Search results for " Segmentation"
showing 10 items of 462 documents
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…
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 …
Can SegFormer be a True Competitor to U-Net for Medical Image Segmentation?
2023
The U-Net model, introduced in 2015, is established as the state-of-the-art architecture for medical image segmentation, along with its variants UNet++, nnU-Net, V-Net, etc. Vision transformers made a breakthrough in the computer vision world in 2021. Since then, many transformer based architectures or hybrid architectures (combining convolutional blocks and transformer blocks) have been proposed for image segmentation, that are challenging the predominance of U-Net. In this paper, we ask the question whether transformers could overtake U-Net for medical image segmentation. We compare SegFormer, one of the most popular transformer architectures for segmentation, to U-Net using three publicl…
Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation
2022
Automatic and accurate segmentation of the left atrial (LA) cavity and scar can be helpful for the diagnosis and prognosis of patients with atrial fibrillation. However, automating the segmentation can be difficult due to the poor image quality, variable LA shapes, and small discrete regions of LA scars. In this paper, we proposed a fully-automatic method to segment LA cavity and scar from Late Gadolinium Enhancement (LGE) MRIs. For the loss functions, we propose two different losses for each task. To enhance the segmentation of LA cavity from the multicenter dataset, we present a hybrid loss that leverages Dice loss with a polynomial version of cross-entropy loss (PolyCE). We also utilize …
Multi-Kernel Implicit Curve Evolution for Selected Texture Regions Segmentation in VHR Satellite Images
2014
Very high resolution (VHR) satellite images provide a mass of detailed information which can be used for urban planning, mapping, security issues, or environmental monitoring. Nevertheless, the processing of this kind of image is timeconsuming, and extracting the needed information from among the huge quantity of data is a real challenge. For some applications such as natural disaster prevention and monitoring (typhoon, flood, bushfire, etc.), the use of fast and effective processing methods is demanded. Furthermore, such methods should be selective in order to extract only the information required to allow an efficient interpretation. For this purpose, we propose a texture region segmentat…
ROBUST ROAD SIGNS SEGMENTATION IN COLOR IMAGES
2012
International audience; This paper presents an efficient method for road signs segmentation in color images. Color segmentation of road signs is a difficult task due to variations in the image acquisition conditions. Therefore, a color constancy algorithm is usually applied prior to segmentation, which increases the computation time. The proposed method is based on a log-chromaticity color space which shows good invariance properties to changing illumination. Thus, the method is simple and fast since it does not require color constancy algorithms. Experiments with a large dataset and comparison with other approaches, show the robustness and accuracy of the method in detecting road signs in …
Road Signs Detection and Reconstruction using Gielis Curves
2012
International audience; Road signs are among the most important navigation tools in transportation systems. The identification of road signs in images is usually based on first detecting road signs location using color and shape information. In this paper, we introduce such a two-stage detection method. Road signs are located in images based on color segmentation, and their corresponding shape is retrieved using a unified shape representation based on Gielis curves. The contribution of our approach is the shape reconstruction method which permits to detect any common road sign shape, i.e. circle, triangle, rectangle and octagon, by a single algorithm without any training phase. Experimental…
A Study on Patch-Based Progressive Coding Schemes of Semi-Regular 3D Meshes for Local Wavelet Compression and View-Dependent Transmission
2010
International audience; This paper firstly introduces a wavelet-based segmentation for three-dimensional (3D) Semi-Regular (SR) meshes, as a pre-processing step, in a region-independent progressive coding algorithm. The proposed segmentation process aims at producing homogeneous regions with respect to their frequency amplitudes on the mesh surface, in other words: patches with different degrees of roughness. We have then studied the behavior of the wavelets, obtained during the independent coding of each region, especially close to the patch boundaries. The main contribution of this paper consists in considering three different possible wavelet decompositions, close to the region borders, …
Generic heuristics on GPU to superpixel segmentation and application to optical flow estimation
2020
Finding clusters in point clouds and matching graphs to graphs are recurrent tasks in computer science domain, data analysis, image processing, that are most often modeled as NP-hard optimization problems. With the development and accessibility of cheap multiprocessors, acceleration of the heuristic procedures for these tasks becomes possible and necessary. We propose parallel implantation on GPU (graphics processing unit) system for some generic algorithms applied here to image superpixel segmentation and image optical flow problem. The aim is to provide generic algorithms based on standard decentralized data structures to be easy to improve and customized on many optimization problems and…
Statistical atlas based exudate segmentation
2013
International audience; Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent referen…