Facial expression recognition can be divided into three steps: face detection, expression feature extraction and expression categorization. Facial expression feature extraction and categorization are the most key issu...
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ISBN:
(纸本)9780769531199
Facial expression recognition can be divided into three steps: face detection, expression feature extraction and expression categorization. Facial expression feature extraction and categorization are the most key issue. To address this issue, we propose a method to combine local binary pattern (LBP) and embedded hidden markov model (EHMM), which is the key contribution of this paper This paper first gives an introduction about facial expression recognition and then describes EHMM and LBP Finally, we give out the LBP-EHMM method in facial expression recognition, and perform an experiment to obtain a comparison between LBP feature and discrete cosine transform (DCT) feature.
This paper describes how the similarities and differences among similar objects can be discovered during learning to facilitate recognition. The application domain is single views of flying model aircraft captured in ...
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ISBN:
(纸本)0819418455
This paper describes how the similarities and differences among similar objects can be discovered during learning to facilitate recognition. The application domain is single views of flying model aircraft captured in silhouette by a CCD camera. The approach was motivated by human psychovisual and monkey neurophysiological data. The implementation uses neural net processing mechanisms to build a hierarchy that relates similar objects to superordinate classes, while simultaneously discovering the salient differences between objects within a class. learning and recognition experiments both with and without the class similarity and difference learning show the effectiveness of the approach on this visual data. To test the approach, the hierarchical approach was compared to a non-hierarchical approach, and was found to improve the average percentage of correctly classified views from 77% to 84%.
This paper presents an analytical performance prediction model and methodology that can be used to predict the execution time, speedup, scalability and similar performance metrics of a targe set of imageprocessing op...
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This paper presents an analytical performance prediction model and methodology that can be used to predict the execution time, speedup, scalability and similar performance metrics of a targe set of imageprocessing operations running on a p-processor parallel system. The model which requires only a few parameters obtainable on a minimal system can help in the systematic design, evaluation and performance tuning of parallel imageprocessing systems. Using the model one can reason about the performance of a parallel imageprocessing system prior to implementation. The method can also support programmers in detecting critical parts of an implementation and system designers in predicting hardware performance and the effect of hardware parameter changes on performance. The execution of parallel imageprocessing operations was studied and operations were arranged in three main problem classes based on data locality and the communication patterns of the algorithms. The core of the method is the derivation of the overhead function, as it is the overhead that determines the achievable speedup. The overheads were examined and modelled for each class. The use of the method is illustrated by four class-representative imageprocessing algorithms: image-scalar addition, convolution, histogram calculation and the Fast Fourier Transform. The developed performance model has been validated on a 16-node parallel machine and it has been shown that the model is able to predict the parallel run-time and other performance metrics of parallel imageprocessing operations accurately.
One of the most critical steps in patternrecognition systems is to extract the most distinctive features of the image. Therefore, the accuracy of these systems is strongly related to the efficiency of this step to re...
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This paper aims that analysing neural network method in patternrecognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The pr...
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ISBN:
(纸本)9783642240362;9783642240379
This paper aims that analysing neural network method in patternrecognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Feature recognition Neural Network model for patternrecognition. The primary function of which is to retrieve in a patternstored in memory, when an incomplete or noisy version of that pattern is presented. An associative memory is a storehouse of associated patterns that are encoded in some form. In auto-association, an input pattern is associated with itself and the states of input and output units coincide. When the storehouse is incited with a given distorted or partial pattern, the associated pattern pair stored in its perfect form is recalled. patternrecognition techniques are associated a symbolic identity with the image of the pattern. This problem of replication of patterns by machines (computers) involves the machine printed patterns. There is no idle memory containing data and programmed, but each neuron is programmed and continuously active.
In this paper our main focus is to discover different machinelearning techniques that are useful biometric System. As biometric authentication system is a combination of both imageprocessing and patternrecognition,...
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The application of neural network technology for patternrecognition and classification in the field of machine intelligence requires advanced computational procedures. Radial Basis Function Networks (RBFN) have been ...
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The application of neural network technology for patternrecognition and classification in the field of machine intelligence requires advanced computational procedures. Radial Basis Function Networks (RBFN) have been getting more attention recently in neural network design for classification purpose. We have devised a procedure to determine the optimal spread of the radial basis functions for performance improvement. We are comparing the results of the radial basis function approach with those of the backpropagation approach for performances on speed and generalization.
Deep learning is a thing of tomorrow which is causing a complete drift from shallow architecture to deep architecture and an estimate shows that by 2017 about 10 % of computers will be learning rather than processing....
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ISBN:
(纸本)9789811016752;9789811016745
Deep learning is a thing of tomorrow which is causing a complete drift from shallow architecture to deep architecture and an estimate shows that by 2017 about 10 % of computers will be learning rather than processing. Deep learning has fast growing effects in the area of patternrecognition, computer vision, speech recognition, feature extraction, language processing, bioinformatics, and statistical classification. To make a system learn, deep learning makes use of a wide horizon of machinelearning algorithms. Gene expression data is uncertain and imprecise. In this paper, we discuss supervised and unsupervised algorithms applied to gene expression dataset. There are intermediate algorithms classified as semi-supervised and self taught which also play an important role to improve the prediction accuracy in diagnosis of cancer. We discuss deep learning algorithms which provide better analysis of hidden patterns in the dataset, thus improving the prediction accuracy.
imagerecognition in smart systems and internet of things applications is rapidly developing. Significant advances in mobile computing technology and machinelearning are expanding horizons to use imagerecognition in...
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Manual sorting/grading of oranges is done at wholesale markets/food processing factories based upon its maturity, size, quality and breeds. With an aim to replace the manual sorting system, this paper proposes the res...
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Manual sorting/grading of oranges is done at wholesale markets/food processing factories based upon its maturity, size, quality and breeds. With an aim to replace the manual sorting system, this paper proposes the research work for automated grading of Oranges using patternrecognition techniques applied on a single color image of the fruit. This research is carried out on 160 Orange fruits collected from varied geographical locations in Vidarbha Region of Maharashtra. System designed can automatically classify an Orange fruit from this region, given its single color image of 640 x 480 pixel resolution, taken inside a special box designed with 430 lux intensity light inside it, by a digital camera. Only 4 features are used to classify oranges into 4 different classes according to the maturity level and 3 different classes as per size of oranges. In this paper two novel techniques based on patternrecognition are proposed Edited Multi Seed Nearest Neighbor Technique and Linear Regression based technique;although Nearest Neighbor Prototype technique is also deployed. Linear Regression based technique can explicitly predict the maturity of the unknown orange fruit, enabling classification into multiple classes with desired lifespan. Experimental results indicate success rate up to 90 % and 98 %. (C) 2016 The Authors. Published by Elsevier B.V.
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