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learning to Predict Visibility and Invisibility from Occlusion Events 8

Learning to Predict Visibility and Invisibility from Occlusi...

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8th international conference on Neural Information Processing Systems, NIPS 1995

作者： Marshall, Jonathan A. Alley, Richard K. Hubbard, Robert S. Department of Computer Science CB 3175 Sitterson Hall University of North Carolina Chapel HillNC27599-3175 United States

Visual occlusion events constitute a major source of depth information. this paper presents a self-organizing neural network that learns to detect, represent, and predict the visibility and invisibility relationships that arise during occlusion events, after a period of exposure to motion sequences containing occlusion and disocclusion events. the network develops two parallel opponent channels or "chains" of lateral excitatory connections for every resolvable motion trajectory. One channel, the "On" chain or 'Visible" chain, is activated when a moving stimulus is visible. the other channel, the "Off" chain or "invisible" chain, carries a persistent, amodal representation that predicts the motion of a formerly visible stimulus that becomes invisible due to occlusion. the learning rule uses disinhibition from the On chain to trigger learning in the Off chain. the On and Off chain neurons can learn separate associations with object depth ordering. the results are closely related to the recent discovery (Assad & Maunsell, 1995) of neurons in macaque monkey posterior parietal cortex that respond selectively to inferred motion of invisible stimuli. © NIPS 1995: Proceedings of the 8th international conference on Neural Information Processing Systems. All rights reserved.

关键词： Visibility

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Partial-discharge diagnosis with artificial neural networks

Partial-discharge diagnosis with artificial neural networks

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international conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： R. Badent K. Kist N. Lewald A.J. Schwab Institute of Electric Energy Systems and High-Voltage Technology University of Karlsruhe Germany

the new diagnosis method employs a classical PD measurement system consisting of a coupling capacitor, measuring impedance, and a "wideband" integrator, cascaded by an artificial network evaluation. Upon passing a polarity detection unit, the output signal of the "wideband" integrator is recorded via a digital storage oscilloscope which simultaneously serves as an interface to the subsequent computer-aided evaluation. the personal computer stores the PD-values in a phase resolving PD-matrix. After sufficient learning with training matrices the system recognizes different fault types with high probability. the recognition likelihood of trained patterns is almost 100 percent and of a new pattern approximately 90 percent, depending on both the number of training matrices and the repetition rate. the implemented artificial neural network is composed of a three layer backpropagation algorithm with threshold units and a recognition volume of up to 16 fault types. To guarantee the highest individual detection rate, each fault type must be trained with the same number of matrices. thereafter, the network is able to recognize previously learned fault types without any other data pre- or post-processing, i.e. the diagnosis system relies exclusively on pattern recognition.< >

关键词： Artificial neural networks pattern recognition Impedance measurement Capacitors Oscilloscopes Computer interfaces Microcomputers Signal resolution Backpropagation algorithms Fault detection

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CLUSTERING WIth UNSUPERVISED learning NEURAL NETWORKS - A COMPARATIVE-STUDY 4

CLUSTERING WITH UNSUPERVISED LEARNING NEURAL NETWORKS - A CO...

引用

4th international conference on Applications of Artificial Neural Networks

作者： WANN, CD thOMOPOULOS, SCA PENN STATE UNIV DEPT ELECT & COMP ENGNDECIS & CONTROL SYST LABUNIV PKPA 16802

ISBN: (纸本)0819412015

A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. the architecture differences and learning procedures between these two models are compared. the performance of ART2 and DIGNET on data clustering and pattern recognition problems with noise or interference is investigated by computer simulations. It is shown that DIGNET generally has faster learning and better clustering performance on the statistical pattern recognition problems. DIGNET has a simpler architecture, and the system parameters can be analytically determined from the self- organizing process. the threshold value used in DIGNET can be specifically determined from a given lower bound on the desirable signal-to-noise ratio (SNR). A modified model based on the features of ART2 and DIGNET is also derived and investigated. the simpler architecture combines the ART2 structure with the advantages of DIGNET model. the concepts of well depth and stage age originally introduced in DIGNET are applied in the modified model. the modified model preserves the features of noise suppression, contrast enhancement and self- organizing stable pattern recognition of ART2, yet provides a specific method to adjust parameters in the network. the network performs a variant of K-means learning, but without the knowledge of a priori information on the actual number of clusters. the networks discussed in this paper are applied and benchmarked against clustering and pattern recognition problems. Comparative simulation results of the networks are also presented.

关键词： Signal to noise ratio Performance modeling Systems modeling Artificial neural networks pattern recognition Computer simulations machine learning Image classification Neural networks Detection and tracking algorithms

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MINIMUM DESCRIPTION LENGth BASED 2D SHAPE-DESCRIPTION

MINIMUM DESCRIPTION LENGTH BASED 2D SHAPE-DESCRIPTION

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4th international CONF ON COMPUTER VISION ( ICCV 93 )

作者： LI, MX CVAP Stockholm Sweden

ISBN: (纸本)0818638702

In this paper, we deal with the problem of 2D shape description, in particular, with contour partitioning, grouping, and classification in terms of straight and curved, based on the Minimum Description Length (MDL) criterion and shape (line, ellipse) fitting techniques. Using the MDL criterion, we can derive, for a given data set and a class of models, a description which best explains the data. We also develop a new algorithm for fitting 2D points to ellipse.

关键词： pattern recognition

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recognition OF OBJECT CLASSES FROM RANGE data

RECOGNITION OF OBJECT CLASSES FROM RANGE DATA

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4th international CONF ON COMPUTER VISION ( ICCV 93 )

作者： REID, ID BRADY, JM Univ of Oxford United Kingdom

ISBN: (纸本)0818638702

We develop techniques for recognizing instances of 3D object classes (which may consist of multiple and/or repeated subparts with internal degrees of freedom, linked by parameterized transformations), from sets of 3D features observations. recognition of a class instance is structured as a search of an interpretation tree in which geometric constraints on pairs of sensed features not only prune the tree, but are used to determine upper and lower bounds on the model parameter values of the instance. A real-valued constraint propagation network unifies the representations of the model parameters, model constraints and feature constraints, and provides a simple and effective mechanism for accessing and updating parameter values. recognition of objects with multiple internal degrees of freedom, including non-uniform scaling and stretching, articulations, and subpart repetitions, is demonstrated for two different types of real range data: 3D edge fragments from a stereo vision system, and position/surface normal data derived from planar patches extracted from a range image.

关键词： pattern recognition

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pattern thEORETIC IMAGE-RESTORATION 4

PATTERN THEORETIC IMAGE-RESTORATION

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4th CONF ON NONLINEAR IMAGE PROCESSING

作者： BREEN, MA ROSS, TD NOVISKEY, MJ AXTELL, ML Tennessee Technological Univ. (United States) Air Force Wright Lab. (United States) Veda Inc. (United States)

ISBN: (纸本)0819411353

pattern theory is a combination of pattern recognition, machine learning, switching theory, and computational complexity technologies with the central theme that the pattern in a function can be found by minimizing the complexity of a particular generalized representation. the sense of `pattern' used in pattern theory has been demonstrated to be very robust. this paper develops a pattern theoretic approach to image restoration. We assume that an original, patterned, binary image has been corrupted by additive noise and is given as a gray-scale image. the decision theoretic approach to restoration would be simply to threshold the gray- scale image to regain a binary image. the pattern theoretic approach is to use two thresholds. these thresholds separate the pixels into three classes: pixels that were very probably white, pixels that were very probably black, and pixels that we are less certain about. We then use only those pixels that we are confident about and find the pattern based on those pixels. Finally, we use this pattern to extrapolate through the pixels that are uncertain. the amount of noise that can be abated depends on the strength of the underlying pattern. this relationship is developed for uniform and normal noise distributions.

关键词： Image restoration Binary data Signal to noise ratio Image processing Nonlinear image processing Statistical analysis Interference (communication) Switching machine learning pattern recognition

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SHAPE FROM TEXTURE FROM A MULTISCALE PERSPECTIVE

SHAPE FROM TEXTURE FROM A MULTISCALE PERSPECTIVE

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4th international CONF ON COMPUTER VISION ( ICCV 93 )

作者： LINDEBERG, T GARDING, J Computational Vision and Active Perception Laboratory (CVAP) Stockholm Sweden

ISBN: (纸本)0818638702

the problem of scale in shape from texture is addressed. the need for (at least) two scale parameters is emphasized;a local scale describing the amount of smoothing used for suppressing noise and irrelevant details when computing primitive texture descriptors from image data, and an integration scale describing the size of the region in space over which the statistics of the local descriptors is accumulated. A novel mechanism for automatic scale selection is proposed, based on normalized derivatives. the resulting texture description can be combined with various assumptions about surface texture in order to estimate local surface orientation. Two specific assumptions, 'weak isotropy' and 'constant area', ar explored in more detail. Experiments on real and synthetic reference data with known geometry demonstrate the viability of the approach.

关键词： pattern recognition

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learning methods for odor recognition modeling 4th

引用

4th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 1992

作者： Amghar, S. Paugam-Moisy, H. Royet, J.P. URA CNRS 934 43 Bd du 11 Novembre 1918 Villeurbanne69622 France URA CNRS 1398 LIP-ENS-Lyon 46 allée d’Italie Lyon Cedex69364 France URA CNRS 180 43 Bd du 11 Novembre 1918 Villeurbanne cedex69622 France

ISBN: (纸本)9783540567356

this paper presents a phenomenon modeling of human odor perception. For a recognition test by discrimination, odors are presented in couples. Pair odors are separated by an arbitrary defined time interval. Individuals have to recognize if the odor which is presented the second time is the same as, or different to the odor which was smelt the first time. recognition does not imply identification but only remains validated by success or check terms. For each tested odor, an evocation list is established by various individuals, hence a feature profil for each odor is available. From these data, the aim is to design a prediction model which determines the score for an individual uniquely from his odor evocation pattern. For modeling this phenomenon, we have applied learning methods especially neural networks. A process named "spy" was used to find the best architecture and the best parameters of a given neural network. this tool observes a net of neural networks, which all work in parallel, from the same sets of patterns, but which differ from one another in some of their parameters. In this communication, we also present a comparative study between neural network performances and those given by discriminant analysis which was applied to this odor memorizing problem. © Springer-Verlag Berlin Heidelberg 1993.

关键词： Odors

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A connectionist learning approach to analyzing linguistic stress 91

A connectionist learning approach to analyzing linguistic st...

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Proceedings of the 4th international conference on Neural Information Processing Systems

作者： Prahlad Gupta David S. Touretzky Department of Psychology Carnegie Mellon University Pittsburgh PA School of Computer Science Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781558602229

We use connectionist modeling to develop an analysis of stress systems in terms of ease of learnability. In traditional linguistic analyses, learnability arguments determine default parameter settings based on the feasibilty of logically deducing correct settings from an initial state. Our approach provides an empirical alternative to such arguments. Based on perceptron learning experiments using data from nineteen human languages, we develop a novel characterization of stress patterns in terms of six parameters. these provide both a partial description of the stress pattern itself and a prediction of its learnability, without invoking abstract theoretical constructs such as metrical feet. this work demonstrates that machine learning methods can provide a fresh approach to understanding linguistic phenomena.

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4th international conference on pattern recognition, 1988

4th International Conference on Pattern Recognition, 1988

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4th international conference on pattern recognition, 1988

ISBN: (纸本)9783540190363

the proceedings contain 66 papers. the special focus in this conference is on pattern recognition. the topics include: Change detection in digital imagery using the adaptive learning networks;image segmentation using causal Markov random field models;preditas — Software package for solving pattern recognition and diagnostic problems;processing poor quality line drawings by local estimation of noise;a color classification algorithm for color images;fuzzy set methods in pattern recognition;a fuzzy hybrid model for pattern classification;on the role of pattern in recognizer design;a statistical study in word recognition;an integrated image segmentation/image analysis system;feature extraction from line drawing images;syntax analysis in automated digitizing of maps;a fast binary template matching algorithm for document image data compression;recognition system for three-view mechanical drawings;a heuristic algorithm for stroke extraction;an analysis of methods for improving long-range connectivity in meshes;Implementation and use of software scanning on a small CLIP4 processor array;Performing global transforms on an SIMD machine;a parallel architecture for model-based object recognition;lapwing - A trainable image recognition system for the linear array processor;a fast algorithm for the automatic recognition of heat sources in satellite images;linguistic definition of generic models in computer vision;a multiple hypothesis rule-based automatic target recognizer;generic cueing in image understanding;knowledge-based approach for adaptive recognition of drawings;extended symbolic projections as a knowledge structure for spatial reasoning;Knowledge-based road network extraction on SPOT satellite images;median-based methods of corner detection;an efflcient Radon transform.

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