Search results for "ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION"

showing 10 items of 982 documents

Cross-Media Color Reproduction and Display Characterization

2012

International audience; In this chapter, we present the problem of cross-media color reproduction, that is, how to achieve consistent reproduction of images in different media with different technologies. Of particular relevance for the color image processing community is displays, whose color properties have not been extensively covered in previous literature. Therefore, we go more in depth concerning how to model displays in order to achieve colorimetric consistency. The structure of this chapter is as follows: After a short introduction, we introduce the field of cross-media color reproduction, including a brief description of current standards for color management, the concept of colori…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONColor reproductionCross media[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyBiologyColor management01 natural sciencesCharacterization (materials science)law.invention010309 opticsConsistency (database systems)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processinglaw0103 physical sciences0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingColor image processingRelevance (information retrieval)Computer visionArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Camera-based measurement of relative image contrast in projection displays

2013

International audience; This research investigated the measured contrast of projection displays based on pictures taken by un-calibrated digital cameras under typical viewing conditions. A high-end radiometer was employed as a reference to the physical response of projection luminance. Checkerboard, gray scale and color complex test images with a range of the projector's brightness and contrast settings were projected. Two local and two global contrast metrics were evaluated on the acquired pictures. We used contrast surface plots and Pearson correlation to investigate the measured contrast versus the projector's brightness and contrast settings. The results suggested, as expected, the proj…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingoptical projectors[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processingprojection luminanceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingmeasured contrastradiometersImage color analysismetrics[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingstatistical analysispicture acquisitioncamerasgray scaleBrightnessoptical variables measurementdigital cameracamera-based measurementRadiometry[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingdisplay devicesDigital camerasuncalibrated digital cameraglobal contrast metricsprojector brightness settingscheckerboardcolor complex test imagesrelative image contrastviewing conditionsradiometerimage processingCorrelationPearson correlationhigh-end radiometerprojection displayprojector contrast settings[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingprojection displayscontrast surface plotsstatistic based metrics
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AN APPROACH TO CORRECTING IMAGE DISTORTION BY SELF CALIBRATION STEREOSCOPIC SCENE FROM MULTIPLE VIEWS

2012

International audience; An important step in the analysis and interpretation of video scenes for recognizing scenario is the aberration corrections introduced during the image acquisition in order to provide and correct real image data. This paper presents an approach on distortion correction based on stereoscopic self calibration from images sequences by using a multi-camera system of vision (network cameras). This approach for correcting image distortion brings an elegant and robust technique with good accuracy. Without any knowledge of shooting conditions, the camera's parameters will be estimated. For this, the image key points of interest are extracted from different overlapping views …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingprojective rectification[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingImage qualityEpipolar geometryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyfundamental matrix[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingCamera auto-calibration0202 electrical engineering electronic engineering information engineeringComputer visionImage rectificationImage warpingImage restorationstereovision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsbusiness.industry020208 electrical & electronic engineeringAstrophysics::Instrumentation and Methods for AstrophysicsReal imageComputer Science::Computer Vision and Pattern Recognitionepipolar geometry020201 artificial intelligence & image processingArtificial intelligencedistortionbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Definition of a mutual reference shape based on information theory and active contours

2013

In this paper, we propose to consider the estimation of a reference shape from a set of different segmentation results using both active contours and information theory. The reference shape is then defined as the minimum of a criterion that benefits from both the mutual information and the joint entropy of the input segmentations. This energy criterion is here justified using similarities between information theory quantities and area measures, and presented in a continuous variational framework. This framework brings out some interesting evaluation measures such as the specificity and sensitivity. In order to solve this shape optimization problem, shape derivatives are computed for each te…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingsegmentation evaluation[ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processingaverage shape[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingactive contours[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]shape gradientsImage processingcardiac MRI.[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processingshape optimizationcardiac MRI[INFO.INFO-IM]Computer Science [cs]/Medical Imaging[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processinginformation theory
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Incorporating depth information into few-shot semantic segmentation

2021

International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Artificial neural networkComputer sciencebusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunications02 engineering and technologyImage segmentationSemanticsVisualization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingMetric (mathematics)0202 electrical engineering electronic engineering information engineeringEmbeddingRGB color modelSegmentationComputer visionArtificial intelligencebusiness
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Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR

2021

International audience; In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Bayesian deep learningCardiac MRI Segmentation[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONUncertainty[INFO.INFO-IM]Computer Science [cs]/Medical ImagingMyocardial scar[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Sub-optimal waypoints, UAV path planning and mosaicing application

2016

International audience; Create a complete system of video surveillance using camera mounted on a robot like UAV to maintain optimized vast area coverage and reconstruct an image by using mosaicing techniques. This paper demonstrated the efficiency of using one UAV to cover vast area using optimized positions.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Cover (telecommunications)Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION010103 numerical & computational mathematics01 natural sciencesUnmanned aerial vehicles[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Computer visionMotion planning0101 mathematics[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Genetic Algorithmbusiness.industry[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO][INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][SPI.TRON] Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics010101 applied mathematicsCoverage path planningArea coverageRobotArtificial intelligencebusiness
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Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO

2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations,…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Matching (graph theory)Feature vectorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[INFO] Computer Science [cs][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Kernel (linear algebra)[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Discriminative modelRobustness (computer science)0202 electrical engineering electronic engineering information engineeringFeature (machine learning)[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ComputingMilieux_MISCELLANEOUSMathematicsbusiness.industryParticle swarm optimization[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognition020201 artificial intelligence & image processingArtificial intelligencebusinessEnergy (signal processing)
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Design and Calibration of an Omni-RGB plus D Camera

2016

International audience; In this paper, we present the design of a new camera combining both predator-like and prey-like vision features. This setup provides both a spherical RGB-view and a directional depth-view of the environment. The model and calibration of the full set-up are described. A few examples will be given to demonstrate the interest and the versatility of such camera for robotics and video surveillance at the oral presentation.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONstereo vision[SPI.TRON] Engineering Sciences [physics]/Electronics[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SPI.TRON]Engineering Sciences [physics]/Electronicsfisheye[ SPI.TRON ] Engineering Sciences [physics]/Electronics[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]dioptric[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]unified model
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Inverse Tone Mapping Based upon Retina Response

2014

International audience; The development of high dynamic range (HDR) display arouses the research of inverse tone mapping methods, which expand dynamic range of the low dynamic range (LDR) image to match that of HDR monitor. This paper proposed a novel physiological approach, which could avoid artifacts occurred in most existing algorithms. Inspired by the property of the human visual system (HVS), this dynamic range expansion scheme performs with a low computational complexity and a limited number of parameters and obtains high-quality HDR results. Comparisons with three recent algorithms in the literature also show that the proposed method reveals more important image details and produces …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Computational complexity theoryArticle SubjectComputer sciencemedia_common.quotation_subjectComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONlcsh:MedicineTone mappinglcsh:TechnologyRetinaGeneral Biochemistry Genetics and Molecular BiologyImage (mathematics)BiomimeticsDistortionImage Interpretation Computer-AssistedHumansContrast (vision)Computer visionlcsh:ScienceHigh dynamic rangeGeneral Environmental Sciencemedia_commonDynamic rangebusiness.industrylcsh:Tlcsh:RGeneral MedicineImage EnhancementHuman visual system modellcsh:QArtificial intelligence[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]businessAlgorithmsColor PerceptionResearch ArticleThe Scientific World Journal
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