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Reject mechanisms for massively parallel neural network character recognition systems

Reject mechanisms for massively parallel neural network char...

引用

neural and stochastic methods in image and signal processing

作者： Garris, Michael D. Wilson, Charles L. Natl. Inst. of Standards and Technology Gaithersburg MD USA

ISBN: (纸本)0819409391

Two reject mechanisms are compared using a massively parallel character recognition system implemented at NIST. The recognition system was designed to study the feasibility of automatically recognizing hand-printed text in a loosely constrained environment. The first method is a simple scalar threshold on the output activation of the winning neurode from the character classifier network. The second method uses an additional neural network trained on all outputs from the character classifier network to accept or reject assigned classifications. The neural network rejection method was expected to perform with greater accuracy than the scalar threshold method, but this was not supported by the test results presented. The scalar threshold method, even though arbitrary, is shown to be a viable reject mechanism for use with neural network character classifiers. Upon studying the performance of the neural network rejection method, analyses show that the two neural networks, the character classifier network and the rejection network, perform very similarly. This can be explained by the strong non-linear function of the character classifier network which effectively removes most of the correlation between character accuracy and all activations other than the winning activation. This suggests that any effective rejection network must receive information from the system which has not been filtered through the non-linear classifier.

关键词： Character recognition

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Modified genetic algorithm for extracting thermal profiles from infrared image data

Modified genetic algorithm for extracting thermal profiles f...

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neural and stochastic methods in image and signal processing

作者： Allred, Lloyd G. Kelly, Gary E. Air Force Ogden Air Logistics Cent. Hill AFB UT USA

ISBN: (纸本)0819409391

Analysis of the thermal profile of an electronic circuit card during warm-up can be useful in detecting malfunctioning components on the card. Extracting the thermal profile can require the processing of 10 to 15 images of 600,000 bytes each. By extracting the heat transient associated with the heat sources on the circuit card, this problem of characterizing the thermal transient can be reduced to one of modeling the peak temperatures associated with a handful of components. The thermal profile of each component can be modeled as a function of four parameters: three are functions of the heat dissipation characteristics of the circuit card, and the fourth is proportional to the power consumption of the component generating the heat. Extraction of the parameters was achieved through a modified genetic algorithm. The genetic algorithm was employed when traditional techniques of Newton, non-linear regression, gradient search, and binary search proved to be slow, unstable, and unreliable. In traditional genetic algorithm implementations, improvement in performance ceases when improvement requires a simultaneous mutation of two or more variables. We seem to have circumvented the difficulty by expressing the problem in the differential domain, and coupling the genetic algorithm with a cooperative `follow the leader' approach to optimization. The extracted power consumption parameters are then employed to distinguish between `good' and `bad' cards.

关键词： image analysis

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Topological separation versus weight sharing in neural net optimization

Topological separation versus weight sharing in neural net o...

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neural and stochastic methods in image and signal processing

作者： Omidvar, Omid M. Wilson, Charles L. Univ. of the District of Columbia Washington DC USA

ISBN: (纸本)0819409391

Recent advances in neural networks application development for real life problems have drawn attention to network optimization. Most of the known optimization methods rely heavily on a weight sharing concept for pattern separation and recognition. The shortcoming of the weight sharing method is attributed to a large number of extraneous weights which play a minimal role in pattern separation and recognition. Our experiments have shown that up to 97% of the connections in the network can be eliminated with little or no change in the network performance. Topological separation should be used when the size of the network is large enough to tackle real life problems such as fingerprint classification. Our research has focused on the network topology by changing the number of connections as secondary method of optimization. Our findings so far indicate that for large networks topological separation yields smaller network size which is more suitable for VLSI implementation. Topological separation is based on the error surface and information content of the network. As such it is an economical way of size reduction which leads to overall optimization. The differential pruning of the connections is based on the weight contents rather than number of connections. The training error may vary with the topological dynamics but the correlation between the error surface and recognition rate decreases to a minimum. Topological separation reduces the size of the network by changing its architecture without degrading its performance. The method also results in a network which is considerably smaller in size with a better performance.

关键词： neural networks

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Two modified crossover and mutation operators for image segmentation by genetic algorithms

Two modified crossover and mutation operators for image segm...

引用

neural and stochastic methods in image and signal processing

作者： Seetharaman, Guna S. Prabhu, Obili S. Narasimhan, Anand Univ. of Southwestern Louisiana Lafayette LA USA

ISBN: (纸本)0819409391

Segmentation of an image refers to partitioning the image into several subimages such that each subimage forms a connected component representing a logical entity present in the scene, and all the segments as a whole produce a meaningful interpretation of the scene being studied. The problem is inherently of NP nature and it is as hard as the simplest possible NP- complete problem called partition. Existence of the unique solution (the truly optimal segmentation) and its sensitivity to the sampling process is yet to be studied thoroughly. The formulation and implementation of a randomized search approach to segment an image, using genetic algorithms, is presented in this paper. A state space representation of partially segmented image using binary strings is considered. The dominant substrings are easily explained in terms of chromosomes. Also the operations such as crossover and mutations are easily abstracted. A modified crossover operator using boundary interaction and region adjacency graph (BIRAG) has been adopted to improve the performance. Also, a simplified mutation operator called `switch' has been devised using the BIRAG. In particular, the specific data structure used in this scheme also facilitates a means for accommodating pixel- level feed back, and model based bias for model based segmentation of images. images from two different scenes are segmented using this approach to illustrate the applicability of such a system.

关键词： image analysis

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Adaptive image segmentation by quantization

Adaptive image segmentation by quantization

引用

neural and stochastic methods in image and signal processing

作者： Liu, Hui Yun, David Y. Univ. of Hawaii/Manoa Honolulu HI USA

ISBN: (纸本)0819409391

Segmentation of images into textural homogeneous regions is a fundamental problem in an image understanding system. Most region-oriented segmentation approaches suffer from the problem of different thresholds selecting for different images. In this paper an adaptive image segmentation based on vector quantization is presented. It automatically segments images without preset thresholds. The approach contains a feature extraction module and a two-layer hierarchical clustering module, a vector quantizer (VQ) implemented by a competitive learning neural network in the first layer. A near-optimal competitive learning algorithm (NOLA) is employed to train the vector quantizer. NOLA combines the advantages of both Kohonen self- organizing feature map (KSFM) and K-means clustering algorithm. After the VQ is trained, the weights of the network and the number of input vectors clustered by each neuron form a 3- D topological feature map with separable hills aggregated by similar vectors. This overcomes the inability to visualize the geometric properties of data in a high-dimensional space for most other clustering algorithms. The second clustering algorithm operates in the feature map instead of the input set itself. Since the number of units in the feature map is much less than the number of feature vectors in the feature set, it is easy to check all peaks and find the `correct' number of clusters, also a key problem in current clustering techniques. In the experiments, we compare our algorithm with K-means clustering method on a variety of images. The results show that our algorithm achieves better performance.

关键词： image analysis

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Mean-field phase transitions for Gibbs random fields

Mean-field phase transitions for Gibbs random fields

引用

neural and stochastic methods in image and signal processing

作者： Elfadel, Ibrahim M. Yuille, Alan L. Massachusetts Inst. of Technology Cambridge MA USA

ISBN: (纸本)0819409391

The use of Gibbs random fields (GRF) to model images poses the important problem of the dependence of the patterns sampled from the Gibbs distribution on its parameters. Sudden changes in these patterns as the parameters are varied are known as phase transitions. In this paper, we concentrate on developing a general deterministic theory for the study of phase transitions when a single parameter, namely the temperature, is varied. This deterministic framework is based on a widely used technique in statistical physics known as the mean-field approximation. Our mean-field theory is general in that it is valid for any number of graylevels, any interaction potential, any neighborhood structure or size, and any set of constraints imposed on the desired images. The mean-field approximation is used to compute closed-form estimates for the critical temperatures at which phase transitions occur for two texture models widely used in the image modeling literature: the Potts model and the autobinomial model. The mean-field model allows us to predict the pattern structure in the neighborhood of these temperatures. These analytical results are verified by computer simulations using a novel mean-field descent algorithm.

关键词： image analysis

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Learning structural and corruption information from samples for Markov-random-field edge detection enhancement

Learning structural and corruption information from samples ...

引用

neural and stochastic methods in image and signal processing

作者： Milun, Davin Sher, David B. SUNY/Buffalo Buffalo NY USA

ISBN: (纸本)0819409391

We have advanced Markov random field research by addressing the issue of obtaining a reasonable, non-trivial, noise model. We have introduced the concept of a double neighborhood MRF. In the past we have estimated MRF probabilities by sampling neighborhood frequencies from images. Now we address the issue of noise models by sampling from pairs of original images together with noisy imagery. Thus we create a probability density function for pairs of neighborhoods across both images. This models the noise within the MRF probability density function without having to make assumptions about its form. This provides an easy way to generate Markov random fields for annealing or other relaxation methods. We have successfully applied this technique, combined with a technique of Hancock and Kittler which adds theoretical noise to an MRF density function, to the problem binary image reconstruction. We now apply it to edge detection enhancement of artificial images. We train the double neighborhood MRF on true edge-maps and edge-maps generated as output of a Sobel edge detector. Our method improves the generated edge-maps - - visually, and using the metrics of number of bits incorrect, and Pratt's figure of merit for edge detectors. We have also successfully improved the output edge-maps of some real images.

关键词： image analysis

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Data fusion through fuzzy reasoning applied to segmentation of multisensory images

Data fusion through fuzzy reasoning applied to segmentation ...

引用

neural and stochastic methods in image and signal processing

作者： Abdulghafour, Muhamad Chandra, T. Abidi, Mongi A. Univ. of Tennessee/Knoxville Knoxville TN USA

ISBN: (纸本)0819409391

Multi-sensor systems provide a purposeful description of the environment that a single sensor cannot offer. Fusing several types of data enhances the recognition capability of a robotic system and yields more meaningful information otherwise unavailable or difficult to acquire by a single sensory modality. Because observations provided by sensors are uncertain, incomplete, and/or imprecise, we adopted the use of the theory of fuzzy sets as a general framework to combine uncertain measurements. We developed a fusion formula based on the measure of fuzziness. This fusion formula satisfies several desirable properties. We established a fuzzification scheme by which different types of input data (images) are modeled. This process is essential in providing suitable predictions and explanations of a set of observations in a given environment. After fusion, a defuzzification scheme is carried out to recover crisp data from the combined fuzzy assessments. This approach was implemented and tested with real range and intensity images acquired using an Odetics Range Finder. The goal is to obtain better scene descriptions through a segmentation process of both images. Despite the low resolution of the images and the amount of noise present, the segmented output picture is suitable for recognition purposes.

关键词： image analysis

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Local estimation of visual signal translation using modulated wavelet transforms

Local estimation of visual signal translation using modulate...

引用

neural and stochastic methods in image and signal processing

作者： Eagleson, Roy A. Univ. of Western Ontario London Ontario Canada

ISBN: (纸本)0819409391

In computational perception, `visual motion analysis' is most commonly identified with the problem of measuring the infinitesimal rate of translation at various local spatial neighborhoods in a time-varying signal. Many problems associated with measuring these motion vectors can be addressed by considering the following simplified one-dimensional case. Given two samples, an original function fo(x), and another sample ft(x) taken momentarily afterwards;compute the translation parameter τ which provides a best-fit for the transformation model, Tτ : fo(x) &rarr ft(x) = fo(x + τ) over some finite local region. The `goodness' of this fit requires evaluation by a suitable performance metric since measurement uncertainty and added noise will corrupt the solution of τ. This error can be reduced if the measurement is supported by a wider spatial region. However, the `pure translation' model is usually only valid within some small local neighborhood. These two competing constraints inherently compromise the measurement process. In this paper, a new technique is developed for estimating this translation parameter using a localized (`wavelet') representation, and it provides a measure of the uncertainty of the resulting estimate. In addition, a trade-off is identified between the local neighborhood width and the uncertainty of the translation estimate. It is similar to the well-known Heisenberg uncertainty principle: The product of the variances of the uncertainty of position and translation is bounded below by a finite constant.

关键词： Computer vision

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image recognition using a neural network

Image recognition using a neural network

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IEEE Workshop on neural Networks for signal processing

作者： K.-C. Ho B.-C. Chieu Department of Electronic Engineering National Taiwan Institute of Technology Taipei Taiwan

A new type of feedforward neural network for recognition of MRF (Markov random field) images is presented. The proposed forward and backward networks are essentially generalizations of the forward and backward procedures in backpropagation training for general feedforward networks. Due to the feedforward structure of the networks, they are recurrent for homogeneous MRF images and easy to implement. Because of the use of the maximum-likelihood criterion, this approach always performs well if all classes of images are equally likely. Basically, the proposed approach takes advantage of the feedforward neural networks and, by the joint probability, solves two basic problems in MRF modeling: how to measure a Gibbs distribution and how to estimate the Gibbs parameters from clean and noisy MRF samples.< >

关键词： image recognition neural networks Feedforward neural networks Maximum likelihood estimation Feedforward systems Computer networks stochastic processes Markov random fields Error analysis Distributed computing

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