The proceedings contain 64 papers. The special focus in this conference is on Segmentation, Detection, Object recognition, Computational Model and Attentive Vision. The topics include: A new framework for object categ...
ISBN:
(纸本)3540675604
The proceedings contain 64 papers. The special focus in this conference is on Segmentation, Detection, Object recognition, Computational Model and Attentive Vision. The topics include: A new framework for object categorization in cortex;the perception of spatial layout in a virtual world;towards a computational model for object recognition in it cortex;straight line detection as an optimization problem;an approach motivated by the jumping spider visual system;factorial code representation of faces for recognition;moving object segmentation based on human visual sensitivity;object classification using a fragment-based representation;confrontation of retinal adaptation model with key features of psychophysical gain behavior dynamics;polarization-based orientation in a natural environment;computation model of eye movement in reading using foveated vision;top-down attention control at feature space for robustpatternrecognition;development of a biologically inspired real-time visual attention system;heading perception and moving objects;curve inference and stereo correspondence;an efficient datastructure for feature extraction in a foveated environment;parallel trellis based stereo matching using constraints;unsupervised learning of biologically plausible object recognitionstrategies;face recognition under varying views;pose-independent object representation by 2-d views;an image enhancement technique based on wavelets;face reconstruction using a small set of feature points;a neural network model for long-range contour diffusion by visual cortex;automatic generation of photo-realistic mosaic image and face and facial landmarks location based on log-polar mapping.
作者：
Petrou, MUniv Surrey
Sch Elect Engn Informat Technol & Math Guildford GU2 5XH Surrey England
learning in the context of a patternrecognition system is defined as the process that allows it to cope with real and ambiguous data. The various ways by which artificial decision systems operate are discussed in con...
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ISBN:
(纸本)3540665994
learning in the context of a patternrecognition system is defined as the process that allows it to cope with real and ambiguous data. The various ways by which artificial decision systems operate are discussed in conjunction with their learning aspects.
We describe the guidelines of a system for monitoring environmental risk situations. The system is based on datamining techniques and in particular classification trees working on the data base collected by the Itali...
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ISBN:
(纸本)3540665994
We describe the guidelines of a system for monitoring environmental risk situations. The system is based on datamining techniques and in particular classification trees working on the data base collected by the Italian National Hydro-geological Net. The gear of our application is to achieve a better discrimination among cases then that obtained by the system which is presently in use. The decision trees are evaluated and selected via a metric that takes a weighted account of the errors of different kinds.
作者：
Jahn, HDLR
Deutsch Zentrum Luft & Raumfahrt EV Inst Weltraumsensor & Planetenerkundung D-12489 Berlin Germany
A parallel-sequential unsupervised learning method for image smoothing is presented which can be implemented with a Multi Layer Neural Network. In contrast to older work of the author which has used 4-connectivity of ...
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ISBN:
(纸本)3540665994
A parallel-sequential unsupervised learning method for image smoothing is presented which can be implemented with a Multi Layer Neural Network. In contrast to older work of the author which has used 4-connectivity of processing elements (neurons) leading to a very big number of recursions now each neuron of network lever t+1 is connected with (2M+1)*(2M+1) neurons of layer t guaranteeing a significant reduction of network layers with the same good smoothing results.
This paper presents a method for concept formation of a personal learning apprentice (PLA) system that attempts to capture users' internal conceptual structure by observing interactions between user and system. Cu...
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ISBN:
(纸本)3540665994
This paper presents a method for concept formation of a personal learning apprentice (PLA) system that attempts to capture users' internal conceptual structure by observing interactions between user and system. Current hot topics on techniques of datamining may potentially contribute to the above purpose, but different from the conventional approaches of datamining, we have to consider more about the aspects in which hom the mined knowledge should be used by the human in the consequent processes, not only about what knowledge should be extracted. In this paper we propose such a process-oriented datamining method based upon an idea of soft systems methodologies proposed by P.B. Checkland in 1980's, and we propose an algorithm for its implementation using evolutional computing.
In the field of patternrecognition, multiple classifier systems based on the combination of the outputs of a set of different classifiers have been proposed as a method for the development of high performance classif...
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ISBN:
(纸本)3540665994
In the field of patternrecognition, multiple classifier systems based on the combination of the outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them make independent errors. This achievement pointed out the fundamental need for methods aimed to design ensembles of "independent" classifiers. However, the most of the recent work focused on the development of combination methods. In this paper, an approach to the automatic design of multiple classifier systems based on unsupervised learning is proposed. Given an initial set of classifiers, such approach is aimed to identify the largest subset of "independent" classifiers. A proof of the optimality of the proposed approach is given. Reported results on the classification of remote sensing images show that this approach allows one to design effective multiple classifier systems.
This paper concerns a novel application of machinelearning to Magnetic Resonance Imaging (MRI) by considering Neural Network models for the problem of image estimation from sparsely sampled k-space. Effective solutio...
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ISBN:
(纸本)3540665994
This paper concerns a novel application of machinelearning to Magnetic Resonance Imaging (MRI) by considering Neural Network models for the problem of image estimation from sparsely sampled k-space. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to reduce the measurement time by omitting as many scanning trajectories as possible. This approach, however, entails underdetermined equations and leads to poor image reconstruction. It is proposed here that significant improvements could be achieved concerning image reconstruction if a procedure, based on machinelearning, for estimating the missing samples of complex k-space were introduced. To this end, the viability of involving Supervised and Unsupervised Neural Network algorithms for such a problem is considered and it is found that their image reconstruction results are very favorably compared to the ones obtained by the trivial zero-filled k-space approach or traditional more sophisticated interpolation approaches.
WISDOM++ is an intelligent document processing system that transforms a paper document into HTML/XML format. The main design requirement is adaptivity, which is realized through the application of machinelearning met...
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Predictive models have been widely used long before the development of the new field that we call datamining. Expanding application demand for datamining of ever increasing data warehouses, and the need for understa...
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Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defi...
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