Search results for "Computer Vision and Pattern Recognition"
showing 10 items of 997 documents
The Kolmogorov superposition theorem and its application to image processing
2010
Best student paper award; International audience
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…
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.
Unsupervised learning of category-specific symmetric 3D keypoints from point sets
2020
Lecture Notes in Computer Science, 12370
3D landmark detection for augmented reality based otologic procedures
2019
International audience; Ear consists of the smallest bones in the human body and does not contain significant amount of distinct landmark points that may be used to register a preoperative CT-scan with the surgical video in an augmented reality framework. Learning based algorithms may be used to help the surgeons to identify landmark points. This paper presents a convolutional neural network approach to landmark detection in preoperative ear CT images and then discusses an augmented reality system that can be used to visualize the cochlear axis on an otologic surgical video.
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,…
Repérage précis de caméras multispectrales et de scanners 3D pour le recalage de données multicapteurs appliqué à l'étude du patrimoine
2012
Session "Atelier V3DPAT"; National audience; Nos travaux portent sur le recalage de données multi-capteurs et spécifiquement sur la projection de textures 2D sur des modèles 3D d'objet du patrimoine en pierre. Nous nous intéressons particulièrement aux textures acquises par imagerie multispectrale mais notre technique est également adaptée à d'autres systèmes optiques d'acquisition tels que l'imagerie thermique. Les modèles 3D, eux, sont acquis par un système de projection de franges. La difficulté du recalage multicapteur vient principalement de la variation de la représentation de l'objet. Ainsi, les points saillants d'un jeu de données ne correspondent pas forcément à ceux d'une autre re…
Reconnaissance de la Forme 3D et Estimation de la Profondeur Implémentation sur FPGA Spartan 3A d'un SoC pour la Vision 3D (Shape From Focus) Problém…
2007
Le terme de « vision 3D » ou « de numérisation 3D », est apparu à la fin des années 1990, pour désigner des techniques d'acquisition de mesures tridimensionnelle sur des surfaces, techniques ayant la caractéristique de donner des nuages de points denses et importants dont l'ordre de grandeur est de quelques dizaines à plusieurs millions de points. Le nuage de points représente en fait l'information de l'image de profondeur et selon des différents traitements à l'image on peut aboutir à un ordre de précision de la reconstitution de l'objet ou scène en 3D. La vision 3D demeure une méthodologie de base pour réassurer le mécanisme de reconstitution des images tridimensionnelles. Outre les besoi…