Search results for "Pattern"

showing 10 items of 4203 documents

Hankelet-based dynamical systems modeling for 3D action recognition

2015

This paper proposes to model an action as the output of a sequence of atomic Linear Time Invariant (LTI) systems. The sequence of LTI systems generating the action is modeled as a Markov chain, where a Hidden Markov Model (HMM) is used to model the transition from one atomic LTI system to another. In turn, the LTI systems are represented in terms of their Hankel matrices. For classification purposes, the parameters of a set of HMMs (one for each action class) are learned via a discriminative approach. This work proposes a novel method to learn the atomic LTI systems from training data, and analyzes in detail the action representation in terms of a sequence of Hankel matrices. Extensive eval…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSequenceMarkov chainDynamical systems theorySupervised learningHankel MatrixHidden Markov ModelLTI system theoryDiscriminative learningLinear time invariant systemDiscriminative modelActionComputer Science::Systems and ControlControl theorySignal ProcessingComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringHidden Markov modelHankel matrixAlgorithmMathematicsImage and Vision Computing
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Wi-Dia: Data-Driven Wireless Diagnostic Using Context Recognition

2018

The recent densification of Wi-Fi networks is exacerbating the effects of well-known pathologies including hidden nodes and flow starvation. This paper provides an automatic diagnostic tool for detecting the source roots of performance impairments by recognizing the wireless operating context. Our tool for Wi-Fi diagnostic, named Wi-Dia, exploits machine learning methods and uses features related to network topology and channel utilization, without impact on regular network operations and working in real-time. Real-time per-link Wi-Fi diagnosis enables recovering actions for context-specific treatments. Wi-Dia classifier recognizes different classes of interference; it is jointly trained us…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaExploitRenewable Energy Sustainability and the EnvironmentComputer sciencebusiness.industryReal-time computingEnergy Engineering and Power TechnologyExperimental dataContext recognitionComputer Science Applications1707 Computer Vision and Pattern RecognitionNetwork topologyIndustrial and Manufacturing EngineeringData modelingData-drivenComputer Networks and CommunicationArtificial IntelligenceWirelessbusinessInstrumentationClassifier (UML)2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)
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An evaluation of recent local image descriptors for real-world applications of image matching

2019

This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but als…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaImage matchingComputer sciencebusiness.industryVisual descriptorsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognition02 engineering and technology03 medical and health sciences0302 clinical medicineLocal Image Descriptors; Image MatchingRobustness (computer science)Computer Science::Computer Vision and Pattern RecognitionComputer Science::Multimedia030221 ophthalmology & optometry0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinessImage matching Data-driven approach Descriptors Evaluation results Local descriptors Local image descriptors Performance degradation Real-worldScene structure Computer vision
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Is There Anything New to Say About SIFT Matching?

2020

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. Th…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - Informaticabusiness.industryComputer scienceImage matchingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformPattern recognition02 engineering and technologySIFT sGLOH2 Quantization Binary descriptors Symmetric matching Hierarchical cascade filtering Deep descriptors Keypoint patch orientation Approximated overlap errorDiscriminative modelArtificial IntelligenceHistogramComputer data storage0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceSIFTsGLOH2quantizationbinary descriptorssymmetric matchinghierarchical cascade filteringdeep descriptorskeypoint patch orientationapproximated overlap errorbusinessQuantization (image processing)SoftwareInternational Journal of Computer Vision
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A Low Cost Solution for NOAA Remote Sensing

2018

United States National Oceanic and Atmospheric Administration (NOAA) weather satellites adopt Advanced Very High Resolution Radiometer (AVHRR) sensors to acquire remote sensing data and broadcast Automatic Picture Transmission (APT) images. The orientation of the scan lines is perpendicular to the orbit of the satellite. In this paper we propose a new low cost solution for NOAA remote sensing. More in detail, our method focuses on the possibility of directly sampling the modulated signal and processing it entirely in software enabled by recent breakthroughs on Software Defined Radios (SDR) and CPU computational speed, while keeping the costs extremely low. We aim to achieve good results wit…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSignal processingComputer Networks and CommunicationsComputer science0211 other engineering and technologiesComputer Science Applications1707 Computer Vision and Pattern RecognitionInformation System02 engineering and technologyRemote SensingRemote sensing (archaeology)Signal ProcessingInformation systemCommunications satelliteSatellite CommunicationElectrical and Electronic Engineering021101 geological & geomatics engineeringRemote sensingProceedings of the 7th International Conference on Sensor Networks
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Image Segmentation through a Hierarchy of Minimum Spanning Trees

2012

Many approaches have been adopted to solve the problem of image segmentation. Among them a noticeable part is based on graph theory casting the pixels as nodes in a graph. This paper proposes an algorithm to select clusters in the images (corresponding to relevant segments in the image) corresponding to the areas induced in the images through the search of the Minimum Spanning Tree (MST). In particular is is based on a clustering algorithm that extracts clusters computing a hierarchy of Minimum Spanning Trees. The main drawback of this previous algorithm is that the dimension of the cluster is not predictable and a relevant portion of found clusters can be composed by micro-clusters that ar…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSpanning treebusiness.industrySingle-linkage clusteringComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionImage segmentationMinimum spanning treeImage SegmentationMinimum Spanning TreesClusteringDistributed minimum spanning treeMinimum spanning tree-based segmentationKruskal's algorithmArtificial IntelligenceComputer Science::Computer Vision and Pattern RecognitionReverse-delete algorithmArtificial intelligencebusinessMathematics
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Entropy-based Localization of Textured Regions

2011

Appearance description is a relevant field in computer vision that enables object recognition in domains as re-identification, retrieval and classification. Important cues to describe appearance are colors and textures. However, in real cases, texture detection is challenging due to occlusions and to deformations of the clothing while person's pose changes. Moreover, in some cases, the processed images have a low resolution and methods at the state of the art for texture analysis are not appropriate. In this paper, we deal with the problem of localizing real textures for clothing description purposes, such as stripes and/or complex patterns. Our method uses the entropy of primitive distribu…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniTexture atlasComputer sciencebusiness.industryLocal binary patternsLow resolutionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCognitive neuroscience of visual object recognitionLatent Dirichlet allocationsymbols.namesakesymbolsEntropy (information theory)SegmentationComputer visionArtificial intelligencebusinessimage analysis textureComputingMethodologies_COMPUTERGRAPHICS
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Normalised compression distance and evolutionary distance of genomic sequences: comparison of clustering results

2009

Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a time consuming procedure and the obtained dissimilarity results is not a metric. Recently, the normalised compression distance was introduced as a method to calculate the distance between two generic digital objects and it seems a suitable way to compare genomic strings. In this paper, the clustering and the non-linear mapping obtained using the evolutionary distance and the compression distance are compared, in order to understand if the two distances sets are similar.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryCompression (functional analysis)Metric (mathematics)Normalized compression distanceuniversal similarity metric USM clustering DNA sequences normalised compression distance evolutionary distance genomic sequences nonlinear mapping bioinformaticsPattern recognitionArtificial intelligenceCluster analysisbusinessDistance matrices in phylogenyMathematics
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Separation of Concerns and Role Implementation in the PASSI Design Process

2007

The use of design patterns proved successful in lowering the development time and number of errors when producing software with the object-oriented paradigm. In previous works we engaged the production of a tool for the reuse of patterns for multi-agent systems. Now we are fronting a new problem: automatic code generation for agents, designed with a specific methodology, with the support of design patterns and using an aspect oriented approach. In this work we present our preliminary experiences in the identification, description, production and use of aspects for multi agent systems and a tool for code production.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryComputer scienceAspect-oriented programmingMulti-agent systemSeparation of concernsReuseSoftware agentSoftware design patternSystems engineeringDesign processCode generationSoftware engineeringbusinessComputer softwareMulti agent systemsObject oriented programmingProblem solving2007 5th IEEE International Conference on Industrial Informatics
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Text localization from photos

2009

In this paper a new text extraction algorithm is proposed. In real scenes the text is usually overlapped or is part of the background. To identify the text regions, in complex conditions, a method exploiting a “multi-resolution feature based method” for extracting text with undefined dimension has been developed. Once identified, the multi-resolution information are merged and skimmed through a set of Support Vector Machines (SVM). The tests and the comparisons with other techniques, performed on heterogeneous images, have shown the effectiveness of the proposed.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryComputer scienceFeature extractionPattern recognitionSupport vector machineSet (abstract data type)Text Localization Image UnderstandingDimension (vector space)Pattern recognition (psychology)Computer visionArtificial intelligencebusinessImage resolution2009 Digest of Technical Papers International Conference on Consumer Electronics
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