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共 276 条记录，以下是161-170 订阅

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Neural Networks Applied for Cork Tiles image Classification

Neural Networks Applied for Cork Tiles Image Classification

引用

ieee symposium on computational intelligence in image and signal processing, ciisp

作者： A. Georgieva I. Jordanov T. Rafik School of Computing University of Portsmouth Portsmouth UK School of Product and Engineering Design University of Wales Institute of Science and Technology Cardiff UK

In this paper a hybrid global optimization method GLPTS is further investigated and applied for feed-forward neural networks supervised learning. The method is initially tested on several benchmark problems and subsequently employed for pattern recognition problem. The proposed technique is used for training neural networks (NN) that have to inspect and classify three types of cork tiles images. During the feature extraction phase, statistical textural characteristics are obtained from the tiles' images and then used for training several different NN architectures. Results from the testing phase are discussed and analysed, showing good generalization abilities of the trained NN. Finally, directions of future work are briefly stated

关键词： Neural networks Tiles image classification Optimization methods Feedforward systems Feedforward neural networks Supervised learning Benchmark testing Pattern recognition Feature extraction

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image Segmentation using a Weighted Kernel PCA Approach to Spectral Clustering

Image Segmentation using a Weighted Kernel PCA Approach to S...

引用

ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Carlos Alzate Johan A. K. Suykens Department of Electrical Engineering ESATSCD-SISTA Katholieke Universiteit Leuven Leuven Belgium

In classical graph-based image segmentation, a data-driven matrix is constructed representing similarities between every pair of pixels. The eigenvectors of such matrices contain relevant information about the clusters present on the image. An approach to image segmentation using spectral clustering with out-of-sample extensions is presented. This approach is based on the weighted kernel PCA framework. An advantage of the proposed method is the possibility to train and validate the clustering model on subsampled parts of the image to be segmented. The cluster indicators for the remaining pixels can then be inferred using the out-of-sample extension. This subsampling scheme can be used to reduce the computation time of the segmentation. Simulation results with grayscale and color images show improvements in terms of computation times together with visually appealing clusters

关键词： image segmentation Kernel Principal component analysis Pixel Clustering algorithms Optimization methods Matrix converters Matrix decomposition Partitioning algorithms Iterative algorithms

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A Proposal of Blind Identification Method Based on Over-Sampling Using Orthogonal Filter for IIR system

A Proposal of Blind Identification Method Based on Over-Samp...

引用

International symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Tajima, Shinya Ogawa, Tomomi Matsumoto, Hiroki Maebashi Inst Technol Maebashi Gunma Japan

ISBN: (纸本)9781479961207

There is the over-sampling method which is one of the methods for realizing the blind identification. In the conventional method, the unknown system is usually assumed by FIR system and the estimation filter is also used by FIR filter. However, if a target system has long impulse length in many real environment, we may have to assume IIR system for the unknown system. If we use FIR filters to estimate such systems, the identification performance is lower. Therefore, it is necessary to research the blind identification method for the unknown system as IIR system. Then, we propose the blind identification based on over-sampling method using orthogonal filter for IIR system and we evaluate the identification performance by computer simulations.

关键词： IIR filters blind source separation signal sampling IIR system blind identification method computer simulations identification performance evaluation orthogonal filter over-sampling method Communication systems computational modeling Computer simulation Estimation Finite impulse response filters Vectors

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Evolutionary Algorithms in the Classification of Mammograms

Evolutionary Algorithms in the Classification of Mammograms

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： D. C. Hope E. Munday S. L. Smith Department of Electronics University of York York UK

The application of pattern recognition techniques to radiology has the potential to detect cancer earlier and save lives, and consequently much research has been devoted to this problem. This worked tackled a subset of the problem, investigating a novel method of classifying mammograms using an evolutionary approach known as Cartesian genetic programming (CGP). Microcalcifications, one of two major indicators of cancer on mammograms, were used for the classification. A large software framework was written in order to investigate this, which allows the viewing of images, manual segmentation of lesions and then automatic classification. Two classification approaches were pursued, the first classifying on texture features and the second, a new approach, classifying by using the lesion's raw pixel array. Early results using the system showed some potential. It was found that during training, networks could obtain correct classification rates of between 80 and 100%. The best results were approaching those in the contemporary literature and suggest the technique warrants further investigation

关键词： Evolutionary computation Breast cancer Cancer detection Pattern recognition image segmentation image processing computational intelligence signal processing algorithms Radiology Genetic programming

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An Improved Evolution-COnstructed (iECO) Features Framework Distribution Statement A: Approved for public release;distribution is unlimited.

An Improved Evolution-COnstructed (iECO) Features Framework ...

引用

ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Price, Stanton R. Anderson, Derek T. Luke, Robert H. Mississippi State Univ Elect & Comp Engn Dept Mississippi State MS 39762 USA US Army RDECOM CERDEC Night Vis & Elect Sensors Directorate Ft Belvoir VA USA

ISBN: (纸本)9781479945030

In image processing and computer vision, significant progress has been made in feature learning for exploiting important cues in data that elude non-learned features. While the field of deep learning has demonstrated state-of-the-art performance, the Evolution-COnstructed (ECO) work of Lillywhite et. al has the advantage of interpretability, and it does not predispose the solution to one of convolution. This paper presents a novel approach for extending the ECO framework. We achieve this through two overarching ideas. First, we address a potential major shortcoming of ECO features- the "features" themselves. The so-called ECO features are simply a transformed image that has been unrolled into a large one dimensional vector. We propose employing feature descriptors to extract pertinent information from the ECO imagery. Furthermore, it is our hypothesis that there exists a unique set of transforms for each feature descriptor used on a given problem domain that leads to the descriptors extracting maximal discriminative information. Second, we introduce constraints on each individual's chromosome to promote population diversity and prevent infeasible solutions. We show through experiments that our proposed iECO framework results in, and benefits from, a unique series of transforms for each descriptor being learned and maintaining population diversity.

关键词： computer vision evolutionary computation feature extraction learning (artificial intelligence) object recognition vectors 1D vector ECO imagery and computer vision feature descriptors feature learning iECO features framework image processing improved evolution-constructed features framework maximal discriminative information extraction pertinent information extraction population diversity promotion public release Biological cells Data mining Feature extraction Object recognition Sociology Statistics Transforms

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Advances in Elastic Graph Matching for Frontal Face Verification

Advances in Elastic Graph Matching for Frontal Face Verifica...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Stefanos Zafeiriou Anastasios Tefas Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

Elastic graph matching is one of the most well known techniques for frontal face recognition/verification and one of the few techniques that can be combined successfully with fully automatic face localization and alignment methods. In this paper we propose a series of techniques that enhance the performance of elastic graph matching in frontal face verification by exploiting the individuality of human facial features in many ways. First the use of discriminant analysis in the feature vectors of the graph nodes is explored. The use of the node deformation for discrimination is also proposed. Moreover, the local similarity values at the nodes of the elastic graph, are weighted by coefficients that are also derived from some discriminant analysis in order to form a total similarity measure between faces. We present an algorithm that combines all the above discriminant steps. Moreover, we propose an algorithm for finding the most discriminant landmarks upon a person's face and a person-specific graph is placed in the spatial coordinates that correspond to these discriminant features. We illustrate the improvements in performance by the proposed advances in frontal face verification using the XM2VTS database

关键词： Facial features Face recognition image analysis Face detection Humans signal processing algorithms Region 2 Cost function computational intelligence signal processing

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A Pseudo-Labeling Framework for Content-based image Retrieval

A Pseudo-Labeling Framework for Content-based Image Retrieva...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Kim-Hui Yap Kui Wu School of Electrical and Electric Engineering Nanyang Technological University Singapore

This paper presents a new pseudo-label fuzzy support vector machine (PLFSVM)-based active learning framework in interactive content-based image retrieval (CBIR) systems. One of the main issues associated with relevance feedback in CBIR systems is the small sample problem where only a limited number of labeled samples are available for learning. This is because image labeling is time consuming and users are often reluctant to label too many images for feedback. Learning from insufficient training samples often constrains the retrieval performance. To address this problem, we propose a new algorithm based on the concept of pseudo-labeling. It incorporates carefully selected unlabeled images to enlarge the training data set and assigns proper pseudo-labels to them. Further, some fuzzy rules are utilized to automatically estimate class membership of the pseudo-labeled images. Fuzzy support vector machine (FSVM) is designed to take into account the fuzzy nature of some training samples during its training. In order to exploit the advantages of pseudo-labeling, active learning and the structure of FSVM, we develop a unified framework to perform content-based image retrieval. Experimental results based on a database of 10,000 images demonstrate the effectiveness of the proposed method

关键词： image retrieval Content based retrieval Feedback Support vector machines Training data image databases Support vector machine classification computational intelligence signal processing Paper technology

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Learning Features and their Transformations from Natural Videos

Learning Features and their Transformations from Natural Vid...

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ieee symposium on computational intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Dutta, Jayanta K. Banerjee, Bonny Univ Memphis Inst Intelligent Syst Memphis TN 38152 USA Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)9781479945153

Learning features invariant to arbitrary transformations in the data is a requirement for any recognition system, biological or artificial. It is now widely accepted that simple cells in the primary visual cortex respond to features while the complex cells respond to features invariant to different transformations. We present a novel two-layered feedforward neural model that learns features in the first layer by spatial spherical clustering and invariance to transformations in the second layer by temporal spherical clustering. Learning occurs in an online and unsupervised manner following the Hebbian rule. When exposed to natural videos acquired by a camera mounted on a cat's head, the first and second layer neurons in our model develop simple and complex cell-like receptive field properties. The model can predict by learning lateral connections among the first layer neurons. A topographic map to their spatial features emerges by exponentially decaying the flow of activation with distance from one neuron to another in the first layer that fire in close temporal proximity, thereby minimizing the pooling length in an online manner simultaneously with feature learning.

关键词： Hebbian learning feedforward neural nets image sensors object recognition pattern clustering unsupervised learning video signal processing Hebbian rule arbitrary transformations artificial recognition system biological recognition system camera cat head complex cell-like receptive field properties complex cells first layer neurons learning features natural videos pooling length primary visual cortex spatial spherical clustering temporal proximity temporal spherical clustering topographic map two-layered feedforward neural model Biological system modeling computational modeling Feedforward neural networks Neurons Predictive models Radio frequency Videos feedforward neural nets Videotapes Biological system modeling Striate Cortex Hebbian principle Sensors (image) Video signal processing Unsupervised learning Cameras radiofrequency Cluster Analysis recognition system Neurons computational modeling Prediction models

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Performance Analysis of the Feedforward and SOM Neural Networks in the Face Recognition Problem

Performance Analysis of the Feedforward and SOM Neural Netwo...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： M.I. Chacon M. P. Rivas-Perea Digital Signal Processing & Vision Laboratory Chihuahua Institute of Technology Chihuahua Chih México

This paper presents a comparative study between a feedforward neural network and a SOM network. The paper also proposes the incorporation of a new spatial feature, face feature lines, FFL, to represent the faces. FFL are considered as new features based on previous studies related to face recognition tasks on newborns. Besides the face feature lines, the feature vector incorporates eigenvectors of the face image obtained with the Karhunen-Loeve transformation. A face recognition system is based on a feedforward neural network, FFBP, method. The second classification scheme uses a self organized map, SOM, architecture combined with the k-means clustering algorithm. Experiments comparing both architectures show no significant differences for the ORL database, 92% for the FFBP and 90% for the SOM. However results obtained for the Yale database, 60% for the FFBP network and 70% for the SOM, indicate a better performance with the SOM architecture

关键词： Performance analysis Neural networks Feedforward neural networks Face recognition Face detection Pediatrics Principal component analysis Facial features Lighting Robustness

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A Recurrent Functional-Link-Based Neural Fuzzy System and Its Applications

A Recurrent Functional-Link-Based Neural Fuzzy System and It...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Cheng-Hung Chen Cheng-Jian Lin Chin-Teng Lin Department of Electrical and Control Engineering National Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering Chaoyang University Taichung Taiwan

In this paper, a recurrent functional-link-based neural fuzzy system (RFLNFS) is proposed for prediction of time sequence and skin color detection. The proposed RFLNFS model uses functional link neural network as the consequent part of fuzzy rules. The RFLNFS model can generate the consequent part of a nonlinear combination of the input variables. The recurrent network is embedded in the RFLNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. Finally, the RFLNFS is applied to two simulations. The simulation results of the dynamic system modeling have shown that the RFLNFS model can solve the temporal problem and the RFLNFS model has superior performance than other models

关键词： Fuzzy systems Neural networks Input variables signal processing algorithms Partitioning algorithms Fuzzy neural networks Recurrent neural networks Polynomials Neurofeedback Fuzzy reasoning

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