This paper describes a portable diagnostic telecardiology system, aimed to benefit the rural people of a third world country like India. The designed system consists of two major blocks;the first one is required to be...
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The focus of this paper is towards development of a logical framework for mining spatio-temporal sequential patterns. The spatial representation language RCC-8, often referred to as region connection calculus and its ...
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The focus of this paper is towards development of a logical framework for mining spatio-temporal sequential patterns. The spatial representation language RCC-8, often referred to as region connection calculus and its spatio-temporal extension, ST 0 , a fragment of prepositional spatio-temporal logic is used as the knowledge representation formalism. Standard abduction is used for mining sequential patterns in spatio-temporal data. Abductive reasoning may yield more than one possible answer and is accompanied by some preference criteria expressed using heuristics. Here, the abduction technique is circumscription which implements the heuristic that changes should only occur when forced to. Various additional heuristics to drive the selection of preferred explanations are discussed.
The proceedings contain 167 papers. The topics discussed include: incremental learning of object detectors using a visual shape alphabet;multiclass object recognition with sparse, localized features;unsupervised learn...
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ISBN:
(纸本)0769525970
The proceedings contain 167 papers. The topics discussed include: incremental learning of object detectors using a visual shape alphabet;multiclass object recognition with sparse, localized features;unsupervised learning of categories from sets of partially matching image features;the layout consistent random random field for recognizing and segmenting partially occluded objects;ultrasound-specific segmentation via correlation and statistical region-based active contours;principled hybrids of generative and discriminative models;a comic section classifier and its application to image datasets;learning non-metric partial similarity based on maximal margin criterion;distributed cost boosting on mis-classification cost;equivalence of non-iterative algorithms for simultaneous low rank approximations of matrices;and semi-supervised classification using liner neighborhood propagation.
The proceedings contain 151 papers. The topics discussed include: clustering appearance for scene analysis;fast compact city modeling for navigation pre-visualization;fusion of summation invariants in 3D human face re...
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ISBN:
(纸本)0769525970
The proceedings contain 151 papers. The topics discussed include: clustering appearance for scene analysis;fast compact city modeling for navigation pre-visualization;fusion of summation invariants in 3D human face recognition;deformation modeling for robust 3D face matching;locally linear models on face appearance manifolds with application to dual-subspace based classification;learning examplar-based categorization for the detection of multi-view multi-pose objects;aligning ASL for statistical translation using discriminative word model;a graph based approach for naming faces in news photos;fast human detection using a cascade of histograms of oriented gradients;real-time-hand pose recognition using low resolution depth images;automatic cast listing in feature-length films with anisotropic manifold space;and body localization in still images using hierarchical models and hybrid search.
In this paper, the usage of diverse features in detecting variability of electroencephalogram (EEG) signals was presented. The classification accuracies of modified mixture of experts (MME), which were trained on dive...
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In this paper, the usage of diverse features in detecting variability of electroencephalogram (EEG) signals was presented. The classification accuracies of modified mixture of experts (MME), which were trained on diverse features, were obtained. The wavelet coefficients and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on diverse features achieved high accuracy rates.
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