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1st international Congress on image and Signal processing

作者： Cao, Jianqiang Tong, Can Southeast Univ Res Ctr Learning Sci Nanjing 210096 Peoples R China

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.

关键词： Local binary pattern

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learning TO DIstINGUISH SIMILAR OBJECTS

LEARNING TO DISTINGUISH SIMILAR OBJECTS

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1st international conference on Applications and Science of Artificial Neural Networks

作者： SEIBERT, M WAXMAN, A GOVE, A MIT LINCOLN LABMACHINE INTELLIGENCE TECHNOL GRPLEXINGTONMA 02173

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%.

关键词： Optical spheres Tin image processing image segmentation Visualization Composites CCD cameras Feature extraction Neural networks Sensors

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An analytical method for predicting the performance of parallel image processing operations

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JOURNAL OF SUPERCOMPUTING 1998年第1-2期12卷 157-174页

作者： Juhasz, Z Univ Veszprem Dept Informat Syst H-8201 Veszprem Hungary

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 image processing 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 image processing systems. Using the model one can reason about the performance of a parallel image processing 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 image processing 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 image processing 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 image processing operations accurately.

关键词： performance prediction image processing analytical model overheads message-passing communication pattern

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Does the learning principle help us improve hand-crafted finger-knuckle-print features? 1

Does the learning principle help us improve hand-crafted fin...

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1st international conference on Communications, Control Systems and Signal processing, CCSSP 2020

作者： Haouam, Mohamed Yassine Meraoumia, Abdallah Bendib, Issam Laimeche, Lakhdar Algeria

ISBN: (纸本)9781728158358

One of the most critical steps in pattern recognition 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 represent the image. In general, hand-craft image features are obtained using feature extraction methods that often have no prior knowledge about the image type, which may result in poor features. In this paper, a hand-craft image feature learning method, based on Local Binary pattern (LBP), for Finger-Knuckle-Print representation and recognition is applied. Thus, in the learning phase, each training finger image is first divided into several non-overlapping regions, and the feature filter and the codebook of each region are individually learned. For the enrolment phase, the obtained learned filter and codebook are applied to each image to extract the histogram feature of each region. Then, the different histograms are concatenated in a single feature vector to represent the finger features. Finally, an SVM classifier is used for the identification phase. The experimental results clearly show that the learning principle offers a good gain in the system accuracy, which can provide an excellent identification rate and thus improve security. © 2020 IEEE.

关键词： Feature extraction

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Analysis of Feature recognition of Neural Network Method in the string recognition

Analysis of Feature Recognition of Neural Network Method in ...

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1st international conference on Computer Science, Engineering and Information Technology (CCSEIT 2011)

作者： Gupta, Amit Kumar Singh, Yash Pal KIET MCA Dept Ghaziabad UP India BIET CSE Dept Jhansi Uttar Pradesh India

ISBN: (纸本)9783642240362;9783642240379

This paper aims that analysing neural network method in pattern recognition. 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 pattern recognition. The primary function of which is to retrieve in a pattern stored 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. pattern recognition 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.

关键词： Neural network machine printed string pattern recognition

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machine learning Methods for Extraction and Classification for Biometric Authentication 1st

Machine Learning Methods for Extraction and Classification f...

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1st international conference on Data Science, machine learning and Applications, 2019

作者： Gunjan, Vinit Kumar Prasad, Puja S. Pathak, Rashmi Kumar, Amit Department of CSE CMR Institute of Technology Hyderabad India Department of CSE Geethanjali College of Engineering and Technology Hyderabad India Department of CSE Siddhant College of Engineering Pune India Department of CSE SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789811514197

In this paper our main focus is to discover different machine learning techniques that are useful biometric System. As biometric authentication system is a combination of both image processing and pattern recognition, in this classification of pattern is a difficult task. machine learning have number of algorithm that makes classification task easy. machine learning is divided as supervised as well as unsupervised learning. In unsupervised learning the machine construct representation of input by getting inputs x1, x2, x3, …, and this is used for decision making, predicting future inputs or we say unsupervised learning finds patterns in the data and mainly solve clustering problem. In supervised learning set of output is already given, only we have to find this set of output from respective input value. In this paper we also discuss the area of machine learning where already work has done for biometric. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Unsupervised learning

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New approaches of radial basis function network for pattern classifiers

New approaches of radial basis function network for pattern ...

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4th international conference on Computer Science and Informatics, JCIS 1998, 1st international Workshop on High Performance, 1st international Workshop on Computer Vision, pattern recognition and image processing Volume 4

作者： Baghavan, S. Li, R. Lebby, G. Sherrod, E. NASA ACE Center Dept. of Electrical Engineering N.C. A and T State University Greensboro NC 27411

The application of neural network technology for pattern recognition 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.

关键词： Radial basis function networks

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Progressive Review Towards Deep learning Techniques 1st

Progressive Review Towards Deep Learning Techniques

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1st international conference on Data Engineering and Communication Technology (ICDECT)

作者： Chaudhari, Poonam Agarwal, Himanshu Symbiosis Inst Technol Pune Maharashtra India

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 pattern recognition, 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 machine learning 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.

关键词： Deep learning Supervised and unsupervised algorithm Semi-supervised and self-taught algorithm Gene expression data

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Genetic Algorithm for image recognition in Smart Systems Applications 1

Genetic Algorithm for Image Recognition in Smart Systems App...

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1st IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-stA 2021

作者： Jannoud, Ismael Masoud, Mohammad Jaradat, Yousef Zerek, Amer Al-Zaytoonah University of Jordan Faculty of Engineering and Technology Amman Jordan University of Zawia Dept. of Electrical and Electronic Engineering Tripolli Libya

ISBN: (纸本)9781665418560

image recognition in smart systems and internet of things applications is rapidly developing. Significant advances in mobile computing technology and machine learning are expanding horizons to use image recognition in smart systems and IoT applications. This paper use the smart system based image matching system utilizing genetic algorithm to design an integrated service to improve the management of image recognition. An efficient genetic algorithm used to match an unknown pattern which represents a partial image with original image. This technique uses a parameter, namely, chromosome. It transformed the spatial coordinates position into a sequence of bits of small length depending on the dimension of the original image, whereby the matching will be obtained in a short time. The system was tested on a sample images showed high matching rate (97%). © 2021 IEEE.

关键词： Chromosomes

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Orange Sorting by Applying pattern recognition on Colour image 1

Orange Sorting by Applying Pattern Recognition on Colour Ima...

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1st international conference on Information Security and Privacy

作者： Jhawar, Jyoti JD Coll Engn & Management Nagpur Maharashtra India

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 pattern recognition 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 pattern recognition 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.

关键词： Orange Sorting pattern recognition Color image processing

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