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检索条件"任意字段=3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007"

共 76 条记录，以下是61-70 订阅

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Epiflow based stereo fusion

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3rd iberian conference on pattern recognition and image analysis

作者： Zhang, Hongsheng Negahdaripour, Shahriar Mako Surg Corp Ft Lauderdale FL 33317 USA Univ Miami Coral Gables FL 33124 USA

ISBN: (纸本)9783540728467

3-D reconstruction from images sequences has been the center topic of computer vision. Real-time applications call for causal processing of stereo sequences, as they are acquired, covering different regions of the scene. The first step is to compute the current stereo disparity, and recursive map building often requires fusing with the previous estimate. In this paper, the epifflow framework [1], originally proposed for establishing matches among stereo feature pairs is generalized to devise an iterative causal algorithm for stereo disparity map fusion. In the context of disparity fusion, quadruplet correspondence of the epiflow tracking algorithm becomes reminiscent of the "closest point" of the 3-D ICP algorithm. Unlike ICP, the 2-D epiflow framework permits incorporating both photometric and geometrical constraints, estimation of the stereo rig motion as supplementary information, as well as identifying local inconsistencies between the two disparity maps. Experiments with real data validate the proposed approach, and improved converge compared to the ICP algorithm.

关键词： image reconstruction

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Inference of stochastic finite-state transducers using N-gram mixtures

Inference of stochastic finite-state transducers using N-gra...

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3rd iberian conference on pattern recognition and image analysis

作者： Alabau, Vicente Casacuberta, Francisco Vidal, Enrique Juan, Alfons Univ Politecn Valencia Dept Sistemes Informat & Computacio E-46071 Valencia Spain

ISBN: (纸本)9783540728481

Statistical pattern recognition has proved to be an interesting framework for machine translation, and stochastic finite-state transducers are adequate models in many language processing areas such as speech translation, computer-assisted translations, etc. The well-known n-gram language models are widely used in this framework for machine translation. One of the application of these n-gram models is to infer stochastic finite-state transducers. However, only simple dependencies can be modelled, but many translations require to take into account strong context and style dependencies. Mixtures of parametric models allow to increase the description power of the statistical models by modelling subclasses of objects. In this work, we propose the use of n-gram mixtures in GIATI, a procedure to infer stochastic finite-state transducers. N-gram mixtures are expected to model topics or writing styles. We present experimental results showing that translation performance can be improved if enough training data is available.

关键词： pattern recognition

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A neural network model for image change detection based on fuzzy cognitive maps

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3rd iberian conference on pattern recognition and image analysis

作者： Pajares, Gonzalo Sanchez-Beato, Alfonso Cruz, Jesus M. Ruz, Jose J. Univ Complutense Fac Informat Dpt Ingn Software & Inteligencia Artif E-28040 Madrid Spain ETS Informat UNED Dpt Informat & Automat E-28040 Madrid Spain Univ Complutense Fac Informat Dpt Arquitectura Comp & Automat E-28040 Madrid Spain

ISBN: (纸本)9783540728467

This paper outlines a neural network model based on the Fuzzy Cognitive Maps (FCM) framework for solving the automatic image change detection problem. Each pixel in the reference image is assumed to be a node in the network. Each node has associated a fuzzy value, which determines the magnitude of the change. Each fuzzy value is updated by a trade-off between the influences received from the fuzzy values from other neurons and its own fuzzy value. Classical approaches in the literature have been designed assuming that the mutual influences between two nodes are symmetric. The main finding of this paper is the assumption that mutual influences could not be symmetric. This non symmetric relationship can be embedded by the FCM paradigm. The performance of the proposed method is illustrated by comparative analysis against some recent image change detection methods.

关键词： Fuzzy control

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Shadow resistant road segmentation from a mobile monocular system

Shadow resistant road segmentation from a mobile monocular s...

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3rd iberian conference on pattern recognition and image analysis

作者： Alvarez, Jose Manuel Lopez, Antonio M. Baldrich, Ramon Univ Autonoma Barcelona Comp Vis Ctr Edif O E-08193 Barcelona Spain Univ Autonoma Barcelona Dept Comp Sci E-08193 Barcelona Spain

ISBN: (纸本)9783540728481

An essential functionality for advanced driver assistance systems (ADAS) is road segmentation, which directly supports ADAS applications like road departure warning and is an invaluable background segmentation stage for other functionalities as vehicle detection. Unfortunately, road segmentation is far from being trivial since the road is in an outdoor scenario imaged from a mobile platform. For instance, shadows are a relevant problem for segmentation. The usual approaches are ad hoe mechanisms, applied after an initial segmentation step, that try to recover road patches not included as segmented road for being in shadow. In this paper we argue that by using a different feature space to perform the segmentation we can minimize the problem of shadows from the very beginning. Rather than the usual segmentation in a color space we propose segmentation in a shadowless image which is computable in real-time using a color camera. The paper presents comparative results for both asphalted and non-asphalted roads, showing the benefits of the proposal in presence of shadows and vehicles.

关键词： image segmentation

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Development of a cascade processing method for microarray spot segmentation

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3rd iberian conference on pattern recognition and image analysis

作者： Daskalakis, Antonis Cavouras, Dionisis Bougioukos, Panagiotis Kostopoulos, Spiros Kalatzis, Ioannis Kagadis, George C. Nikiforidis, George Univ Patras Dept Med Phys Sch Med GR-26503 Rion Greece Technol Educ Inst Athens Med Signal & Image Processing Lab Dept Med Instrument Technol Athens 12210 Greece

ISBN: (纸本)9783540728467

A new method is proposed for improving microarray spot segmentation for gene quantification. The method introduces a novel combination of three image processing stages, applied locally to each spot image: i/ Fuzzy C-Means unsupervised clustering, for automatic spot background noise estimation, ii/ power spectrum deconvolution filter design, employing background noise information, for spot image restoration, iii/ Gradient-Vector-Flow (GVF-Snake), for spot boundary delineation. Microarray images used in this study comprised a publicly available dataset obtained from the database of the MicroArray Genome Imaging & Clustering Tool website. The proposed method performed better than the GVF-Snake algorithm (Kullback-Liebler metric: 0.0305 bits against 0.0194 bits) and the SPOT commercial software (pairwise mean absolute error between replicates: 0.234 against 0.303). Application of efficient adaptive spot-image restoration on cDNA microarray images improves spot segmentation and subsequent gene quantification.

关键词： Microarrays

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Bayesian oil spill segmentation of SAR images via graph cuts

Bayesian oil spill segmentation of SAR images via graph cuts

引用

3rd iberian conference on pattern recognition and image analysis

作者： Pelizzari, Sonia Bioucas-Dias, Jose M. Univ Tecn Lisboa IST Inst Telecomun P-1100 Lisbon Portugal

ISBN: (纸本)9783540728481

This paper extends and generalizes the Bayesian semi-supervised segmentation algorithm [1] for oil spill detection using SAR images. In the base algorithm on which we build on, the data term is modeled by a finite mixture of Gamma distributions. The prior is an M-level logistic Markov Randorn Field enforcing local continuity in a statistical sense. The methodology proposed in [1] assumes two classes and known smoothness parameter. The present work removes these restrictions. The smoothness parameter controlling the degree of homogeneity imposed on the scene is automatically estimated and the number of used classes is optional. Semi-automatic estimation of the class parameters is also implemented. The maximum a posteriori (MAP) segmentation is efficiently computed via the alpha-expansion algorithm [2], a recent Graph-cut technique, The effectiveness of the proposed approach is illustrated with simulated (Gaussian or Gamma data term and M-level logistic classes) and real ERS data.

关键词： image segmentation

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Robust automatic speech recognition using PD-MEEMLIN

Robust automatic speech recognition using PD-MEEMLIN

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3rd iberian conference on pattern recognition and image analysis

作者： Hernandez, Igmar Garcia, Paola Nolazco, Juan Buera, Luis Lleida, Eduardo Tecnol Monterrey Dept Comp Sci Campus Monterrey Monterrey Mexico Univ Zaragoza Commun Technol Grp GTC I3A Zaragoza Spain

ISBN: (纸本)9783540728481

This work presents a robust normalization technique by cascading a speech enhancement method followed by a feature vector normalization algorithm. To provide speech enhancement the Spectral Subtraction (SS) algorithm is used;this method reduces the effect of additive noise by performing a subtraction of the noise spectrum estimate over the complete speech spectrum. On the other hand, an empirical feature vector normalization technique known as PD-MEMLIN (Phoneme-Dependent Multi-Enviroment Models based LInear Normalization) has also shown to be effective. PD-MEMLIN models clean and noisy spaces employing Gaussian Mixture Models (GMMs), and estimates a set of linear compensation transformations to be used to clean the signal. The proper integration of both approaches is studied and the final design, PD-MEEMLIN (Phoneme-Dependent Multi-Enviroment Enhanced Models based LInear Normalization), confirms and improves the effectiveness of both approaches. The results obtained show that in very high degraded speech PD-MEEMLIN outperforms the SS by a range between 11.4 % and 34.5 %, and for PD-MEMLIN by a range between 11.7 % and 24.84 %. Furthemore, in moderate SNR, i.e. 15 or 20 dB, PD-MEEMLIN is as good as PD-MEMLIN and SS techniques.

关键词： Speech recognition

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Measuring the applicability of self-organization maps in a case-based reasoning system

Measuring the applicability of self-organization maps in a c...

引用

3rd iberian conference on pattern recognition and image analysis

作者： Fornells, A. Golobardes, E. Martorell, J. M. Garrell, J. M. Bernado, E. Macia, N. Ramon Llull Univ Res Grp Intelligent Syst Engn & Arquitectura La Quatre Camins 2 Barcelona 08022 Spain

ISBN: (纸本)9783540728481

Case-Based Reasoning (CBR) systems solve new problems using others which have been previously resolved. The knowledge is composed of a set of cases stored in a case memory, where each one describes a situation in terms of a set of features. Therefore, the size and organization of the case memory influences in the computational time needed to solve new situations. We organize the memory using Self-Organization Maps, which group cases with similar properties into patterns. Thus, CBR is able to do a selective retrieval using only the cases from the most suitable pattern. However, the data complexity may hinder the identification of patterns and it may degrade the accuracy rate. This work analyses the successful application of this approach by doing a previous data complexity characterization. Relationships between the performance and some measures of class separability and the discriminative power of attributes are also found.

关键词： statistical and structural pattern recognition data complexity neural networks self-organization maps case-based reasoning soft computing

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analysis of detecting the malarial parasite infected blood images using statistical based approach 3

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3rd Kuala Lumpur International conference on Biomedical Engineering 2006 (BioMed 2006)

作者： Raviraja, S. Bajpai, Gaurav Sharma, Sudhir Kumar Acad Med Sci & Technol Fac Comp Sci Khartoum Sudan Acad Med Sci & Technol Fac Biomed Engn Khartoum Sudan

ISBN: (纸本)9783540680161

This work introduces a blood image processing for detecting and classifying malarial parasites in images of Giemsa stained blood slides, in order to evaluate the parasitaemia of the blood. Generally blood images are made up of three different kinds of cells, red, white and blood platelets. Their dimension, shape and their color distinguish these. In malarial blood, the red corpuscles of vertebrates are infected by malarial parasites. The aim of this paper is to detect the red blood cells that are infected by malarial parasites using statistical based approach. Further evaluation of the size and shape of the nuclei of the parasite is also considered.

关键词： digital image processing pattern recognition shape analysis invariant moments malarial blood images

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Scalable evolutionary design of CA pattern classifier

Scalable evolutionary design of CA pattern classifier

引用

3rd Indian International conference on Artificial Intelligence, IICAI 2007

作者： Nath, Joy Deep Mitra, Pabitra Ganguly, Niloy Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9780972741224

The paper reports a scalable evolutionary design for pattern recognition using Multiple Attractor Cellular Automata (MACA). MACA helps to impart non-linearity in the classifier using Hamming distance based attractors. Isomorphism in MACA was exploited to make the method scalable to large classification problems involving non-linear boundaries. Extensive experimentation was performed on datasets with different topologies to establish the efficacy of the proposed method Abstract: compared to existing popular approaches like support vector machines. The classifier was applied to satellite image analysis problem. Experiments on different types of data sets were performed to discover the classifier's feature selection capabilities. Copyright © 2007 IICAI.

关键词： Cellular automata

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