Multi-class SVMs with the probabilistic output ( MP-SVMs ) are important techniques in pattern recognition . Two key challenges for efficient GPU accelerations for MP-SVM are : ( i ) many kernel values are repeatedly computed as a binary SVM classifier is trained iteratively , resulting in repeated accesses to the high latency GPU memory ; ( ii ) performing training or estimating probability in parallel requires a much larger memory footprint than the GPU memory . Feature Subset Selection is a well known task in the Machine Learn-ing , Data Mining , Pattern Recognition and Text Learning paradigms . In this chapter , we present a set of Estimation of Distribution Algorihtms ( EDAs ) inspired techniques to tackle the Feature Subset Selection problem in Machine Learning and Data Mining tasks . In pattern recognition literature , it is well known that a finite number of training samples cause practical difficulties in designing a classifier . Moreover , the generalization error of the classifier tends to increase as the number of features gets large . This correspondence calls attention to several frequently used assumptions and techniques culled from the pattern recognition literature . Cluster analysis or " unsupervised " classification ( from " unsupervised learning " , in pattern recognition literature ) usually concerns a set of exploratory multivariate data analysis methods and techniques for grouping either statistical data units or variables into groups of similar elements , that is finding a clustering structure in the data . Classical clustering methods usually work with a set of objects as statistical data units described by a set of homogeneous ( that is , of the same type ) variables in a two-way framework . The proposed technique is the implementation of a parallel comparison of data with respect to various pre-stored categories . We design smart pulse-based stochastic-logic blocks to provide an efficient pattern recognition analysis . The proposed architecture can speed-up the screening process of huge databases by two orders of magnitude with respect classical software-based solutions , thus implying a great improvement in terms of total performance ( speed and power dissipation ) . The same procedure is repeated for the construction of inner iris boundary . The circular iris region can be un-wrapped into rectangular form for the purpose of pattern recognition analysis . The iris localization was conducted on iris images taken from healthy women free from Human Papilloma Virus ( HPV ) . vulgaris ) samples and their antioxidant potential ( IC50 ) by using powerful chemometric tools , such as Wald-Wolfowitz run test , cluster analysis and sum of ranking differences , in order to obtain groupings of the samples which share similar properties or to test if the samples come from the same population . Besides the aforementioned classification or pattern recognition analysis , the regression methods ( linear , multiple linear and artificial neural network regressions ) were carried out in order to establish reliable linear and non-linear relationships between IC50 and contents of betalains , phenolic compounds and dry matter . The results reveal certain influence of foil cover , used during the cultivation , on beetroot antioxidant potential and content of the determined betalains and phenolic compounds , as well as the strong non-linear relationship between the analyzed variables . It includes such measures as a set monotonicity , complexness , repetition , relative dimensionality , relative dependence approximation simplicity , relative inconsistency , evenness , class separability and compactness , integrated criteria of training set quality evaluation , sample and feature selection criteria . The using of offered criterions in practice allows to automatize the process of a construction , analysis and comparison of neural models for pattern recognition problem solving . Then a BP neural network is established in which the time factor is considered based on genetic algorithm . The characteristic parameters are used as input specimen , learning and training the network to complete the pattern recognition of model working state . Experiments show that the method can quickly and reliably discriminate the conditions of the punch die and has strong practicability . c ) Technologies related to learning Technologies for adapting to similar tasks and using past experience must be developed . d ) Technologies related to pattern recognition Pattern recognition for spatial and temporal sensory information must be developed . e ) Others Programming languages must be developed which validate input , movement description , parallel movement control , use environmental models , graphic and speech understanding , and effective human-computer interfaces . Significant performance gain has been observed , especially in the presence of large amount of training data , when deep learning techniques are used for image classification and speech recognition . On the one hand , hierarchical and recursive networks have demonstrated great promise in automatically learning thousands or even millions of features for pattern recognition ; on the other hand concerns about deep learning have been raised and many fundamental questions remain open . Some potential problems with the current DL frameworks include : reduced transparency and discriminativeness of the features learned at hidden layers ; training difficulty due to exploding and vanishing gradients ; lack of a thorough mathematical understanding about the algorithmic behavior , despite of some attempts made on the theoretical side ; dependence on the availability of large amount of training data ; complexity of manual tuning during training . An Internet of Things Application . This paper presents an IoT-enabled server device which is capable of authenticating a person by fingerprint recognition and grant access to the client application for making the attendance logging possible with the increased level of security . The ability of the device is to provide precise matching of fingerprints increases the possibility of error-free attendance logging . Fingerprint image segmentation is part of preprocessing in fingerprint image recognition system . It has a critical effect to the fingerprint image recognition system . A combined method segmentation method is proposed in this article . However , these systems still encounter difficulties due to various noise factors present in operating environments . The purpose of this article is to give an overview of fingerprint-based recognition and discuss In addition , papers by Ao Liu , Minrong Lu , Jin Zhang , and Fan Wu focused on structural optimization of neural network models . Moreover , papers by Xiaofeng Zou , Minrong Lu , Xiaoyong Tang , Titinunt Kitrungrotsakul , and Fan Wu focused on performance optimization of neural network models and their applications in various fields , such as monetary policy prediction , bioinformatics , image classification , pedestrian re-identification , fingerprint pattern recognition , and human personality classification . The paper by Zheng Xiao et al. focused on task scheduling and virtual machine ( VM ) allocation in distributed computing environments and described a workload-driven coordination mechanism between virtual machine allocation and task scheduling . A contactless type of a five fingerprint image capturing apparatus is provided to solve user 's aversion , the distortion of a fingerprint , and the deterioration of fingerprint recognizing performance by obtaining the front and both-side image the five fingerprint of a user with an attempt by using a single photographing device with a pair of reflecting units or a pair of optical refracting units and a rotating function or using a pair of reflecting units or a pair of optical refracting units . A contactless type of a five fingerprint image capturing apparatus comprises the followings : 5 five finger supports(20 ) ; a pair of reflecting units or optical refracting units which is installed at an inclination angle capable of radiating the fingerprint area of both sides in both sides of fingers supported by each finger support ; a camera(40 ) which obtains a five fingerprint image by photographing all areas of a finger print including a fingerprint area of a front direction corresponding to an optical axis and a fingerprint area of both sides not corresponding to the optical axis for each finger while moving to the lower part of the finger supported by the each finger support according as a rotating unit rotates in a state that the camera is fixed in a side of the rotating unit ; a light device(50 ) which is installed in the front end of the camera and radiates light to the fingerprint area of a user ; a diffuser(60 ) ; an operation switch(70 ) ; and a housing(10 ) . In this paper , the research of fingerprint automatic recognition with micro-computer-based image processing system is introduced . Method of fingerprint image preprocessing , fingerprint recognition and classification fingerprint database establishment and verification are presented . However , the parallelization of existing applications has very frequently proved to be a cumbersome task and in many cases the parallel application is slower than the original serial one . This work demonstrates the parallelization of one high-end bioinformatics application ( multiple sequence alignment for amino acids or nucleotide sequences “ MAFFT ” ) as well as a novel security application ( fingerprinting recognition “ NBIS ” ) on a highly parallel , yet a very low cost , system . We initially demonstrate the method for parallelizing the applications and then we focus on the end performance . With the development of information science and technology , people awaremore about social public safe and personal informa-tion ’s safe . First , it is described that the development and research state of thefingerprint recognition in chapter one . The biometric identifica-tion technology and the fingerprint recognition technology are translated in concept , and the principles and features of fingerprint recogni-tion are given obviously too . Artificial bee colony ( ABC ) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source . In this paper , we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems . Finally , the proposed approach is tested on several pattern recognition problems . This paper deals with the optimal use of a sequence of prior observations in order to recognize patterns . The class of pattern recognition problems which we treat is completely general in that the results may be applied to patterns of a visual , aural or electromagnetic origin . We show that the use of prior observations for pattern recognition may be described as a process of learning the statistical characteristics of the patterns involved . ABSTRACT This paper describes some relations between stochastic automata and stochastic languages , and the possible application of stochastic languages to pattern recognition problems . Stochastic finite-state and stochastic context-free languages are defined . The approach makes few assumptions about the target pattern class and should therefore be fairly general , as long as the target class has predictable image boundaries . We show that this is indeed the case by demonstrating the technique on two other pattern detection/recognition problems . Because our object and pattern detection approach is very much learning-based , how well a system eventually performs depends heavily on the quality of training examples it receives . The recommendations based on the natural computation for the pattern recognition problem were described . The main work purpose included analysis a natural computing and the search for the pattern recognition problem solution by new features . Natural computing model types description and the trends of the virtual environments applications of the problems solving were considered . Previous studies have proven that imitating the mechanism of recognizing alien cells is beneficial and provides so many solutions to the pattern recognition related problems . These efforts emulate the human immune system in recognizing the cells by considering every essential component or features of the subjects . For the vector classification problem considered ( circle in the square ) , this algorithm trains 20 times faster than backprop . For the pattern recognition type problems considered , the implementation in parallel architecture of this algorithm would train in half the time of the implementation in parallel architecture of standard backpropagation . Such an algorithm would be useful in time-critical applications where there is a steady stream of training data at a fixed rate . Class imbalance may reduce the classifier performance in several recognition pattern problems . Such negative effect is more notable with least represented class ( minority class ) Patterns . To answer that question , a rigorous mathematical approach has been developed and implemented as computer software . Our preliminary results indicate that the conceptual approach that has been developed can be used to answer this question , and suggest that memristor networks are capable of real-time pattern recognition . Conventional image editing software in combination with other techniques are not only difficult to apply to an image but also permits a user to perform some basic functions one at a time . However , image processing algorithms and photogrammetric systems are developed in the recent past for real-time pattern recognition applications . A graphical user interface ( GUI ) is developed which can perform multiple functions simultaneously for the analysis and estimation of geometric distortion in an image with reference to the corresponding distorted image . Real time pattern recognition applications often deal with high dimensional data , which require a data reduction step which is only performed offline . However , this loses the possibility of adaption to a changing environment . A bagging ensemble consists of a set of classifiers trained independently and combined by a majority vote . Such a combination improves generalization performance but can require large amounts of memory and computation , a serious drawback for addressing portable real-time pattern recognition applications . We report here a compact three-dimensional ( 3D ) multiprecision very large-scale integration ( VLSI ) implementation of a bagging ensemble . The proposed formal neuron has a regular circuit pattern as it consists mostly of memory hardware . Thus a wafer scale implementation of a network composed of such neurons may provide a cost effective way to solve many real-time pattern recognition problems . > The feature and classifier selection is one of the most challenging tasks in the field of Pattern Recognition . As the performance of 400 dimensional feature is already established in numeral recognition field , for the present work we have focused on performance evaluation of classifiers in handling complex real time Pattern Recognition problems like Numeral Recognition . Here we have selected Support Vector Machine ( SVM ) , Library for Large Linear ( LIBLINEAR ) , Multilayer Perceptron ( MLP ) , Fuzzy Un-ordered Rule Induction Algorithm ( FURIA ) , Modified Quadratic Discriminant Function ( MQDF ) as the classifiers for recognition of the numerals and comparison of the results . The efficiency of the proposed algorithm is shown by specific examples . The paper studies modifications of compositions ( ensembles ) of algorithms for solving real-time pattern recognition problems . The aim of the study is to improve the known machine learning algorithms for pattern recognition using a minimum amount of time ( the minimum number of used classifiers ) and with a given accuracy of the results . A training algorithm has been developed to find these sets of straight line segments that minimize the mean square error . This technique has been applied on two real pattern recognition problems : ( 1 ) breast cancer data set to classify tumors as benign or malignant ; ( 2 ) wine data set to classify wines in one of the three different cultivators from which they could be derived . This technique was also tested with two artificial data sets in order to show its ability to solve approximation function problems . It has been demonstrated that the spectral relaxation solution of K-way grouping is located on the subspace of the largest K eigenvectors . However , we find from a lot of experiments that the top K eigenvectors can not always detect the structure of the data for real pattern recognition problems . So it is necessary to select eigenvectors for spectral clustering . The heart of the classifier is a trainable integrated analog neural network structure . Because of its speed the hardware realization is able to solve real time image recognition problems . Simulations with a standard particle image have shown good results not only for the 2D case , but also for the 3D case . This approach is based on the client/server architecture . On the server side , the standard academic research software LabVIEW platform is used for real time pattern recognition processing , enabling the quick augmented reality application creation . Adobe Flash technology is used to get a well-designed friendly graphical client interface . Edinburgh . 1938 . 4 & Hoover , J. E. , Classification of Fingerprints . U.S. Government Printing Office , Washington . 1945 . To increase the classification accuracy of fingerprint images , it is necessary to improve image quality which is a key role for correct recognition . In other words , enhancing the fingerprint images leads us to obtain better results in classification of fingerprint images . Although Gabor filter and fast Fourier transform ( FFT ) are used to enhance fingerprint images , Gabor filter acts better than FFT in detection of incorrect ridge endings and ridge bifurcation , while FFT tries to connect broken ridges together and fill the created holes . Comparison of Neuro-fuzzy Networks for Classification Fingerprint Images Classification means to assign a given fingerprint to one of the existing classes already recognized in the literature . A search over all the fingerprints in the database takes a long time , so thegoal is to reduce the search time and computational complexity by choosing an appropriate subset of database for search . In this paper , we propose an approach for the classification of fingerprint databases . It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices . A fuzzy multilayer perceptron is used for the classification of fingerprint patterns . The input vector consists of texturebased features along with some directional features . This is possible because the leukemia ’s are generally characterized by numerical and structural chromosomal abnormalities . During the last three decades , several developments in the field of automated chromosome analysis have been occurred . Different preprocessing , segmentation , feature extraction and classification methods are applied to develop an automatic karyotyping system . Automated chromosome classification has been an important pattern recognition problem for decades . In order to im-prove the performance of automated chromosome classification , artificial intelligence and machine learning methods have been widely used in the computer-assisted chromosome detection and classification systems . This paper is focused on these algorithms , especially on artificial neural network ( ANN ) and wavelet transform algorithms . A proper hypothesis is performed for separating overlapped and touching chromosome images . The isolation of overlapping and touching metaspread chromosome image helps in accurate automated chromosome classification to obtain a karyotype . The overall accuracy of the proposed method works well for separation of touching chromosome with 97.09 % and overlapped chromosome with 95 % . A set of statistical and supervised neural network classification methods were used to classify the sex of the patellar feature vectors . Nonlinear classifiers such as neural networks have been used in previous research to analyze several medical diagnosis problems , including quantitative tissue characterization and automated chromosome classification . In this paper , different classification methods were compared . It can be utilized for radiation treatment planning , inverse source problems , and 3D and 4D computed tomography . Furthermore it is directly applicable to many computer vision and pattern recognition problems , as well as to problems in robotics and polymer science . Here , we present an algorithm for the discrete implementation of the Wiener filter and provide a comparison of the proposed image reconstruction method with the filtered back projection algorithms . Invariant image descriptors play an important role in many computer vision and pattern recognition problems such as image search and retrieval . A dominant paradigm today is that of " bags of features " , a representation of images as distributions of primitive visual elements . The finite mixture model based on the Student's-t distribution , which is heavily tailed and more robust than the Gaussian mixture model ( GMM ) , is a flexible and powerful tool to address many computer vision and pattern recognition problems . However , the Student's-t distribution is unbounded and symmetrical around its mean . Separating the influence of the spectral distribution of the illumination and the reflectance properties of the object is known as the color constancy problem . Successful separation is important for vision and pattern recognition tasks , quality control in the graphic arts and image database applications . We describe an approach to the color constancy problem which is based on statistical assumptions about the distribution of colors . Thus , the main goals of this paper are to identify the comparison of the performance of criterion function in the context of partition clustering approach , k means , and agglomerative hierarchical approach . By comparing all this we establish right clustering algorithm to produce qualitative clustering of real world document . And also modify existing algorithm to establish right algorithm which we try to make more efficient than existing algorithms which we are study in this paper . In particular , we compare the two main approaches of document clustering , agglomerative hierarchical clustering Modified BIRCH and Partitional clustering algorithm K-means . As a result of comparing both algorithms we attempt to establish appropriate clustering technique to generate qualitative clustering of real world document . Many challenges exist in how to analyze Weibo data . Most works focus on Weibo clustering and topic classification based on analyzing the text contents only . However , the traditional approaches do not work well because most messages on Weibo are very short Chinese sentences . Similarity-based Clustering ( SBC ) is used by many existing systems for filtering information . Decentralized gossip-based overlays offer a simple , robust and scalable solution to SBC clustering . Convergence and communication complexity are the two key areas of concern when SBC is implemented using these overlays . In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed . Clustering of spike trains based on the presence of synchronous neural activity is of high relevance in neurophys-iological studies . In this context such activity is thought to be associated with functional structures in the brain . We show that the model is capable of reproducing the typical hierarchical structure of real retail systems . Furthermore , it appears that the choice of protocol and strategy has an impact on the degree of spatial clustering of outlets as well as on the performance of each individual supplier . The choice of strategy is particularly critical for relatively weak suppliers . Experiments are conducted using conversational speech from several languages . The models are first evaluated in a separate syllable clustering task and then as a part of a full ZS system in order to examine the potential of BNP methods and illuminate the relative importance of different model design choices . After exploiting concept- and context-enriched sentence vector representations , we develop two co-clustering frameworks to enhance sentence-level clustering for theme-based summarization — integrated clustering and interactive clustering — both allowing word and document to play an explicit role in sentence clustering as independent text objects rather than using word or concept as features of a sentence in a document set . In each framework , we experiment with two-level co-clustering ( i.e. , sentence-word co-clustering or sentence-document co-clustering ) and three-level co-clustering ( i.e. , document-sentence-word co-clustering ) . Compared against concept- and context-oriented sentence-representation reformation , co-clustering shows a clear advantage in both intrinsic clustering quality evaluation and extrinsic summarization evaluation conducted on the Document Understanding Conferences ( DUC ) datasets . In this paper , based on analysis of the auto-ware communication behaviour , a network level approach in clustering of HTTP auto-ware communication is proposed . The experimentation with real outbound HTTP traffic data which collected through a proxy server of a private network gives a considerable result in clustering HTTP auto-ware traffic . The results can be used as a good resource for further security purposes such as malicious domain/URL detection or investigation of HTTP based malware . An important question is then how to determine the set of service levels to maintain and how to map each request to an appropriate service level , such that the requested reliabilities are guaranteed and the total cost of mapping is minimized ? We formally formulate this as an optimal request clustering problem since each service level acts as a cluster and can host multiple requests . In particular , we formulate the Migratory Clustering Problem and the Non-Migratory Clustering Problem , depending on whether a request can migrate from one service level to another after its initial assignment . More specifically , we propose a mixture of PLC models to capture the multiple peaks in buzz time-series and furthermore develop a probabilistic graphical model ( K-MPLC ) to automatically discover inherent life cycle patterns within a collection of buzzes . Our experiment results show that our proposed method significantly outperforms existing state-of-the-art approaches on buzzes clustering . As a result , the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution . We show how the adversarial autoencoder can be used in applications such as semi - supervised classification , disentangling style and content of images , unsupervised clustering , dimensionality reduction and data visualization . We performed experiments on MNIST , Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi - supervised classification tasks . The network model for two-class pattern classification originally proposed by C. Koutsougeras and C.A. Papachristou ( 1988 ) is extended to n-class pattern classification . The proposed model has the advantage of expanding the network by adding the units during the partitioning of the input space while other models have the network topology specified . Through unsinkability classification , we can know whether the ship will sink or not and its sinking probability . The unsinkability classification is a N-class pattern recognition problem . The fuzzy support vector machine ( FSVM ) is used to distinguish a certain unsinkability grade from other unsinkability grades firstly . This paper proposes a new method named the binary common spatial patterns ( BCSP ) algorithm to extend the basic CSP method to multi-class recognition . Our method arranges the spatial filters and Fisher classifiers in the form of a binary tree whereby N - 1 spatial filters and N - 1 Fisher classifiers are calculated for N class recognition . This is fewer than must be calculated in other methods ( e.g. one-versus-rest , OVR ) .
Multi-class SVMs with the probabilistic output ( MP-SVMs ) are important techniques in pattern recognition . Two key challenges for efficient GPU accelerations for MP-SVM are : ( i ) many kernel values are repeatedly computed as a binary SVM classifier is trained iteratively , resulting in repeated accesses to the high latency GPU memory ; ( ii ) performing training or estimating probability in parallel requires a much larger memory footprint than the GPU memory .
Feature Subset Selection is a well known task in the Machine Learn-ing , Data Mining , Pattern Recognition and Text Learning paradigms . In this chapter , we present a set of Estimation of Distribution Algorihtms ( EDAs ) inspired techniques to tackle the Feature Subset Selection problem in Machine Learning and Data Mining tasks .
In pattern recognition literature , it is well known that a finite number of training samples cause practical difficulties in designing a classifier . Moreover , the generalization error of the classifier tends to increase as the number of features gets large .
This correspondence calls attention to several frequently used assumptions and techniques culled from the pattern recognition literature .
Cluster analysis or " unsupervised " classification ( from " unsupervised learning " , in pattern recognition literature ) usually concerns a set of exploratory multivariate data analysis methods and techniques for grouping either statistical data units or variables into groups of similar elements , that is finding a clustering structure in the data . Classical clustering methods usually work with a set of objects as statistical data units described by a set of homogeneous ( that is , of the same type ) variables in a two-way framework .
The proposed technique is the implementation of a parallel comparison of data with respect to various pre-stored categories . We design smart pulse-based stochastic-logic blocks to provide an efficient pattern recognition analysis . The proposed architecture can speed-up the screening process of huge databases by two orders of magnitude with respect classical software-based solutions , thus implying a great improvement in terms of total performance ( speed and power dissipation ) .
The same procedure is repeated for the construction of inner iris boundary . The circular iris region can be un-wrapped into rectangular form for the purpose of pattern recognition analysis . The iris localization was conducted on iris images taken from healthy women free from Human Papilloma Virus ( HPV ) .
vulgaris ) samples and their antioxidant potential ( IC50 ) by using powerful chemometric tools , such as Wald-Wolfowitz run test , cluster analysis and sum of ranking differences , in order to obtain groupings of the samples which share similar properties or to test if the samples come from the same population . Besides the aforementioned classification or pattern recognition analysis , the regression methods ( linear , multiple linear and artificial neural network regressions ) were carried out in order to establish reliable linear and non-linear relationships between IC50 and contents of betalains , phenolic compounds and dry matter . The results reveal certain influence of foil cover , used during the cultivation , on beetroot antioxidant potential and content of the determined betalains and phenolic compounds , as well as the strong non-linear relationship between the analyzed variables .
It includes such measures as a set monotonicity , complexness , repetition , relative dimensionality , relative dependence approximation simplicity , relative inconsistency , evenness , class separability and compactness , integrated criteria of training set quality evaluation , sample and feature selection criteria . The using of offered criterions in practice allows to automatize the process of a construction , analysis and comparison of neural models for pattern recognition problem solving .
Then a BP neural network is established in which the time factor is considered based on genetic algorithm . The characteristic parameters are used as input specimen , learning and training the network to complete the pattern recognition of model working state . Experiments show that the method can quickly and reliably discriminate the conditions of the punch die and has strong practicability .
c ) Technologies related to learning Technologies for adapting to similar tasks and using past experience must be developed . d ) Technologies related to pattern recognition Pattern recognition for spatial and temporal sensory information must be developed . e ) Others Programming languages must be developed which validate input , movement description , parallel movement control , use environmental models , graphic and speech understanding , and effective human-computer interfaces .
Significant performance gain has been observed , especially in the presence of large amount of training data , when deep learning techniques are used for image classification and speech recognition . On the one hand , hierarchical and recursive networks have demonstrated great promise in automatically learning thousands or even millions of features for pattern recognition ; on the other hand concerns about deep learning have been raised and many fundamental questions remain open . Some potential problems with the current DL frameworks include : reduced transparency and discriminativeness of the features learned at hidden layers ; training difficulty due to exploding and vanishing gradients ; lack of a thorough mathematical understanding about the algorithmic behavior , despite of some attempts made on the theoretical side ; dependence on the availability of large amount of training data ; complexity of manual tuning during training .
An Internet of Things Application . This paper presents an IoT-enabled server device which is capable of authenticating a person by fingerprint recognition and grant access to the client application for making the attendance logging possible with the increased level of security . The ability of the device is to provide precise matching of fingerprints increases the possibility of error-free attendance logging .
Fingerprint image segmentation is part of preprocessing in fingerprint image recognition system . It has a critical effect to the fingerprint image recognition system . A combined method segmentation method is proposed in this article .
However , these systems still encounter difficulties due to various noise factors present in operating environments . The purpose of this article is to give an overview of fingerprint-based recognition and discuss
In addition , papers by Ao Liu , Minrong Lu , Jin Zhang , and Fan Wu focused on structural optimization of neural network models . Moreover , papers by Xiaofeng Zou , Minrong Lu , Xiaoyong Tang , Titinunt Kitrungrotsakul , and Fan Wu focused on performance optimization of neural network models and their applications in various fields , such as monetary policy prediction , bioinformatics , image classification , pedestrian re-identification , fingerprint pattern recognition , and human personality classification . The paper by Zheng Xiao et al. focused on task scheduling and virtual machine ( VM ) allocation in distributed computing environments and described a workload-driven coordination mechanism between virtual machine allocation and task scheduling .
A contactless type of a five fingerprint image capturing apparatus is provided to solve user 's aversion , the distortion of a fingerprint , and the deterioration of fingerprint recognizing performance by obtaining the front and both-side image the five fingerprint of a user with an attempt by using a single photographing device with a pair of reflecting units or a pair of optical refracting units and a rotating function or using a pair of reflecting units or a pair of optical refracting units . A contactless type of a five fingerprint image capturing apparatus comprises the followings : 5 five finger supports(20 ) ; a pair of reflecting units or optical refracting units which is installed at an inclination angle capable of radiating the fingerprint area of both sides in both sides of fingers supported by each finger support ; a camera(40 ) which obtains a five fingerprint image by photographing all areas of a finger print including a fingerprint area of a front direction corresponding to an optical axis and a fingerprint area of both sides not corresponding to the optical axis for each finger while moving to the lower part of the finger supported by the each finger support according as a rotating unit rotates in a state that the camera is fixed in a side of the rotating unit ; a light device(50 ) which is installed in the front end of the camera and radiates light to the fingerprint area of a user ; a diffuser(60 ) ; an operation switch(70 ) ; and a housing(10 ) .
In this paper , the research of fingerprint automatic recognition with micro-computer-based image processing system is introduced . Method of fingerprint image preprocessing , fingerprint recognition and classification fingerprint database establishment and verification are presented .
However , the parallelization of existing applications has very frequently proved to be a cumbersome task and in many cases the parallel application is slower than the original serial one . This work demonstrates the parallelization of one high-end bioinformatics application ( multiple sequence alignment for amino acids or nucleotide sequences “ MAFFT ” ) as well as a novel security application ( fingerprinting recognition “ NBIS ” ) on a highly parallel , yet a very low cost , system . We initially demonstrate the method for parallelizing the applications and then we focus on the end performance .
With the development of information science and technology , people awaremore about social public safe and personal informa-tion ’s safe . First , it is described that the development and research state of thefingerprint recognition in chapter one . The biometric identifica-tion technology and the fingerprint recognition technology are translated in concept , and the principles and features of fingerprint recogni-tion are given obviously too .
Artificial bee colony ( ABC ) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source . In this paper , we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems . Finally , the proposed approach is tested on several pattern recognition problems .
This paper deals with the optimal use of a sequence of prior observations in order to recognize patterns . The class of pattern recognition problems which we treat is completely general in that the results may be applied to patterns of a visual , aural or electromagnetic origin . We show that the use of prior observations for pattern recognition may be described as a process of learning the statistical characteristics of the patterns involved .
ABSTRACT This paper describes some relations between stochastic automata and stochastic languages , and the possible application of stochastic languages to pattern recognition problems . Stochastic finite-state and stochastic context-free languages are defined .
The approach makes few assumptions about the target pattern class and should therefore be fairly general , as long as the target class has predictable image boundaries . We show that this is indeed the case by demonstrating the technique on two other pattern detection/recognition problems . Because our object and pattern detection approach is very much learning-based , how well a system eventually performs depends heavily on the quality of training examples it receives .
The recommendations based on the natural computation for the pattern recognition problem were described . The main work purpose included analysis a natural computing and the search for the pattern recognition problem solution by new features . Natural computing model types description and the trends of the virtual environments applications of the problems solving were considered .
Previous studies have proven that imitating the mechanism of recognizing alien cells is beneficial and provides so many solutions to the pattern recognition related problems . These efforts emulate the human immune system in recognizing the cells by considering every essential component or features of the subjects .
For the vector classification problem considered ( circle in the square ) , this algorithm trains 20 times faster than backprop . For the pattern recognition type problems considered , the implementation in parallel architecture of this algorithm would train in half the time of the implementation in parallel architecture of standard backpropagation . Such an algorithm would be useful in time-critical applications where there is a steady stream of training data at a fixed rate .
Class imbalance may reduce the classifier performance in several recognition pattern problems . Such negative effect is more notable with least represented class ( minority class ) Patterns .
To answer that question , a rigorous mathematical approach has been developed and implemented as computer software . Our preliminary results indicate that the conceptual approach that has been developed can be used to answer this question , and suggest that memristor networks are capable of real-time pattern recognition .
Conventional image editing software in combination with other techniques are not only difficult to apply to an image but also permits a user to perform some basic functions one at a time . However , image processing algorithms and photogrammetric systems are developed in the recent past for real-time pattern recognition applications . A graphical user interface ( GUI ) is developed which can perform multiple functions simultaneously for the analysis and estimation of geometric distortion in an image with reference to the corresponding distorted image .
Real time pattern recognition applications often deal with high dimensional data , which require a data reduction step which is only performed offline . However , this loses the possibility of adaption to a changing environment .
A bagging ensemble consists of a set of classifiers trained independently and combined by a majority vote . Such a combination improves generalization performance but can require large amounts of memory and computation , a serious drawback for addressing portable real-time pattern recognition applications . We report here a compact three-dimensional ( 3D ) multiprecision very large-scale integration ( VLSI ) implementation of a bagging ensemble .
The proposed formal neuron has a regular circuit pattern as it consists mostly of memory hardware . Thus a wafer scale implementation of a network composed of such neurons may provide a cost effective way to solve many real-time pattern recognition problems . >
The feature and classifier selection is one of the most challenging tasks in the field of Pattern Recognition . As the performance of 400 dimensional feature is already established in numeral recognition field , for the present work we have focused on performance evaluation of classifiers in handling complex real time Pattern Recognition problems like Numeral Recognition . Here we have selected Support Vector Machine ( SVM ) , Library for Large Linear ( LIBLINEAR ) , Multilayer Perceptron ( MLP ) , Fuzzy Un-ordered Rule Induction Algorithm ( FURIA ) , Modified Quadratic Discriminant Function ( MQDF ) as the classifiers for recognition of the numerals and comparison of the results .
The efficiency of the proposed algorithm is shown by specific examples . The paper studies modifications of compositions ( ensembles ) of algorithms for solving real-time pattern recognition problems . The aim of the study is to improve the known machine learning algorithms for pattern recognition using a minimum amount of time ( the minimum number of used classifiers ) and with a given accuracy of the results .
A training algorithm has been developed to find these sets of straight line segments that minimize the mean square error . This technique has been applied on two real pattern recognition problems : ( 1 ) breast cancer data set to classify tumors as benign or malignant ; ( 2 ) wine data set to classify wines in one of the three different cultivators from which they could be derived . This technique was also tested with two artificial data sets in order to show its ability to solve approximation function problems .
It has been demonstrated that the spectral relaxation solution of K-way grouping is located on the subspace of the largest K eigenvectors . However , we find from a lot of experiments that the top K eigenvectors can not always detect the structure of the data for real pattern recognition problems . So it is necessary to select eigenvectors for spectral clustering .
The heart of the classifier is a trainable integrated analog neural network structure . Because of its speed the hardware realization is able to solve real time image recognition problems . Simulations with a standard particle image have shown good results not only for the 2D case , but also for the 3D case .
This approach is based on the client/server architecture . On the server side , the standard academic research software LabVIEW platform is used for real time pattern recognition processing , enabling the quick augmented reality application creation . Adobe Flash technology is used to get a well-designed friendly graphical client interface .
Edinburgh . 1938 . 4 & Hoover , J. E. , Classification of Fingerprints . U.S. Government Printing Office , Washington . 1945 .
To increase the classification accuracy of fingerprint images , it is necessary to improve image quality which is a key role for correct recognition . In other words , enhancing the fingerprint images leads us to obtain better results in classification of fingerprint images . Although Gabor filter and fast Fourier transform ( FFT ) are used to enhance fingerprint images , Gabor filter acts better than FFT in detection of incorrect ridge endings and ridge bifurcation , while FFT tries to connect broken ridges together and fill the created holes .
Comparison of Neuro-fuzzy Networks for Classification Fingerprint Images Classification means to assign a given fingerprint to one of the existing classes already recognized in the literature . A search over all the fingerprints in the database takes a long time , so thegoal is to reduce the search time and computational complexity by choosing an appropriate subset of database for search .
In this paper , we propose an approach for the classification of fingerprint databases . It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices .
A fuzzy multilayer perceptron is used for the classification of fingerprint patterns . The input vector consists of texturebased features along with some directional features .
This is possible because the leukemia ’s are generally characterized by numerical and structural chromosomal abnormalities . During the last three decades , several developments in the field of automated chromosome analysis have been occurred . Different preprocessing , segmentation , feature extraction and classification methods are applied to develop an automatic karyotyping system .
Automated chromosome classification has been an important pattern recognition problem for decades . In order to im-prove the performance of automated chromosome classification , artificial intelligence and machine learning methods have been widely used in the computer-assisted chromosome detection and classification systems . This paper is focused on these algorithms , especially on artificial neural network ( ANN ) and wavelet transform algorithms .
A proper hypothesis is performed for separating overlapped and touching chromosome images . The isolation of overlapping and touching metaspread chromosome image helps in accurate automated chromosome classification to obtain a karyotype . The overall accuracy of the proposed method works well for separation of touching chromosome with 97.09 % and overlapped chromosome with 95 % .
A set of statistical and supervised neural network classification methods were used to classify the sex of the patellar feature vectors . Nonlinear classifiers such as neural networks have been used in previous research to analyze several medical diagnosis problems , including quantitative tissue characterization and automated chromosome classification . In this paper , different classification methods were compared .
It can be utilized for radiation treatment planning , inverse source problems , and 3D and 4D computed tomography . Furthermore it is directly applicable to many computer vision and pattern recognition problems , as well as to problems in robotics and polymer science . Here , we present an algorithm for the discrete implementation of the Wiener filter and provide a comparison of the proposed image reconstruction method with the filtered back projection algorithms .
Invariant image descriptors play an important role in many computer vision and pattern recognition problems such as image search and retrieval . A dominant paradigm today is that of " bags of features " , a representation of images as distributions of primitive visual elements .
The finite mixture model based on the Student's-t distribution , which is heavily tailed and more robust than the Gaussian mixture model ( GMM ) , is a flexible and powerful tool to address many computer vision and pattern recognition problems . However , the Student's-t distribution is unbounded and symmetrical around its mean .
Separating the influence of the spectral distribution of the illumination and the reflectance properties of the object is known as the color constancy problem . Successful separation is important for vision and pattern recognition tasks , quality control in the graphic arts and image database applications . We describe an approach to the color constancy problem which is based on statistical assumptions about the distribution of colors .
Thus , the main goals of this paper are to identify the comparison of the performance of criterion function in the context of partition clustering approach , k means , and agglomerative hierarchical approach . By comparing all this we establish right clustering algorithm to produce qualitative clustering of real world document . And also modify existing algorithm to establish right algorithm which we try to make more efficient than existing algorithms which we are study in this paper .
In particular , we compare the two main approaches of document clustering , agglomerative hierarchical clustering Modified BIRCH and Partitional clustering algorithm K-means . As a result of comparing both algorithms we attempt to establish appropriate clustering technique to generate qualitative clustering of real world document .
Many challenges exist in how to analyze Weibo data . Most works focus on Weibo clustering and topic classification based on analyzing the text contents only . However , the traditional approaches do not work well because most messages on Weibo are very short Chinese sentences .
Similarity-based Clustering ( SBC ) is used by many existing systems for filtering information . Decentralized gossip-based overlays offer a simple , robust and scalable solution to SBC clustering . Convergence and communication complexity are the two key areas of concern when SBC is implemented using these overlays .
In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed . Clustering of spike trains based on the presence of synchronous neural activity is of high relevance in neurophys-iological studies . In this context such activity is thought to be associated with functional structures in the brain .
We show that the model is capable of reproducing the typical hierarchical structure of real retail systems . Furthermore , it appears that the choice of protocol and strategy has an impact on the degree of spatial clustering of outlets as well as on the performance of each individual supplier . The choice of strategy is particularly critical for relatively weak suppliers .
Experiments are conducted using conversational speech from several languages . The models are first evaluated in a separate syllable clustering task and then as a part of a full ZS system in order to examine the potential of BNP methods and illuminate the relative importance of different model design choices .
After exploiting concept- and context-enriched sentence vector representations , we develop two co-clustering frameworks to enhance sentence-level clustering for theme-based summarization — integrated clustering and interactive clustering — both allowing word and document to play an explicit role in sentence clustering as independent text objects rather than using word or concept as features of a sentence in a document set . In each framework , we experiment with two-level co-clustering ( i.e. , sentence-word co-clustering or sentence-document co-clustering ) and three-level co-clustering ( i.e. , document-sentence-word co-clustering ) . Compared against concept- and context-oriented sentence-representation reformation , co-clustering shows a clear advantage in both intrinsic clustering quality evaluation and extrinsic summarization evaluation conducted on the Document Understanding Conferences ( DUC ) datasets .
In this paper , based on analysis of the auto-ware communication behaviour , a network level approach in clustering of HTTP auto-ware communication is proposed . The experimentation with real outbound HTTP traffic data which collected through a proxy server of a private network gives a considerable result in clustering HTTP auto-ware traffic . The results can be used as a good resource for further security purposes such as malicious domain/URL detection or investigation of HTTP based malware .
An important question is then how to determine the set of service levels to maintain and how to map each request to an appropriate service level , such that the requested reliabilities are guaranteed and the total cost of mapping is minimized ? We formally formulate this as an optimal request clustering problem since each service level acts as a cluster and can host multiple requests . In particular , we formulate the Migratory Clustering Problem and the Non-Migratory Clustering Problem , depending on whether a request can migrate from one service level to another after its initial assignment .
More specifically , we propose a mixture of PLC models to capture the multiple peaks in buzz time-series and furthermore develop a probabilistic graphical model ( K-MPLC ) to automatically discover inherent life cycle patterns within a collection of buzzes . Our experiment results show that our proposed method significantly outperforms existing state-of-the-art approaches on buzzes clustering .
As a result , the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution . We show how the adversarial autoencoder can be used in applications such as semi - supervised classification , disentangling style and content of images , unsupervised clustering , dimensionality reduction and data visualization . We performed experiments on MNIST , Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi - supervised classification tasks .
The network model for two-class pattern classification originally proposed by C. Koutsougeras and C.A. Papachristou ( 1988 ) is extended to n-class pattern classification . The proposed model has the advantage of expanding the network by adding the units during the partitioning of the input space while other models have the network topology specified .
Through unsinkability classification , we can know whether the ship will sink or not and its sinking probability . The unsinkability classification is a N-class pattern recognition problem . The fuzzy support vector machine ( FSVM ) is used to distinguish a certain unsinkability grade from other unsinkability grades firstly .
This paper proposes a new method named the binary common spatial patterns ( BCSP ) algorithm to extend the basic CSP method to multi-class recognition . Our method arranges the spatial filters and Fisher classifiers in the form of a binary tree whereby N - 1 spatial filters and N - 1 Fisher classifiers are calculated for N class recognition . This is fewer than must be calculated in other methods ( e.g. one-versus-rest , OVR ) .
Efficient Multi-Class Probabilistic SVMs on GPUs Feature Subset Selection by Estimation of Distribution Algorithms Effect of the feature vector size on the generalization error: the case of MLPNN and RBFNN classifiers Candide's Practical Principles of Experimental Pattern Recognition On cluster analysis of complex and heterogeneous data An unconventional computing technique for ultra-fast and ultra-low power data mining A Comparison of Iris Localization Techniques for Pattern Recognition Analysis Chemometric guidelines for selection of cultivation conditions influencing the antioxidant potential of beetroot extracts The training set quality measures for neural network learning Study of Punch Die Condition Discrimination Based on Wavelet Packet and Genetic Neural Network Distributed artificial intelligence: an annotated bibliiography Deeply-Supervised Nets Biometric Fingerprint Attendance System: An Internet of Things Application Fingerprint image segmentation based on a combined method Fingerprint-Based Recognition Editorial introduction: special issue on advances in parallel and distributed computing for neural computing Contactless type of five fingerprint images capturing apparatus MICRO-COMPUTER-BASED FINGERPRINT AUTOMATIC RECOGNITION Parallelizing bioinformatics and security applications on a low-cost multi-core system Research of an Automatic Fingerprint Recognition System Based on PC Training Spiking Neural Models Using Artificial Bee Colony Learning to recognize patterns in a random environment Stochastic Automata, Stochastic Languages, and Pattern Recognition Learning and example selection for object and pattern detection Natural computing: From natural phenomena to practical tasks Recognizing Music Features Pattern Using Modified Negative Selection Algorithm for Songs Genre Classification A conjugate-gradient based algorithm to train feedforward multilayer perceptrons Using hybrid associative classifier with translation (HACT) for studying imbalanced data sets Computing with Memristor Networks GUI for Coordinate Measurement of an Image for the Estimation of Geometric Distortion of an Opto-electronic Display System The on-line curvilinear component analysis (onCCA) for real-time data reduction A compact 3D VLSI classifier using bagging threshold network ensembles Distributed arithmetic implementation of artificial neural networks Comparison of the classifiers in Bangla handwritten numeral recognition Модификация алгоритма WaldBoost для повышения эффективности решения задач распознавания образов в реальном времени A New Machine Learning Technique Based on Straight Line Segments Spectral clustering with eigenvector selection based on entropy ranking Single Chip-VLSI-realization of a neural net for image recognition An approach to develop a LabVIEW based augmented reality application for smartphones The Sending of Fingerprints by the Telephoto System Combining Gabor filter and FFT for fingerprint enhancement based on a regional adaption method and automatic segmentation Comparison of Neuro-fuzzy Networks for Classification Fingerprint Images A New Approach for the Fingerprint Classification Based On Gray-Level Co- Occurrence Matrix Fingerprint classification using a fuzzy multilayer perceptron SURVEY ON CHROMOSOME IMAGE ANALYSIS FOR ABNORMALITY DETECTION IN LEUKEMIAS Algorithms for Chromosome Classification Analysis of human chromosome images: Application towards an automated chromosome classification Patella sex determination by 3D statistical shape models and nonlinear classifiers. A Wiener Filtering Approach over the Euclidean Motion Group for Radon Transform Inversion Spatially-Sensitive Affine-Invariant Image Descriptors Bounded Asymmetrical Student's-t Mixture Model Color image normalization through illuminant recovery Comparison of Algorithms for Document Clustering Time Improving Policy of Text Clustering Algorithm by Reducing Computational Overheads Weibo clustering: A new approach utilizing users' reposting data in social networking services Optimum Benefit Protocol: A fast converging, bandwidth-efficient decentralized similarity overlay Spectral Clustering of Synchronous Spike Trains A Multiagent Model of Negotiation Processes between Multiple Actors in Urban Developments: A Framework for and Results of Numerical Experiments Comparison of Non-Parametric Bayesian Mixture Models for Syllable Clustering and Zero-Resource Speech Processing Enhancing sentence-level clustering with integrated and interactive frameworks for theme-based summarization Behaviour similarity based to cluster automated HTTP communication Optimal Request Clustering for Link Reliability Guarantee in Wireless Networked Control Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery Adversarial Autoencoders Pattern classifying neural network based on Fisher's linear discriminant function Damaged ship unsinkability classification model based on fuzzy support vector machine A multi-class pattern recognition method for motor imagery EEG data
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Estimation of Distribution Algorithms ICPR PATMOS Asia International Conference on Modelling and Simulation ISNN (2) AISTATS VECIMS AICCSA HIS IJCNN ICMLA IECON Medical Imaging: Image Processing ECCV (2) ICASSP IJCNN INTERSPEECH WCNC ICDM ArXiv FSKD SMC
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