Search results for "Binary pattern"

showing 8 items of 18 documents

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.

2016

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.

Support Vector Machinegenetic structuresDatabases FactualComputer science[INFO.INFO-IM] Computer Science [cs]/Medical Imaging02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciences[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0202 electrical engineering electronic engineering information engineeringImage Processing Computer-AssistedSegmentationComputer visionmedicine.diagnostic_test[ INFO.INFO-IM ] Computer Science [cs]/Medical ImagingDiabetic retinopathyHistogram of oriented gradientsmedicine.anatomical_structure020201 artificial intelligence & image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingTomography Optical CoherenceLocal binary patternsFeature vectorDiabetic macular edemaFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingSensitivity and SpecificityMacular Edema010309 opticsOptical coherence tomographyHistogram0103 physical sciencesmedicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingHumansMacular edema[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingRetinaDiabetic Retinopathybusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognitionImage segmentationmedicine.diseaseeye diseasesSupport vector machineComputingMethodologies_PATTERNRECOGNITIONsense organsArtificial intelligencebusinessAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Modular Method of Detection, Localization and Counting of Mutliple-Taxon Pollen Apertures Using Bag of Words

2014

International audience; Accurate recognition of airborne pollen taxa is crucial for understanding and treating allergic diseases, which affect an important proportion of the world population. Modern computer vision techniques enables the detection of discriminant characteristics. Apertures is one of these characteristic that has been little explored up to now. In this paper, a flexible method of detection, localization and counting of apertures of different pollen taxa with varying appearances is proposed. Apertures are described based by primitive images following the Bag-of-Words strat-egy. A confidence map is estimated based on the classification of sampled regions. The method is designe…

Contextual image classificationComputer sciencebusiness.industryLocal binary patternspattern recognitionaperturesCognitive neuroscience of visual object recognition[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Image segmentationmedicine.disease_cause[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Atomic and Molecular Physics and OpticsComputer Science Applicationsbag of wordsRobustness (computer science)Bag-of-words modelPollenLBPPattern recognition (psychology)medicineComputer visionArtificial intelligenceElectrical and Electronic Engineeringbusinesspalynology
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Image Colorization Method Using Texture Descriptors and ISLIC Segmentation

2017

We present a new colorization method to assign color to a grayscale image based on a reference color image using texture descriptors and Improved Simple Linear Iterative Clustering (ISLIC). Firstly, the pixels of images are classified using Support Vector Machine (SVM) according to texture descriptors, mean luminance, entropy, homogeneity, correlation, and local binary pattern (LBP) features. Then, the grayscale image and the color image are segmented into superpixels, which are obtained by ISLIC to produce more uniform and regularly shaped superpixels than those obtained by SLIC, and the classified images are further post-processed combined with superpixles for removing erroneous classific…

Pixelbusiness.industryColor imageLocal binary patternsComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognitionImage segmentationGrayscaleImage textureComputer Science::Computer Vision and Pattern RecognitionArtificial intelligencebusinessCluster analysisComputingMethodologies_COMPUTERGRAPHICS
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Multi-Scale Feature Extraction for Vehicle Detection Using Phis-Lbp

2018

International audience; Multi-resolutionobjectdetectionfacesseveraldrawbacksincludingitshighdimensionalityproducedby a richer image representation in different channels or scales. In this paper, we propose a robust and lightweight multi-resolution method for vehicle detection using local binary patterns (LBP) as channel feature. Algorithm acceleration is done using LBP histograms instead of multi-scale feature maps and by extrapolating nearby scales to avoid computing each scale. We produce a feature descriptor capable of reaching a similar precision to other computationally more complex algorithms but reducing its size from 10 to 800 times. Finally, experiments show that our method can obt…

[SPI]Engineering Sciences [physics][SPI] Engineering Sciences [physics]Computer Science::Computer Vision and Pattern Recognitionfeatures pyramidsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONFeature extractionvehicle detectiontextureLocal Binary Patterns
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Studies on the Effectiveness of Multispectral Images for Face Recognition: Comparative Studies and New Approaches

2013

In this paper, we investigate face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, three state of the art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are challenged against the IRIS-M3 multispectral face data base to evaluate their robustness against high illumination variation. Second, we propose to enhance the Performance of the three mentioned algorithms, which has been drastically decreased because of the non-monotonic illumination variation that distinguishes the IRIS-M3 face database. Instead of the usual braod band images…

Computer sciencebusiness.industryLocal binary patternsMultispectral imageFeature extractionPattern recognitionSpectral bandsBinary patternFacial recognition systemRobustness (computer science)HistogramComputer visionArtificial intelligencebusiness2013 International Conference on Signal-Image Technology & Internet-Based Systems
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Spatio-Temporal Saliency Detection in Dynamic Scenes using Local Binary Patterns

2014

International audience; Visual saliency detection is an important step in many computer vision applications, since it reduces further processing steps to regions of interest. Saliency detection in still images is a well-studied topic. However, videos scenes contain more information than static images, and this additional temporal information is an important aspect of human perception. Therefore, it is necessary to include motion information in order to obtain spatio-temporal saliency map for a dynamic scene. In this paper, we introduce a new spatio-temporal saliency detection method for dynamic scenes based on dynamic textures computed with local binary patterns. In particular, we extract l…

business.industryLocal binary patternsComputer sciencemedia_common.quotation_subjectComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognition[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]video saliencyMotion (physics)visual saliencyKadir–Brady saliency detector[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Salience (neuroscience)PerceptionLBPSaliency mapComputer visionArtificial intelligencebusinessmedia_commonVisual saliency
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Local Directional Multi Radius Binary Pattern

2018

Face recognition becomes an important task performed routinely in our daily lives. This application is encouraged by the wide availability of powerful and low-cost desktop and embedded computing systems, while the need comes from the integration in too much real world systems including biometric authentication, surveillance, human-computer interaction, and multimedia management. This article proposes a new variant of LBP descriptor referred as Local Directional Multi Radius Binary Pattern (LDMRBP) as a robust and effective face descriptor. The proposed LDMRBP operator is built using new neighborhood topology and new pattern encoding scheme. The adopted face recognition system consists of th…

BiometricsContextual image classificationbusiness.industryComputer scienceFeature vectorFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION020206 networking & telecommunicationsPattern recognition02 engineering and technologyBinary patternFacial recognition systemComputingMethodologies_PATTERNRECOGNITIONHistogram0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinessFace detection
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Classification of SD-OCT Volumes with LBP: Application to DME Detection

2015

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Our method is based on Local Binary Patterns (LBP) features to describe the texture of Optical Coherence Tomography (OCT) images and we compare different LBP features extraction approaches to compute a single signature for the whole OCT volume. Experimental results with two datasets of respectively 32 and 30 OCT volumes show that regardless of using low or high level representations, features derived from LBP texture have highly discriminative power. Moreover, the experimen…

genetic structuresLocal binary patternsComputer scienceDiabetic macular edemaSpectral domain02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineOptical coherence tomographyDiscriminative modelLBP0202 electrical engineering electronic engineering information engineeringmedicineDMEComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingmedicine.diagnostic_testbusiness.industryeye diseasesDiabetic Macular EdemaOCT020201 artificial intelligence & image processingArtificial intelligencesense organsOptical Coherence Tomographybusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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