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Detecting human activities automatically in a video stream in various scenes is a challenging task. The major difficulty of this task lies in how to extract the spatial and temporal features of video sequences so that the human activities can be recognized. To tackle this problem, we propose a new classifier model using a BCM-based spiking neural network, where the auto-regulated plasticity and meta-plasticity...
Information management and extraction in the field of biomedical research has become a requirement with the rapid increase in the amount of data being published in this area. In this paper, a graphical model, Conditional Random Fields has been used to extract a particular gene-gene relationship called ??coexpression?? from the existing literature. First, a Conditional Random Fields based model has...
Pattern theorem in financial time-series is one of the most important technical analysis methods in financial prediction. Recent researches have achieved big progresses in identifying and recognizing time-series patterns. And most of the recent works on time-series deal with this task by using static approaches and mainly focus on the recognition accuracy, but considering that recognition of patterns...
Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to face recognition tasks, there is always the need to construct large image repositories from people. Images...
The recognition of wood defects is very significant for reasonable selection and scientific utilization of wood. X-ray was adopted as a measure method for wood nondestructive testing. The difference of X-ray intensity after exposure is tested in order to judge whether the wood defects exist or not. Then the defects images were processed effectively. A group of describing shape features parameters...
Neural network analysis, an important branch in data mining, has been widely used in statistical analysis, pattern recognition, image processing, biological species division and customer division. Based on division method, the paper rationally selected initial class center, dynamically regulated the number of classification during image classification, and proposed an image recognition method. In...
In this study, PCA (principal component analysis) was used to select features and eliminate the redundancy features in process of rolling bearing fault monitoring. And then a new method was mentioned out to optimize the feature space with P-PCA (parts principal component analysis), which needs to deal with the data of each fault categories with PCA firstly, and then reconstructed the feature space...
Due to rotating at high speed and operating under malcondition, the turbine-generator set rotor sometimes vibrates violently, which damages the other major components, and moreover the abnormal vibration would cause serious fault accident and economical loss. By means of condition monitoring and fault diagnosis technique, a novel approach using wavelet neural network is brought forward to transient...
ldquoSoundscapesrdquo are maps that depict the sound content of an area at a time interval. Sound features encapsulate information which can be combined with the visual features of a landscape, in order to produce useful ecological observations/data, for areas of environmental or ecological interest. These include monitoring of the wildlife, the inhabitation and the use/human activities of the area,...
Gabor texture descriptor have gained much attention for different aspects of computer vision and pattern recognition. Recently, on the Rayleigh nature of Gabor filter out-puts Rayleigh model Gabor texture descriptor is proposed.In this paper, we investigate the performance of these two Gabor texture descriptor in texture classification. We built a texture classification system based on BPNN, and use...
We present in this paper a new regression method adapted to problems dealing with a huge set of potential features like in pattern recognition. This method combines a boosted forward feature selection algorithm and a generalized regression neural network. The feature selection uses a new criterion, the fuzzy functional criterion, to evaluate the relevance of each feature. It is well suited to measure...
Independent component analysis (ICA) is a powerful tool for redundancy reduction and nonGaussian data analysis. Artificial neural network (ANN), especially the self-organizing map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, we proposed a novel multi-layer neural network for pattern classification. First, two...
Locally linear embedding (LLE) is a prevalent manifold learning method in pattern recognition and machine learning. It preserves the intrinsic structure information of data set and has been widely applied to feature extraction and dimensionality reduction. This paper introduces LLE to aircraft pose recognition. The representative motion poses of an aircraft in the air are analyzed. Unfolding results...
The important meaning of the optical fiber fusion defect recognition was introduced based on ISO14000. Detecting the optical fiber fusion point by using the UltraPAC system, aiming at the defect feature, the method of analyzing and extracting the defect eigenvalue by using wavelet packet analysis and pattern recognition by making use of the wavelet neural network is discussed. This method can realize...
This paper proposes a model of human recognition that simulates a human's continuous change in observation from rough overall check to fine detailed check. It has been applied to banknote recognition. Banknote classification for twenty eight samples has demonstrated that the proposed method enables to identify not only the kind of a banknote but also its degree of weariness.
After analyzing flexibility material processing (such as quilting processing) influencing factor of pattern deformation, the edges of the original image and the deformation image are extracted. Then they are changed into coordinate. On top of it, the data are put into the Elman neural networks to train which has been built and the original image is used to as the teacher signal to tutoring. At last,...
Data classification has been studied widely in the fields of Artificial Intelligence, Machine Learning, Data Mining and Pattern Recognition. Up to the present, the development of classification has made great achievements, and many kinds of classified technology and theory will continue to emerge. This paper discusses a great deal of classification algorithms based on the Artificial Neural Networks,...
The discrimination of asymptomatic chronic alcoholics from non-alcoholics using the brain activity patterns is studied in this paper. Detection of the abnormalities in the cognitive processing ability of chronic alcoholics is essential for their rehabilitation, and also in screening them for certain jobs. The brain patterns evoked in response to visual stimuli, known as visual evoked potentials (VEP),...
Natural scene categorization is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Thousands of images are generated every day, which implies the necessity to classify, organize and access them using an easy, faster and efficient way. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways...
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