that no separate training...
Document examiners use a variety of features to analyze a given handwritten document for writer verification. The challenge in the automatic classification of a pair of documents to belong to the same or different writer, are both (i)The task of proper selection and extraction of features from the handwritten document and (ii)The use of a proper model that is capable of utilizing the true discriminatory...
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