Search results for "Histogram"

showing 10 items of 115 documents

Multi-channel chromatic transformations for nonlinear color pattern recognition

2002

We present a new approach for color pattern recognition based on multi-channel nonlinear correlations. High discrimination capability is obtained in comparison with common linear multi-channel detection methods. We apply the nonlinear morphological correlation to different color channel decompositions as RGB and ATD channels. Moreover, in order to improve the discrimination we have introduced a new color transformation. When a high selectivity is required, the combination of the nonlinear correlation and the new color decomposition yields to detect the object using just a single channel. Simulation results are provided.

Color histogramChannel (digital image)business.industryComputer scienceColor imageColor normalizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONColor balancePattern recognitionImage processingHSL and HSVColor spaceAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic MaterialsOpticsRGB color modelArtificial intelligenceElectrical and Electronic EngineeringPhysical and Theoretical ChemistrybusinessComputer Science::Information TheoryOptics Communications
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A Gamut Preserving Color Image Quantization

2007

International audience; We propose a new approach for color image quantization which preserves the shape of the color gamut of the studied image. Quantization consists to find a set of color representative of the color distribution of the image. We are looking here for an optimal LUT (look up table) which contains information on the image's gamut and on the color distribution of this image. The main motivation of this work is to control the reproduction of color images on different output devices in order to have the same color feeling, coupling intrinsic informations on the image gamut and output device calibration. We have developped a color quantization algorithm based on an image depend…

Color histogramColor imagebusiness.industry010102 general mathematicsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONColor balance02 engineering and technologyColor space01 natural sciencesColor quantizationGamut[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Computer Science::Computer Vision and Pattern Recognition[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingColor depth0202 electrical engineering electronic engineering information engineeringRGB color model020201 artificial intelligence & image processingComputer visionArtificial intelligence0101 mathematicsbusinessComputingMethodologies_COMPUTERGRAPHICSMathematics14th International Conference of Image Analysis and Processing - Workshops (ICIAPW 2007)
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A Combinatorial Color Edge Detector

2004

In this paper, we present an edge detection approach in color image using neighborhood hypergraph. The edge structure is detected by a structural model. The Color Image Neighborhood Hypergraph (CINH) representation is first computed, then the hyperedges of CINH are classified into noise or edge based on hypergraph properties. To evaluate the algorithm performance, experiments were carried out on synthetic and real color images corrupted by alpha-stable noise. The results show that the proposed edge detector finds the edges properly from color images.

Color histogramComputer scienceColor imagebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionColor spaceEdge detectionColor quantizationRGB color modelColor filter arrayArtificial intelligencebusinessImage gradientMathematicsofComputing_DISCRETEMATHEMATICS
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Semantic and topological classification of images in magnetically guided capsule endoscopy

2012

International audience; Magnetically-guided capsule endoscopy (MGCE) is a nascent technology with the goal to allow the steering of a capsule endoscope inside a water filled stomach through an external magnetic field. We developed a classification cascade for MGCE images with groups images in semantic and topological categories. Results can be used in a post-procedure review or as a starting point for algorithms classifying pathologies. The first semantic classification step discards over-/under-exposed images as well as images with a large amount of debris. The second topological classification step groups images with respect to their position in the upper gastrointestinal tract (mouth, es…

Color histogramComputer scienceFeature extraction[INFO.INFO-IM] Computer Science [cs]/Medical ImagingImage processingFundus (eye)Content-based image retrieval030218 nuclear medicine & medical imaginglaw.invention03 medical and health sciences0302 clinical medicineDiscriminative modelCapsule endoscopylaw[INFO.INFO-IM]Computer Science [cs]/Medical ImagingmedicineUpper gastrointestinalComputer visionSegmentationAntrumContextual image classification[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryStomachmedicine.anatomical_structureFeature (computer vision)Duodenum030211 gastroenterology & hepatologyArtificial intelligencebusiness
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Color and Flow Based Superpixels for 3D Geometry Respecting Meshing

2014

We present an adaptive weight based superpixel segmentation method for the goal of creating mesh representation that respects the 3D scene structure. We propose a new fusion framework which employs both dense optical flow and color images to compute the probability of boundaries. The main contribution of this work is that we introduce a new color and optical flow pixel-wise weighting model that takes into account the non-linear error distribution of the depth estimation from optical flow. Experiments show that our method is better than the other state-of-art methods in terms of smaller error in the final produced mesh.

Color histogramComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONOptical flow010103 numerical & computational mathematics02 engineering and technologyImage segmentation01 natural sciencesWeightingDistribution (mathematics)[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Flow (mathematics)Computer Science::Computer Vision and Pattern Recognition[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligence0101 mathematicsbusinessRepresentation (mathematics)Adaptive opticsComputingMilieux_MISCELLANEOUS
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Karhunen-Loe`ve transform applied to region-based segmentation of color aerial images

2001

The use of the Karhunen-Loeve transform (KLT) for region- based segmentation of aerial images by color and textural attributes is presented. Our aerial images are shown to be homogeneous color im- ages within the Karhunen-Loeve color representation space, which means they can be represented more easily and the region-based seg- mentation algorithms can be optimized. For texture analysis, the KLT is the basis of the local linear transform (LLT) and allows structural infor- mation about textures to be represented in an optimal and condensed manner. The LLT provides a system of textural analysis in the form of an adapted filter bank. We end the paper by presenting a method for merg- ing textur…

Color histogramContextual image classificationColor imagebusiness.industryComputer scienceGeneral EngineeringScale-space segmentationImage processingImage segmentationAtomic and Molecular Physics and OpticsImage textureRGB color modelComputer visionSegmentationArtificial intelligencebusinessOptical Engineering
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Perceptual similarity between color images using fuzzy metrics

2016

A method to measure the similarity between color images is proposed.Correlation among the color image channels is taken into account.Proposed similarity measure is based on fuzzy metrics because of their advantages.The proposal matches well with the perceptual visual similarity between color images. In many applications of the computer vision field measuring the similarity between (color) images is of paramount importance. However, the commonly used pixelwise similarity measures such as Mean Absolute Error, Peak Signal to Noise Ratio, Mean Squared Error or Normalized Color Difference do not match well with perceptual similarity. Recently, it has been proposed a method for gray-scale image s…

Color histogramMean squared errorColor similarityComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologySimilarity measureFuzzy logicLow level image processingFuzzy metricsSimilarity (network science)0202 electrical engineering electronic engineering information engineeringMedia TechnologyComputer visionElectrical and Electronic EngineeringMathematicsPerceptual image similarityColor differencebusiness.industryColor image020206 networking & telecommunicationsPattern recognitionColor imagingPeak signal-to-noise ratioPerceptual observationsColor image qualityFuzzy logicComputer Science::Computer Vision and Pattern RecognitionSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessJournal of Visual Communication and Image Representation
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A neural network based automatic road signs recognizer

2003

Automatic road sign recognition systems are aimed at detection and recognition of one or more road signs from real-world color images. In this research, road signs are detected and extracted from real world scenes on the basis of their color and shape features. A dynamic region growing technique is adopted to enhance color segmentation results obtained in the HSV color space. The technique is based on a dynamic threshold that reduces the effect of hue instability in real scenes due to external brightness variation. Classification is then performed on extracted candidate regions using multilayer perceptron neural networks. The obtained results show good detection and recognition rates of the…

Color histogramPixelArtificial neural networkColor normalizationComputer scienceColor imagebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCognitive neuroscience of visual object recognitionPattern recognitionHSL and HSVImage segmentationRegion growingSegmentationComputer visionArtificial intelligencebusinessHue
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Application of the S-CIELAB color model to processed and calibrated images with a colorimetric dithering method.

2009

This work uses the S-CIELAB color model to compare images that have been calibrated and processed using a colorimetric dithering method which simulates increments in viewing distance. Firstly, we obtain XYZ calibrated images by applying the appropriate color transformations to the original images. These transformations depend on whether the image is viewed on a display device or encoded by a capture device, for example. Secondly, we use a colorimetric dithering method consisting of a partitive additive mixing of XYZ tristimulus values. The number of dithered pixels depends on simulated viewing distance. The dithered tristimulus values are transformed to digital data to observe the dithering…

Color histogramPixelColor differencebusiness.industryComputer scienceColor imageColor normalizationDigital imagingColor balanceColor spaceAtomic and Molecular Physics and OpticsColor quantizationColor modelOpticsICC profileColor depthRGB color modelColor filter arrayDitherbusinessColorimetryImage resolutionOptics express
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On the uniform sampling of CIELAB color space and the number of discernible colors

2013

This paper presents a useful algorithmic strategy to sample uniformly the CIELAB color space based on close packed hexagonal grid. This sampling scheme has been used successfully in different research works from computational color science to color image processing. The main objective of this paper is to demonstrate the relevance and the accuracy of the hexagonal grid sampling method applied to the CIELAB color space. The second objective of this paper is to show that the number of color samples computed depends on the application and on the color gamut boundary considered. As demonstration, we use this sampling to support a discussion on the number of discernible colors related to a JND.

Color histogram[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONcomputational color imagingColor balance[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyperceptually uniform color spaceColor space01 natural sciences010309 optics[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingICC profile0103 physical sciencesColor depth[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering3D close packed hexagonal gridComputer visionSamplingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICSMathematicsColor differencebusiness.industry020207 software engineeringColor quantizationColor modelArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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