Search results for "RECOGNITION"
showing 10 items of 3607 documents
Reconstruction of hyperspectral cutaneous data from an artificial neural network-based multispectral imaging system.
2011
International audience; The development of an integrated MultiSpectral Imaging (MSI) system yielding hyperspectral cubes by means of artificial neural networks is described. The MSI system is based on a CCD camera, a rotating wheel bearing a set of seven interference filters, a light source and a computer. The resulting device has been elaborated for in vivo imaging of skin lesions. It provides multispectral images and is coupled with a software reconstructing hyperspectral cubes from multispectral images. Reconstruction is performed by a neural network-based algorithm using heteroassociative memories. The resulting hyperspectral cube provides skin optical reflectance spectral data combined…
Ontology-driven Image Analysis for Histopathological Images
2010
International audience; Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibi…
The Kolmogorov superposition theorem and its application to image processing
2010
Best student paper award; International audience
Sketch of an automatic image based pollen detection system
2012
The ability of measuring accurately airborne pollen concentration in the environment is an important goal for palynology. It has been unsatisfactory for agile usage to date. Huge volumes of airborne particles prevent palynologists from opportunely processing statistically suitable information. Additionally, measurements from stationary pollen monitors cannot be accurately associated to individuals. In the context of computer vision, this paper presents the outline for the structure of an image based pollen detection system, under the framework of the Personalized Pollen Profiling and Geospatial Mapping project based on individual information of allergic patient profile measured at multiple …
A classification approach to prostate cancer localization in 3T Multi-Parametric MRI
2016
International audience; Multiparametric-magnetic resonance imaging (mp-MRI) has demonstrated, in many studies, its potential in prostate cancer detection and analysis. We propose a supervised classification approach based on mp-MRI data base of 20 patients, in order to localize prostate cancer and to achieve a cartographic representation of the prostate voxels based on classification results. Proposed method provides a computer aided detection (CAD) software for prostatic cancer. For that, we have extracted varied features providing functional, anatomical and metabolic information helping the classifier to distinguish between three different classes ("Healthy", "Benign" and "Pathologic"). W…
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 …
Wavelet Decomposition in Laplacian Pyramid for Image Fusion
2016
International audience; The aim of image fusion is to combine information from the set of images to get a single image which contains a more accurate description than any individual source image. While the scene contains objects in different focus due to the limited depth-of-focus of optical lenses in camera then by using image fusion technique we can get an image which has better focus across all area. In this paper, a multifocus image fusion method using combination Laplacian pyramid and wavelet decomposition is proposed. The fusion process contains the following steps: first, the multifocus images are decomposed using Laplacian pyramid into several levels of pyramid. Then at each level o…
An original approach for gas chromatography-olfactometry detection frequency analysis: Application to gin
2012
Abstract Gas Chromatography-Olfactometry (GC-O) is a technique that lies halfway between physicochemical and sensory analysis and involves the perception of volatile flavour compounds, separated by gas chromatography, by the human nose. Of the different GC-O procedures available, detection frequency has been proved to be more rapid and more repeatable, even with an untrained panel. This characteristic regarding the panel is often not considered when dealing with the sensory attributes determined by assessors. An original approach to GC-O using the detection frequency procedure has been developed and tested on two types of gin and made it possible to benefit from sensory data. The panel cons…
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…
hidden markov random fields and cuckoo search method for medical image segmentation
2020
Segmentation of medical images is an essential part in the process of diagnostics. Physicians require an automatic, robust and valid results. Hidden Markov Random Fields (HMRF) provide powerful model. This latter models the segmentation problem as the minimization of an energy function. Cuckoo search (CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms. It has shown its efficiency in many engineering optimization problems. In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.