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Image Processing, IEEE Transactions on

Issue 8 • Date Aug. 1998

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Displaying Results 1 - 11 of 11
  • Guest Editorial Applications Of Artificial Neural Networks To Image Processing

    Publication Year: 1998 , Page(s): 1093 - 1096
    Cited by:  Papers (3)
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    Freely Available from IEEE
  • Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach

    Publication Year: 1998 , Page(s): 1165 - 1181
    Cited by:  Papers (21)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB)  

    This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches View full abstract»

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  • A modular neural network vector predictor for predictive image coding

    Publication Year: 1998 , Page(s): 1198 - 1217
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (624 KB)  

    In this paper, we present a modular neural network vector predictor that improves the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes, based on its directional variances. One expert predictor is optimized for stationary blocks, and each of the other four expert predictors are optimized to predict horizontal, vertical, 45°, and 135° diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is transmitted to the receiver about the selected predictor or the integration of the predictors. Experimental results show that the proposed scheme gives an improvement of 1.7 dB over a single multilayer perceptron (MLP) predictor. Furthermore, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over a single MLP predictor. The perceptual quality of the predicted images is also significantly improved View full abstract»

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  • Foveal automatic target recognition using a multiresolution neural network

    Publication Year: 1998 , Page(s): 1122 - 1135
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB)  

    This paper presents a method for detecting and classifying a target from its foveal (graded resolution) imagery using a multiresolution neural network. Target identification decisions are based on minimizing an energy function. This energy function is evaluated by comparing a candidate blob with a library of target models at several levels of resolution simultaneously available in the current foveal image. For this purpose, a concurrent (top-down-and-bottom-up) matching procedure is implemented via a novel multilayer Hopfield (1985) neural network. The associated energy function supports not only interactions between cells at the same resolution level, but also between sets of nodes at distinct resolution levels. This permits features at different resolution levels to corroborate or refute one another contributing to an efficient evaluation of potential matches. Gaze control, refoveation to more salient regions of the available image space, is implemented as a search for high resolution features which will disambiguate the candidate blob. Tests using real two-dimensional (2-D) objects and their simulated foveal imagery are provided View full abstract»

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  • Neural network-based systems for handprint OCR applications

    Publication Year: 1998 , Page(s): 1097 - 1112
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    Over the last five years or so, neural network (NN)-based approaches have been steadily gaining performance and popularity for a wide range of optical character recognition (OCR) problems, from isolated digit recognition to handprint recognition. We present an NN classification scheme based on an enhanced multilayer perceptron (MLP) and describe an end-to-end system for form-based handprint OCR applications designed by the National Institute of Standards and Technology (NIST) Visual Image Processing Group. The enhancements to the MLP are based on (i) neuron activations functions that reduce the occurrences of singular Jacobians; (ii) successive regularization to constrain the volume of the weight space; and (iii) Boltzmann pruning to constrain the dimension of the weight space. Performance characterization studies of NN systems evaluated at the first OCR systems conference and the NIST form-based handprint recognition system are also summarized View full abstract»

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  • Automatic target recognition using a feature-decomposition and data-decomposition modular neural network

    Publication Year: 1998 , Page(s): 1113 - 1121
    Cited by:  Papers (5)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB)  

    A modular neural network classifier has been applied to the problem of automatic target recognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multiresolution features and by the introduction of a higher level neural network on the top of the individual networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data-decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of real FLIR images View full abstract»

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  • Target discrimination in synthetic aperture radar using artificial neural networks

    Publication Year: 1998 , Page(s): 1136 - 1149
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (508 KB)  

    This paper addresses target discrimination in synthetic aperture radar (SAR) imagery using linear and nonlinear adaptive networks. Neural networks are extensively used for pattern classification but here the goal is discrimination. We show that the two applications require different cost functions. We start by analyzing with a pattern recognition perspective the two-parameter constant false alarm rate (CFAR) detector which is widely utilized as a target detector in SAR. Then we generalize its principle to construct the quadratic gamma discriminator (QGD), a nonparametrically trained classifier based on local image intensity. The linear processing element of the QCD is further extended with nonlinearities yielding a multilayer perceptron (MLP) which we call the NL-QGD (nonlinear QGD). MLPs are normally trained based on the L2 norm. We experimentally show that the L2 norm is not recommended to train MLPs for discriminating targets in SAR. Inspired by the Neyman-Pearson criterion, we create a cost function based on a mixed norm to weight the false alarms and the missed detections differently. Mixed norms can easily be incorporated into the backpropagation algorithm, and lead to better performance. Several other norms (L8, cross-entropy) are applied to train the NL-QGD and all outperformed the L2 norm when validated by receiver operating characteristics (ROC) curves. The data sets are constructed from TABILS 24 ISAR targets embedded in 7 km2 of SAR imagery (MIT/LL mission 90) View full abstract»

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  • Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition

    Publication Year: 1998 , Page(s): 1223 - 1230
    Cited by:  Papers (12)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (256 KB)  

    This paper evaluates the performance of an image compression system based on wavelet-based subband decomposition and vector quantization. The images are decomposed using wavelet filters into a set of subbands with different resolutions corresponding to different frequency bands. The resulting subbands are vector quantized using the Linde-Buzo-Gray (1980) algorithm and various fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive neural network through an unsupervised learning process. The quality of the multiresolution codebooks designed by these algorithms is measured on the reconstructed images belonging to the training set used for multiresolution codebook design and the reconstructed images from a testing set View full abstract»

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  • Matching pursuit filters applied to face identification

    Publication Year: 1998 , Page(s): 1150 - 1164
    Cited by:  Papers (49)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB)  

    We present a face identification algorithm that automatically processes an unknown image by locating and identifying the face. The heart of the algorithm is the use of pursuit filters. A matching pursuit filter is an adapted wavelet expansion, where the expansion is adapted to both the data and the pattern recognition problem being addressed. For identification, the filters find the features that differentiate among faces, whereas, for detection, the filters encode the similarities among faces. The filters are designed though a simultaneous decomposition of a training set into a two-dimensional (2-D) wavelet expansion. This yields a representation that is explicitly 2-D and encodes information locally. The algorithm uses coarse to fine processing to locate a small set of key facial features, which are restricted to the nose and eye regions of the Face. The result is an algorithm that is robust to variations in facial expression, hair style, and the surrounding environment. Based on the locations of the facial features, the identification module searches the data base for the identity of the unknown face using matching pursuit filters to make the identification. The algorithm was demonstrated on three sets of images. The first set was images from the FERET data base. The second set was infrared and visible images of the same people. This demonstration was done to compare performance on infrared and visible images individually, and on fusing the results from both modalities. The third set was mugshot data from a law enforcement application View full abstract»

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  • Use of nonlinear principal component analysis and vector quantization for image coding

    Publication Year: 1998 , Page(s): 1218 - 1223
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    The nonlinear principal component analysis (NLPCA) method is combined with vector quantization for the coding of images. The NLPCA is realized using the backpropagation neural network (NN), while vector quantization is performed using the learning vector quantizer (LVQ) NN. The effects of quantization in the quality of the reconstructed images are then compensated by using a novel codebook vector optimization procedure View full abstract»

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  • Fast road classification and orientation estimation using omni-view images and neural networks

    Publication Year: 1998 , Page(s): 1182 - 1197
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB)  

    This paper presents the results of integrating omnidirectional view image analysis and a set of adaptive backpropagation networks to understand the outdoor road scene by a mobile robot. Both the road orientations used for robot heading and the road categories used for robot localization are determined by the integrated system, the road understanding neural networks (RUNN). Classification is performed before orientation estimation so that the system can deal with road images with different types effectively and efficiently. An omni-view image (OVI) sensor captures images with 360 degree view around the robot in real-time. The rotation-invariant image features are extracted by a series of image transformations, and serve as the inputs of a road classification network (RCN). Each road category has its own road orientation network (RON), and the classification result (the road category) activates the corresponding RON to estimate the road orientation of the input image. Several design issues, including the network model, the selection of input data, the number of the hidden units, and learning problems are studied. The internal representations of the networks are carefully analyzed. Experimental results with real scene images show that the method is fast and robust View full abstract»

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Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

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Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003