this work presents a classification technique based on artificial immune system (AIS). the method consists of a modification of the real-valued negative selection (RNS) algorithm for patternrecognition. Our approach ...
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this paper presents a gesture recognition method for detecting and classifying both cyclic and non-cyclic human motion patterns in real-time applications. the semantic segmentation of a constantly captured human motio...
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ISBN:
(纸本)9783642140600
this paper presents a gesture recognition method for detecting and classifying both cyclic and non-cyclic human motion patterns in real-time applications. the semantic segmentation of a constantly captured human motion data stream is a key research topic, especially if both cyclic and non-cyclic gestures are considered during the human-computer interaction. the system measures the temporal coherence of the movements being captured according to its knowledge database, and once it has a sufficient level of certainty on its observation semantics the motion pattern is labeled automatically. In this way, our recognition method is also capable of handling time-varying dynamic gestures. the effectiveness of the proposed method is demonstrated via recognition experiments with a triple-axis accelerometer and a 3D tracker used by various performers.
Most of the quasi-dense matching algorithms are designed for general scene structures. In reality, there are many man-made objects in the scene, such as for example buildings and furniture which include many planar re...
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this paper presents an efficient human verification system based on vein patterns of the hand A new absorption based technique his been proposed to collect good quality images withthe help of a low cost camera and li...
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ISBN:
(纸本)9783642149214
this paper presents an efficient human verification system based on vein patterns of the hand A new absorption based technique his been proposed to collect good quality images withthe help of a low cost camera and light source the system automatically detects the region of interest from the image and does the necessary processing to extract vinous features Matching technique based on Euclidean Distance has been used for making the decision It has been tested on a data set of 1750 image samples collected from 341 individuals the accuracy of the recognition system is found to be 99 26% with FRR of 0 03%
the Gaussian mixture model (GMM) has been widely used in patternrecognition problems for clustering and probability density estimation For pattern classification however, the GMM has to consider two issues model stru...
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ISBN:
(纸本)9783642149214
the Gaussian mixture model (GMM) has been widely used in patternrecognition problems for clustering and probability density estimation For pattern classification however, the GMM has to consider two issues model structure in high-dimensional space and discriminative training for optimizing the decision boundary In this paper we propose a classification method using subspace GMM density model and discriminative training During discriminative training under the minimum classification error (MCE) criterion boththe GMM parameters and the subspace parameters are optimized discriminatively Our experimental results on the MNIST handwritten digit data and UCI datasets demonstrate the superior classification performance of the proposed method
Strong abilities of brain, in robust and intelligent processing of data are considered in many researches. Furthermore, chaotic behavior is reported both in microscopic scale (neurons) and macroscopic one (brain behav...
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the bilingual language corpus has a great effect on the performance of a statistical machine translation system. More data will lead to better performance. However, more data also increase the computational load. In t...
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the proceedings contain 118 papers. the topics discussed include: cost sensitive classification in data mining;web users access paths clustering based on possibilistic and fuzzy sets theory;on probabilistic models for...
ISBN:
(纸本)3642173128
the proceedings contain 118 papers. the topics discussed include: cost sensitive classification in data mining;web users access paths clustering based on possibilistic and fuzzy sets theory;on probabilistic models for uncertain sequential pattern mining;nearest neighbour distance matrix classification;fast retrieval of time series using a multi-resolution filter with multiple reduced spaces;fixing the threshold for effective detection of near duplicate web documents in web crawling;topic-constrained hierarchical clustering for document datasets;MSDBSCAN: multi-density scale-independent clustering algorithm based on DBSCAN;CPLDP: an efficient large dataset processing system built on cloud platform;a refinement approach to handling model misfit in semi-supervised learning;adapt the mRMR criterion for unsupervised feature selection;construction cosine radial basic function neural networks based on artificial immune networks;and spatial filter selection with LASSO for EEG classification.
the proceedings contain 118 papers. the topics discussed include: cost sensitive classification in data mining;web users access paths clustering based on possibilistic and fuzzy sets theory;on probabilistic models for...
ISBN:
(纸本)3642173152
the proceedings contain 118 papers. the topics discussed include: cost sensitive classification in data mining;web users access paths clustering based on possibilistic and fuzzy sets theory;on probabilistic models for uncertain sequential pattern mining;nearest neighbour distance matrix classification;fast retrieval of time series using a multi-resolution filter with multiple reduced spaces;fixing the threshold for effective detection of near duplicate web documents in web crawling;topic-constrained hierarchical clustering for document datasets;MSDBSCAN: multi-density scale-independent clustering algorithm based on DBSCAN;CPLDP: an efficient large dataset processing system built on cloud platform;a refinement approach to handling model misfit in semi-supervised learning;adapt the mRMR criterion for unsupervised feature selection;construction cosine radial basic function neural networks based on artificial immune networks;and spatial filter selection with LASSO for EEG classification.
Considering the diversity of symbol representation in the engineering drawings and the adaptability of the symbol recognition method, we propose the structural description and learning method of symbols and implement ...
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ISBN:
(纸本)9788988678275
Considering the diversity of symbol representation in the engineering drawings and the adaptability of the symbol recognition method, we propose the structural description and learning method of symbols and implement a real drawing recognition system embedding the self- learning mechanism. In the system, the symbol templates are automatically created by the incremental sample learning and used in the recognition. Meanwhile, different types of recognition result are used to guide the further learning work: template creating or updating. Experiments show that the recognition ability will improve incrementally without modifying the program and only a few samples are needed.
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