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检索条件"任意字段=9th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2014"

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A new visibility partition for affine pattern matching

A new visibility partition for affine pattern matching

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9th international conference on Discrete Geometry for Computer Imagery, DGCI 2000

作者： Hagedoorn, Michiel Overmars, Mark Veltkamp, Remco C. Department of Computer Science Utrecht University Padualaan 14 3584 CH Utrecht Netherlands

ISBN: (纸本)3540413960

Visibility partitions play an important role in computer vision and pattern matching. this paper studies a new type of visibility, reflection-visibility, with applications in affine pattern matching: it is used in the definition of the reflection metric between two patterns consisting of line segments. this metric is affine invariant, and robust against noise, deformation, blurring, and cracks. We present algorithms that compute the reflection visibility partition in O((n+κ) log(n)+v) randomised time, where κ is the number of visibility edges (at most O(n2)), and v is the number of vertices in the partition (at most O(n2+κ2)). We use this partition to compute the reflection metric in O(r(nA + n B)) randomised time, for two line segment unions, with nA and nB line segments, separately, where r is the complexity of the overlay of two reflection-visibility partitions (at most O(nA4 + nB4)). © Springer-Verlag Berlin Heidelberg 2000.

关键词： Visibility

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Unsupervised texture segmentation using feature distributions 9th

Unsupervised texture segmentation using feature distribution...

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

作者： Ojala, Timo Pietikäinen, Matti Machine Vision and Media Processing Group Infotech Oulu University of Oulu OuluFIN-90570 Finland

ISBN: (纸本)3540635076

this paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions. A region-based algorithm is developed for coarse image segmentation and a pixelwise classification scheme for improving localization of region boundaries. the performance of the method is evaluated with various types of test images. the same set of parameter values is used in all the experiments with texture mosaics in order to demonstrate the robustness of our approach. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Local binary pattern

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9th Iberoamerican Congress on pattern recognition, CIARP 2004

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9th Iberoamerican Congress on pattern recognition, CIARP 2004

ISBN: (纸本)3540235272

the proceedings contain 87 papers. the special focus in this conference is on Progress in pattern recognition, Image Analysis and Applications. the topics include: Use of context in automatic annotation of sports videos;content based retrieval of 3D data;a promising framework for computer vision, robotics and learning;adaptive color model for figure-ground segmentation in dynamic environments;real-time infrared object tracking based on mean shift;optimal positioning of sensors in 2D;computer vision algorithms versus traditional methods in food technology;radiance function estimation for object classification;detecting and ranking saliency for scene description;decision fusion for object detection and tracking using mobile cameras;selection of an automated morphological gradient threshold for image segmentation;localization of caption texts in natural scenes using a wavelet transformation;a depth measurement system with the active vision of the striped lighting and rotating mirror;fast noncontinuous path phase-unwrapping algorithm based on gradients and mask;color active contours for tracking roads in natural environments;generation of N-parametric appearance-based models through non-uniform sampling;gaze detection by wide and narrow view stereo camera;a new auto-associative memory based on lattice algebra;image segmentation using morphological watershed applied to cartography;3D object surface reconstruction using growing self-organised networks;single layer morphological perceptron solution to the N-bit parity problem;robust self-organizing maps;extended associative memories for recalling gray level patterns;new associative memories to recall real-valued patterns and feature maps for non-supervised classification of low-uniform patterns of handwritten letters.

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Introducing an experimental framework in C# for fingerprint recognition

Introducing an experimental framework in C# for fingerprint ...

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6th Mexican conference on pattern recognition, MCPR 2014

作者： Medina-Pérez, Miguel Angel Loyola-González, Octavio Gutierrez-Rodríguez, Andres Eduardo García-Borroto, Milton Altamirano-Robles, Leopoldo Instituto Nacional de Astrofísica Óptica Y Electrónica Luis Enrique Erro No. 1 Sta. María Tonanzintla Puebla C.P. 72840 Mexico Centro de Bioplantas Universidad de Ciego de Ávila Carretera a Morón km 9 Ciego de Ávila Cuba Instituto Superior Politécnico José A. Echeverría C. 114 No. 11901 e/ Ciclovia y Rotonda Apdo. 6028 Marianao Habana Cuba

ISBN: (纸本)9783319074900

In this paper, a framework for fingerprint recognition is introduced. the framework contains several algorithms for fingerprint matching and feature extraction, as well as the evaluation protocol for several fingerprint verification competitions. All the algorithms are implemented in C# providing the source code which researchers can review, reuse or modify. In order to show the framework relevance, an experimental comparison among different matching algorithms is presented. © 2014 Springer international Publishing.

关键词： pattern matching

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

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

ISBN: (纸本)3540635084

the proceedings contain 98 papers. the special focus in this conference is on Image Databases. the topics include: Content-centric computing in visual systems;holographic image representations;image databases are not databases with images;customizing MPEG video compression algorithms to specific application;a new lossless image compression algorithm based on arithmetic coding;analysis of a two step MPEG video system;dedicated hardware processors for a real-time image data pre-processing implemented in FPGA structure;wavelet transform architectures;lossless compression of pre-press images using a novel colour decorrelation technique;real time hardware architecture for visual robot navigation;speeding up fractal encoding of images using a block indexing technique;adding associative meshes to the PACCO I.P. environment;smoothing of MPEG multi-program video coding for packet networks;audio-visual processing for scene change detection;weighted walkthroughs in retrieval by content of pictorial data;a new approach to computation of curvature scale space image for shape similarity retrieval;optimal keys for image database indexing;the terminological image retrieval model;novel block truncation coding of image sequences for limited-color display;image registration with shape mixtures;image retrieval by color regions;interactive model-based matching retrieval;histogram families for color-based retrieval in image databases;image retrieval by multidimensional elastic matching;optimization methods in multilayer classifier networks for automatic control of lamellibranch larva growth and neural networks for region detection.

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Metabolic pathway inference from time series data: A non iterative approach

Metabolic pathway inference from time series data: A non ite...

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6th iapr international conference on pattern recognition in bioinformatics, prib 2011

作者： Astola, Laura Groenenboom, Marian Gomez Roldan, Victoria Van Eeuwijk, Fred Hall, Robert D. Bovy, Arnaud Molenaar, Jaap Biometris Wageningen University and Research Centre Droevendaalsesteeg 1 Wageningen Netherlands Netherlands Consortium for Systems Biology Amsterdam Netherlands Plant Research International Wageningen University Research Centre Wageningen Netherlands Centre for Biosystems Genomics P.O. Box 98 Wageningen Netherlands

ISBN: (纸本)9783642248542

In this article, we present a very fast and easy to implement method for reconstruction of metabolic pathways based on time series data. To model the metabolic reactions, we use the well-established setting of ordinary differential equations. In the present article we consider a network leading to the accumulation of quercetin-glycosides in tomato (Solanum lycopersicum). Quercetin belongs to a group of plant secondary metabolites, generally referred to as flavonoids, which are extensively being studied for their variety of important functions in plants as well as for their potentially health-promoting effects on human. We use time series measurements of metabolite concentrations of quercetin derivatives. In the present setting, the observed concentrations are the variables and the reaction rates are the unknown parameters. A standard method is to solve the parameters by reverse engineering, where the ordinary differential equations (ODE) are solved repeatedly, resulting in impractical computation times. We use an alternative method that estimates the parameters by least squares minimization, and which is, in the order of hundred times faster than the iterative method. Our reconstruction method can incorporate an arbitrary a priori known network structure as well as positivity constraints on the reaction rates. In this way we can avoid over-fitting, which is another often encountered problem in network reconstruction, and thus obtain better estimates for the parameters. We test the presented method by reconstructing artificial networks and compare it with the more conventional method in terms of residuals between the observed and fitted concentrations, computing times and the proportion of correctly identified edges in the network. Finally we exploit this fast method to statistically infer the kinetic constants in the flavonoid pathway. We remark that the method as such is not limited to metabolic network reconstructions, but can be used with any type of time-series

关键词： Ordinary differential equations

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A novel saccade signals detection algorithm for EOG-based Human Activity recognition 9th

A novel saccade signals detection algorithm for EOG-based Hu...

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9th international conference on bioinformatics and Biomedical Engineering (iCBBE)

作者： Lv, Z. Guan, J. N. Zhou, B. Y. Wu, X. P. Anhui Univ Key Lab Intelligent Comp & Signal Proc Hefei Anhui Peoples R China Anhui Univ Coinnovat Ctr Informat Supply & Assurance Technol Hefei Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781315683010;9781138027848

A research on the relationship between eye movements and human behavior is a hot topic in the field of Human Activity recognition (HAR). In this paper, a novel saccade signals detection algorithm for EOG-based HAR, which aims to improve the performance of HAR system, was proposed. In the proposed algorithm, Common Spatial pattern (CSP) was utilized to build spatial filters, and then use it to process original multi-channel EOG signals. Consequently, feature parameters of different saccade signals can be acquired. To valid the performance of the proposed algorithm, a linear Support Vector Machine (SVM) was chosen. In lab environment, four types of saccade signals corresponding to up, down, left and right were used as analysis objects. Experimental results show that the accuracy recognition ratio is about 97.7%, which reveal that the proposed algorithm has a good classification performance in saccade signals analysis.

关键词： EOG saccade signals Common Spatial pattern (CSP) joint approximate diagonalization Support Vector Machine (SVM)

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Object recognition with näive Bayes-NN via prototype generation

Object recognition with näive Bayes-NN via prototype genera...

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6th Mexican conference on pattern recognition, MCPR 2014

作者： Escalante, Hugo Jair Sotomayor, Mauricio Montes, Manuel Lopez-Monroy, A. Pastor Computer Science Department INAOE Luis Enrique Erro No. 1 Puebla 72840 Mexico Instituto Tecnologico de Orizaba Av. Oriente 9 No. 832 Orizaba Ver. 66451 Mexico

Na9;ve Bayes nearest neighbors (NBNN) is a variant of the classic KNN classifier that has proved to be very effective for object recognition and image classification tasks. Under NBNN an unseen image is classified... 详细信息

ISBN: (纸本)9783319074900

Naïve Bayes nearest neighbors (NBNN) is a variant of the classic KNN classifier that has proved to be very effective for object recognition and image classification tasks. Under NBNN an unseen image is classified by looking at the distance between the sets of visual descriptors of test and training images. Although NBNN is a very competitive pattern classification approach, it presents a major drawback: it requires of large storage and computational resources. NBNN's requirements are even larger than those of the standard KNN because sets of raw descriptors must be stored and compared, therefore, efficiency improvements for NBNN are necessary. Prototype generation (PG) methods have proved to be helpful for reducing the storage and computational requirements of standard KNN. PG methods learn a reduced subset of prototypical instances to be used by KNN for classification. In this contribution we study the suitability of PG methods for enhancing the capabilities of NBNN. throughout an extensive comparative study we show that PG methods can reduce dramatically the number of descriptors required by NBNN without significantly affecting its discriminative performance. In fact, we show that PG methods can improve the classification performance of NBNN by using much less visual descriptors. We compare the performance of NBNN to other state-of-the-art object recognition approaches and show the combination of PG and NBNN outperforms alternative techniques. © 2014 Springer international Publishing.

关键词： Object recognition

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Object recognition with Näaive Bayes-NN via Prototype Generation

Object recognition with Näaive Bayes-NN via Prototype Gener...

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6th Mexican conference on pattern recognition, MCPR 2014

作者： Escalante, Hugo Jair Sotomayor, Mauricio Montes, Manuel Lopez-Monroy, A. Pastor Computer Science Department INAOE Luis Enrique Erro No. 1 Puebla72840 Mexico Instituto Tecnologico de Orizaba Av. Oriente 9 No. 832 Orizaba Ver66451 Mexico

ISBN: (纸本)9783319074900

Nave Bayes nearest neighbors (NBNN) is a variant of the classic KNN classifier that has proved to be very effective for object recognition and image classification tasks. Under NBNN an unseen image is classified by looking at the distance between the sets of visual descriptors of test and training images. Although NBNN is a very competitive pattern classification approach, it presents a major drawback: it requires of large storage and computational resources. NBNN’s requirements are even larger than those of the standard KNN because sets of raw descriptors must be stored and compared, therefore, efficiency improvements for NBNN are necessary. Prototype generation (PG) methods have proved to be helpful for reducing the storage and computational requirements of standard KNN. PG methods learn a reduced subset of prototypical instances to be used by KNN for classification. In this contribution we study the suitability of PG methods for enhancing the capabilities of NBNN. throughout an extensive comparative study we show that PG methods can reduce dramatically the number of descriptors required by NBNN without significantly affecting its discriminative performance. In fact, we show that PG methods can improve the classification performance of NBNN by using much less visual descriptors. We compare the performance of NBNN to other state-of-the-art object recognition approaches and show the combination of PG and NBNN outperforms alternative techniques. © Springer international Publishing Switzerland 2014.

关键词： Object recognition

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9th international conference on Swarm Intelligence, ANTS 2014

9th International Conference on Swarm Intelligence, ANTS 201...

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9th international conference on Swarm Intelligence, ANTS 2014

ISBN: (纸本)9783319099514

the proceedings contain 26 papers. the special focus in this conference is on Swarm Intelligence. the topics include: A novel ant colony algorithm for building neural network topologies;an ACO algorithm to solve an extended cutting stock problem for scrap minimization in a bar mill;angle modulated particle swarm variants;ant colony optimization on a budget of 1000;application of supervisory control theory to swarms of e-puck and kilobot robots;diversity rate of change measurement for particle swarm optimisers;influencing a flock via Ad Hoc teamwork;a medoid-based ACO clustering algorithm;standardized analysis and topological influence;scheduling a galvanizing line by ant colony optimization;leveraging stigmergy on a multi-robot construction platform for unknown environments;swarm in a fly bottle: feedback-based analysis of self-organizing temporary lock-ins;temporal task allocation in periodic environments;towards a cognitive design pattern for collective decision-making;a novel competitive quantum-behaviour evolutionary multi-swarm optimizer algorithm based on CUDA architecture applied to constrained engineering design;cooperative object recognition: behaviours of a artificially evolved swarm;emergent diagnoses from a collective of radiologists;foraging agent swarm optimization with applications in data clustering;GPU implementation of food-foraging problem for evolutionary swarm robotics systems;particle swarm optimisation with enhanced memory particles;sorting in swarm robots using communication-based cluster size estimation;a low-cost real-time tracking infrastructure for ground-based robot swarms;morphology learning via MDL and ants;parallelizing solution construction in ACO for GPUS and solving resource-constraint project scheduling problems based on ACO algorithms.

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