0000000001219402

AUTHOR

Alexandre Krebs

showing 5 related works from this author

Color and multispectral image processing for the detection of inflammatory lesions of the stomach

2019

The work presented in this manuscript is part of the ANR project EMMIE. This project aims to develop an innovative multimodal system for the detection of inflammatory lesions in the stomach. To this purpose, a prototype has been developed to be able to acquire NBI endoscopic images and multispectral images during human's antrum exploration. The prototype is made of a standard endoscope and multispectral images.The prototype can acquire two types of data: NBI images and spectra. These two modalities are processed independently. Common image processing features are used to recognize four kind of diseases: active gastritis, chronic gastritis, metaplasia and atrophy. In addition, visual based f…

Machine LearningNarrow Band Imaging and multispectral imaging[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Classification des lésions de l'estomacApprentissage par transfertClassification of stomach lesionsInverse problem and optimizationApprentissage superviséTransfer LearningEndoscopie digestiveProblèmes inverses et optimisationDigestive endoscopyImagerie NBI et multispectrale
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Estimating intrinsic image from successive images by solving underdetermined and overdetermined systems of the dichromatic model

2020

International audience; Estimating an intrinsic image from a sequence of successive images taken from an object at different angles of illumination can be used in various applications such as objects recognition, color classification, and the like; because, in so doing, it can provide more visual information. Meanwhile, according to the well-known dichromatic model, each image can be considered a linear combination of three components, including intrinsic image, shading factor, and specularity. In this study, at first, two simple independent constrained and parallelized quadratic programming steps were used for computing values of the shading factor and the specularity of each successive of…

PixelUnderdetermined systemComputer sciencebusiness.industry[INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONSingular value decompositionIntrinsic image[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]Dichromatic ModelOverdetermined systemGamutSpecularity[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Singular value decompositionComputer visionQuadratic programmingArtificial intelligenceLinear combinationbusinessComputingMethodologies_COMPUTERGRAPHICS
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Quadratic Objective Functions for Dichromatic Model Parameters Estimation

2017

International audience; In this paper, we present a novel method to estimate dichromatic model parameters from a single color image. Estimation of reflectance, shading and specularity has many applications such as shape recovery, specularity removal and facilitates classical image processing and computer vision tasks such as segmentation or classification. Our method is based on two successive and independent constrained quadratic programming steps to recover the parameters of the model. Compared to recent methods, our approach has the advantage to transform a complex inverse problem into two parralelizable optimization steps that are much easier to solve. We have compared our method with r…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingLinear programmingColor imagebusiness.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 & telecommunicationsImage processing02 engineering and technologyInverse problem[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Quadratic equation[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Specularity[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingRobustness (computer science)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionQuadratic programmingArtificial intelligencebusinessAlgorithmMathematics
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[Regular Paper] Detection of H. pylori Induced Gastric Inflammation by Diffuse Reflectance Analysis

2018

Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomach's spectra with an accuracy of 98%. These results are promising and the same pipeline could be adapted for …

Pathologymedicine.medical_specialtybiologybusiness.industryStomachdigestive oral and skin physiologyLinear svmSubtractionEarly detectionInflammationHelicobacter pyloribiology.organism_classification01 natural sciencesGastric DiseasesMouse Stomach010309 optics03 medical and health sciences0302 clinical medicinemedicine.anatomical_structure030220 oncology & carcinogenesis0103 physical sciencesMedicinemedicine.symptombusiness2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)
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Detection of H. pylori induced gastric inflammation by diffuse reflectance analysis

2018

International audience; Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomachs spectra with an accuracy of 98%. These results are promising and the same pipelin…

Diffuse Reflectance Analysis[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]ComputingMethodologies_PATTERNRECOGNITIONHelicobacter pylori[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processingdigestive oral and skin physiology[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Inflammatory lesionsGastric DiseasesSupervised Learning
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