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9th international conference on Image Analysis and Processing, ICIAP 1997

作者： Wendling, Laurent Desachy, Jacky Paries, Alain IRIT 118route de Narbonne Toulouse Cedex31062 France Université Bordeaux I LaBRI 351avenue de la Libération Talence Cedex33405 France

ISBN: (纸本)3540635076

In this paper, a method of pattern recognition based on images split into a set of trees composed of fuzzy regions is presented. First, a fuzzy segmentation based on possibilistic c-means is carried out in the raster image. Fuzzy support have been defined from a first level cut. On each cluster, a fuzzy region is assumed to be a convex combination of sets with associated features. A set of sample trees is achieved from the application of the segmentation algorithm on characteristic objects. then, a tree isomorphism to recognize is defined to recognize an object. At last, a new tree compression method is introduced to decrease the complexity when we have to manage with a large set of trees. © Springer-Verlag Berlin Heidelberg 1997.

关键词： pattern recognition

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9th international conference on Image Analysis and Processing, ICIAP 1997

9th International Conference on Image Analysis and Processin...

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9th international conference on Image Analysis and Processing, ICIAP 1997

ISBN: (纸本)3540635076

the proceedings contain 88 papers. the special focus in this conference is on Segmentation, Image Analysis and pattern recognition. the topics include: Challenges and opportunities for PRandCV research in year 2000 and beyond;multi-scale gradient magnitude watershed segmentation;segmentation of multispectral images of works of art through principal component analysis;multiscale edge detection via normal changes;extending adjacency to fuzzy sets for coping with imprecise image objects;adaptive selection of image classifiers;classification reliability and its use in multi-classifier systems;color linear model;a computational approach to color illusions;improved textured images segmentation using an energy functional;contribution to the colour segmentation by means of an algorithm which reduces the CCDs saturation problems;pyramid-based multi-sensor image data fusion with enhancement of textural features;texture analysis using pairwise interaction maps;estimation of the color image gradient with perceptual attributes and contour line extraction from color images of scanned maps.

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A fast agglomerative community detection method for protein complex discovery in protein interaction networks

A fast agglomerative community detection method for protein ...

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8th iapr international conference on pattern recognition in bioinformatics, prib 2013

作者： Rahman, Mohammad S. Ngom, Alioune School of Computer Sciences University of Windsor 5115 Lambton Tower Windsor ON N9B 3P4 Canada

ISBN: (纸本)9783642391583

Proteins are known to interact with each other by forming protein complexes and in order to perform specific biological functions. Many community detection methods have been devised for the discovery of protein complexes in protein interaction networks. One common problem in current agglomerative community detection approaches is that vertices with just one neighbor are often classified as separate clusters, which does not make sense for complex identification. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large protein interaction networks (PINs). In this paper, we propose a new agglomerative algorithm, FAC-PIN, based on a local premetric of relative vertex-to-vertex clustering value and which addresses the above two issues. Our proposed FAC-PIN method is applied to eight PINs from different species, and the identified complexes are validated using experimentally verified complexes. the preliminary computational results show that FAC-PIN can discover protein complexes from PINs more accurately and faster than the HC-PIN and CNM algorithms, the current state-of-the-art agglomerative approaches to complex prediction. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Computational efficiency

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the relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

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BMC bioinformatics 2015年第4期16卷 1-14页

作者： Ibrahim, Zina M. Ngom, Alioune Kings Coll London Inst Psychiat London SE5 8AF England NIHR Biomed Res Ctr Mental Hlth South London England Biomed Res Unit Dementia South London England Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Maudsley NHS Fdn Trust London SE5 8AZ England

Background: Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data. Results: We propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data. Conclusions: Our proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner.

关键词： Protein Complexes Weighted Networks Functional Modules Network Clustering Criterion Protein Interaction Networks

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Bayesian optimization algorithm for the non-unique oligonucleotide probe selection problem

Bayesian optimization algorithm for the non-unique oligonucl...

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4th iapr international conference on pattern recognition in bioinformatics, prib 2009

作者： Soltan Ghoraie, Laleh Gras, Robin Wang, Lili Ngom, Alioune Bioinformatics and PRML Lab Department of Computer Science University of Windsor 401 Sunset Ave. Windsor ON N9B 3P4 Canada

ISBN: (纸本)3642040306

DNA microarrays are used in order to recognize the presence or absence of different biological components (targets) in a sample. therefore, the design of the microarrays which includes selecting short Oligonucleotide sequences (probes) to be affixed on the surface of the microarray becomes a major issue. this paper focuses on the problem of computing the minimal set of probes which is able to identify each target of a sample, referred to as Non-unique Oligonucleotide Probe Selection. We present the application of an Estimation of Distribution Algorithm (EDA) named Bayesian Optimization Algorithm (BOA) to this problem, for the first time. the presented approach considers integration of BOA and state-of-the-art heuristics introduced for the non-unique probe selection problem. this approach provides results that compare favorably with the state-of-the-art methods. It is also able to provide biologists with more information about the dependencies between the probe sequences of each dataset. © 2009 Springer Berlin Heidelberg.

关键词： Probes

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Analysis situs and image processing 9th

Analysis situs and image processing

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9th international conference on Image Analysis and Processing, ICIAP 1997

作者： Sloboda, Fridrich Zat'ko, Bedrich Institute of Control Theory and Robotics Slovak Academy of Sciences Dúbravska cesta 9 Bratislava842 37 Slovakia

ISBN: (纸本)3540635076

In the paper a topological approach to approximation of planar Jordan curves and arcs is described. the approximation is based on the basic notions of intrinsic geometry of metric spaces: on the notion of a shortest path in a polygonaly bounded compact set and on the notion of a geodesic diameter of a polygon. Furthermore, a new linear time algorithm for the shortest path problem solution is described, and the approximation of the most important characteristic set in image processing is shown. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Clustering algorithms

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Joint iapr international Workshops Structural and Syntactic pattern recognition, SSPR 2002 and Statistical Techniques in pattern recognition, SPR 2002

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Joint iapr 9th international Workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th international Workshop on Statistical Techniques in pattern recognition, SPR 2002

ISBN: (纸本)3540440119

the proceedings contain 90 papers. the special focus in this conference is on Graphs, Languages, Strings and Grammars. the topics include: Spectral methods for view-based 3-D object recognition using silhouettes;machine learning for sequential data;graph-based methods for vision;reducing the computational cost of computing approximated median strings;tree k-grammar models for natural language modelling and parsing;algorithms for learning function distinguishable regular languages;non-bayesian graph matching without explicit compatibility calculations;spectral feature vectors for graph clustering;identification of diatoms by grid graph matching;string edit distance, random walks and graph matching;learning structural variations in shock trees;a comparison of algorithms for maximum common subgraph on randomly connected graphs;inexact multisub graph matching using graph eigenspace and clustering models;optimal lower bound for generalized median problems in metric space;structural description to recognising arabic characters using decision tree learning techniques;feature approach for printed document image analysis;example-driven graphics recognition;estimation of texels for regular mosaics using model-based interaction maps;using graph search techniques for contextual colour retrieval;comparing shape and temporal PDMs;linear shape recognition with mixtures of point distribution models;curvature weighted evidence combination for shape-from-shading;probabilistic decisions in production nets;an application of machine learning techniques for the classification of glaucomatous progression;estimating the joint probability distribution of random vertices and arcs by means of second-order random graphs.

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

9th IAPR TC3 International Workshop on Artificial Neural Net...

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

ISBN: (纸本)9783030583088

the proceedings contain 24 papers. the special focus in this conference is on Artificial Neural Networks in pattern recognition. the topics include: Named entity disambiguation at scale;geometric attention for prediction of differential properties in 3d point clouds;preface;deep learning methods for image guidance in radiation therapy;how (not) to measure bias in face recognition networks;feature extraction: A time window analysis based on the x-ite pain database;pain intensity recognition - an analysis of short-time sequences in a real-world scenario;a deep learning approach for efficient registration of dual view mammography;deep transfer learning for texture classification in colorectal cancer histology;applications of generative adversarial networks to dermatologic imaging;typing plasmids with distributed sequence representation;kp-yolo: A modification of yolo algorithm for the keypoint-based detection of qr codes;using mask r-cnn for image-based wear classification of solid carbide milling and drilling tools;a hybrid deep learning approach for forecasting air temperature;intentional image similarity search;using cnns to optimize numerical simulations in geotechnical engineering;going for 2d or 3d? investigating various machine learning approaches for peach variety identification;a transfer learning end-to-end arabic text-to-speech (tts) deep architecture;ml-based trading models: An investigation during covid-19 pandemic crisis;innvestigate-gui - explaining neural networks through an interactive visualization tool;structured (de)composable representations trained with neural networks;long distance relationships without time travel: Boosting the performance of a sparse predictive autoencoder in sequence modeling.

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Integral based approach for determining motion vector fields 9th

Integral based approach for determining motion vector fields

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9th international conference on Image Analysis and Processing, ICIAP 1997

作者： Nomura, Atsushi Department of Cultural and International Studies Yamaguchi Prefectural University Sakurabatake 3-2-1 Yamaguchi753 Japan

ISBN: (纸本)3540635076

this paper describes a method determining a motion vector field from an image sequence. In this method, a couple of a basic constraint equations in integral form and an additional constraint equation of spatio-temporal local constancy of a motion vector field is solved by the over-deterministic approach such as the least squares method. the method is applied to artificially synthesized image sequences and a real image one. As the results, robustness of the proposed method to noisy image sequences is confirmed. In addition, it is confirmed that the method requires less calculation cost than a conventional spatio-temporal optimization method. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Least squares approximations

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9th Brazilian Symposium on bioinformatics, BSB 2014

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19th Iberoamerican Congress on pattern recognition, CIARP 2014

ISBN: (纸本)9783319125671

the proceedings contain 126 papers. the special focus in this conference is on biometrics technology, evolutionary multi-objective optimization, searching for patterns in imbalanced data, lattice neural networks, metaheuristics for multi-objective Optimization, pattern recognition and lattice neural networks theory and applications. the topics include: An introduction to evolutionary multi-objective optimization with some applications in pattern recognition;rotation-invariant nonlinear filters design;estimation of bubble size distribution based on power spectrum;a robust tracking algorithm based on HOGs descriptor;real time hardware accelerator for image filtering;self-adaptive skin segmentation in color images;a comparison of fused segmentation algorithms for iris verification;angle estimation using Hahn moments for image analysis;searching for patterns in imbalanced data;on the use of locality sensitive hashing for audio following;from speech quality measures to speaker recognition performance;artificial neural networks for acoustic lung signals classification;feature analysis for audio classification;assessing cross-cut shredded document assembly;person reidentification and recognition in video;automatic classification of coating epithelial tissue;scalable feature extraction for visual surveillance;thermal face recognition using local patterns;evaluation of key point descriptors for gender recognition;conformal geometric method for voting;a fast pavement location approach for autonomous car navigation;fast edge detection in RGB-D images;summarization of videos by image quality assessment;a deformable model to search characteristic facial points;lifting filters adjustment for lossless image compression applications;morphological change of a scene employing synthetic multispectral and panchromatic images and data fusion approach for employing multiple classifiers to improve lake shoreline analysis.

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