Search results for "image texture"

showing 10 items of 40 documents

An Image Segmentation Algorithm based on Community Detection

2016

International audience; With the recent advances in complex networks, image segmentation becomes one of the most appropriate application areas. In this context, we propose in this paper a new perspective of image segmentation by applying two efficient community detection algorithms. By considering regions as communities, these methods can give an over-segmented image that has many small regions. So, the proposed algorithms are improved to automatically merge those neighboring regions agglomerative to achieve the highest modularity/stability. To produce sizable regions and detect homogeneous communities, we use the combination of a feature based on the Histogram of Oriented Gradients of the …

[ INFO ] Computer Science [cs]Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Minimum spanning tree-based segmentationImage texture0202 electrical engineering electronic engineering information engineeringcommunity detection[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Segmentation[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]modularityImage segmentationSegmentation-based object categorizationbusiness.industry[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]Pattern recognitionImage segmentationcomplex networksHistogram of oriented gradientsRegion growing020201 artificial intelligence & image processingArtificial intelligencebusiness
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Image registration for quality assessment of projection displays

2014

International audience; In the full reference metric based image quality assessment of projection displays, it is critical to achieve accurate and fully automatic image registration between the captured projection and its reference image in order to establish a subpixel level mapping. The preservation of geometrical order as well as the intensity and chromaticity relationships between two consecutive pixels must be maximized. The existing camera based image registration methods do not meet this requirement well. In this paper, we propose a markerless and view independent method to use an un-calibrated camera to perform the task. The proposed method including three main components: feature e…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingImage qualitybusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage registrationKanade–Lucas–Tomasi feature trackerImage processingImage texture[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingComputer visionArtificial intelligenceProjection (set theory)businessImage restorationMathematicsFeature detection (computer vision)
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Manufactured object sub-segmentation based on reflection motion estimation

2015

International audience; In computer vision, reflection is a long-standing problem, it covers image textures, makes original color difficult to recognize, complicates the understanding of the scene. Most of the time, it is considered as “noise”. Many methods are proposed in order to reduce or delete the reflection effects in the image, but generally, the performances are not quite satisfactory. While instead of working on “de-noising”, we propose a method to take advantage of moving reflections that can be used for different computer vision applications. For instance, the segmentation of reflective manufactured objects is presented in this paper. We focus on tracking reflection components an…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingSegmentation-based object categorizationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognition02 engineering and technologyImage segmentation01 natural sciencesScale space010309 opticsImage texture[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingRegion growingMotion estimation0103 physical sciences0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligenceReflection (computer graphics)businessMathematics
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Multi-Kernel Implicit Curve Evolution for Selected Texture Regions Segmentation in VHR Satellite Images

2014

Very high resolution (VHR) satellite images provide a mass of detailed information which can be used for urban planning, mapping, security issues, or environmental monitoring. Nevertheless, the processing of this kind of image is timeconsuming, and extracting the needed information from among the huge quantity of data is a real challenge. For some applications such as natural disaster prevention and monitoring (typhoon, flood, bushfire, etc.), the use of fast and effective processing methods is demanded. Furthermore, such methods should be selective in order to extract only the information required to allow an efficient interpretation. For this purpose, we propose a texture region segmentat…

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR][INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR]Pixelbusiness.industryComputer science0211 other engineering and technologiesGraphics processing unitBoundary (topology)Scale-space segmentation02 engineering and technologyImage segmentationFuzzy logicImage texture11. Sustainability0202 electrical engineering electronic engineering information engineeringGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingComputer visionSegmentation[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]Artificial intelligenceElectrical and Electronic EngineeringbusinessComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering
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Features extraction on complex images

2005

The accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using microscopy. This technique allows the capture of a complex image (i.e., one containing magnitude and phase), and the reconstruction of the phase and magnitude information. Digital holograms give a new dimension to texture analysis, since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on the image segmentation of patterned wafers for defect detection. The paper presents a comparison between the features obtained using Gabor filtering on complex images under illumination and…

business.industryComputer scienceFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHolographyFilter (signal processing)Image segmentationIterative reconstructionlaw.inventionImage texturelawDigital holographic microscopyComputer visionArtificial intelligencebusinessDigital holographyFeature detection (computer vision)
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Statistical methods for texture analysis applied to agronomical images

2008

For activities of agronomical research institute, the land experimentations are essential and provide relevant information on crops such as disease rate, yield components, weed rate... Generally accurate, they are manually done and present numerous drawbacks, such as penibility, notably for wheat ear counting. In this case, the use of color and/or texture image processing to estimate the number of ears per square metre can be an improvement. Then, different image segmentation techniques based on feature extraction have been tested using textural information with first and higher order statistical methods. The Run Length method gives the best results closed to manual countings with an averag…

business.industryFeature extractionPattern recognitionImage processingImage segmentationTexture (music)Class (biology)Image (mathematics)Image textureCluster validity indexComputer visionArtificial intelligencebusinessMathematicsImage Processing: Machine Vision Applications
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A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval

2014

Abstract This paper presents a texture descriptor based on wavelet frame transforms. At each position in the image, and for each resolution level, we consider both vertical and horizontal wavelet detail coefficients as the components of a bivariate random vector. The magnitudes and angles of these vectors are computed. At each level the empirical histogram of magnitudes is modeled by a Generalized Gamma distribution, and the empirical histogram of angles is modeled by a different version of the von Mises distribution that accounts for histograms with 2 modes. Each texture is characterized by few parameters. A new distance is presented (based on the Kullback–Leibler divergence) that allows g…

business.industryTexture DescriptorGeneralized gamma distributionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionWaveletImage textureArtificial IntelligenceComputer Science::Computer Vision and Pattern RecognitionHistogramSignal Processingvon Mises distributionComputer Vision and Pattern RecognitionArtificial intelligenceDivergence (statistics)businessImage retrievalSoftwareMathematicsPattern Recognition
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Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

2018

Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cell…

medical disorderComputer sciencePopulationFeature extraction02 engineering and technologybiomedical optical imagingmedical image processing030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineImage textureblood0202 electrical engineering electronic engineering information engineeringSegmentationimage texturecellular biophysicsCluster analysiseducationimage segmentationdiseaseeducation.field_of_studyIndirect immunofluorescenceContextual image classificationbusiness.industryfeature extractionPattern recognitionImage segmentationSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)020201 artificial intelligence & image processingfluorescenceComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareimage classificationIET Computer Vision
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Tissue classification by texture and spectral analysis of intracoronary ultrasound radio-frequency data

2002

Imaging of vascular structures by intracoronary ultrasound allows in principal the recognition of different lesion types due to the echomorphology in the B-mode image. The subjective visual diagnosis is often difficult, especially the differentiation between thrombi and non-calcified plaque. The aim of this study was the extraction of features from the ultrasound radio-frequency signal for an objective characterization of coronary tissue. Methods of texture analysis and frequency analysis were used to differentiate red and white thrombi in vitro. Eight texture parameters of first and second order significantly differentiated red and white thrombi. The backscatter transfer function of red th…

medicine.medical_specialtybusiness.industryUltrasoundLesion typesmedicine.diseaseTexture (geology)Image texturecardiovascular systemMedicineIntracoronary ultrasoundSpectral analysiscardiovascular diseasesRadio frequencyRadiologyThrombusbusinesscirculatory and respiratory physiologyBiomedical engineeringComputers in Cardiology 1995
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Automatic dynamic texture segmentation using local descriptors and optical flow

2012

A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e., motion field). To this end, we develop a framework based on the appearance and motion modes. For the appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for the motion mode, we use the optical flow and the local temporal texture descriptor to represent the temporal variations of the DT. In addition, for the optical flow, we use the histogram of orie…

ta113business.industrySegmentation-based object categorizationComputer scienceTexture DescriptorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flowScale-space segmentationPattern recognitionImage segmentationComputer Graphics and Computer-Aided DesignImage textureMotion fieldRegion growingComputer Science::Computer Vision and Pattern RecognitionHistogramComputer visionSegmentationArtificial intelligencebusinessSoftwareIEEE Transactions on Image Processing
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