Recherche

Afficher tous les résultats pour

Accueil

Concept

Méthode d'Otsu

Applied sciences
Information engineering
Intelligence artificielle
Vision par ordinateur

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (30)

Fast correlation function calculator A high-performance pair-counting toolkit

Cheng Zhao

Context. A novel high-performance exact pair-counting toolkit called fast correlation function calculator (FCFC) is presented.Aims. With the rapid growth of modern cosmological datasets, the evaluation of correlation functions with observational and simula ...

EDP SCIENCES S A2023

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Devavrat Tomar, Seyedbehzad Bozorgtabar

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

IEEE2023

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Jiancheng Yang, Li Zhang, Zhigang Li, Bo Du

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

Springer International Publishing Ag2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Jiancheng Yang, Xiang Li, Amos Sironi, Jian Zhou, Jie Zhou, Siqi Liu

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in m ...

2023

Neural Distributed Image Compression with Cross-Attention Feature Alignment

Ali Garjani

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider ...

IEEE COMPUTER SOC2023

Semantic Segmentation of Remote Sensing Images With Sparse Annotations

Devis Tuia

Training convolutional neural networks (CNNs) for very high-resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor-intensive and time-consuming to produce. Moreover, professional photograph interpreter ...

2022

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Dian Wu

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

IOP Publishing Ltd2022

Natural-Looking Adversarial Examples From Freehand Sketches

Davide Nanni, Hakgu Kim

Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...

IEEE2022

Perceiving Humans: from Monocular 3D Localization to Social Distancing

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...

IEEE Institute of Electrical and Electronics Engineers2022

Poly-NL: Linear Complexity Non-local Layers With 3rd Order Polynomials

Grigorios Chrysos, Filippos Kokkinos

Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions. Such pairwise functions underpin the effectiveness of non-lo ...

IEEE2021

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

Volkan Cevher, Cong Bang Vu

In this paper, we propose a novel splitting method for finding a zero point of the sum of two monotone operators where one of them is Lipschizian. The weak convergence the method is proved in real Hilbert spaces. Applying the proposed method to composite m ...

2021

Joint Segmentation and Path Classification of Curvilinear Structures

Pascal Fua, Agata Justyna Mosinska, Mateusz Kozinski

Detection of curvilinear structures in images has long been of interest. One of the most challenging aspects of this problem is inferring the graph representation of the curvilinear network. Most existing delineation approaches first perform binary segment ...

2020

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Claudiu-Cristian Musat, Hazim Kemal Ekenel

Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are ...

2020

Multi-View Shape Estimation of Transparent Containers

Pascal Frossard, Andrea Cavallaro, Apostolos Modas

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions. In this paper, we propose a method for jointly localising container-like obj ...

IEEE2020

Model-based image reconstruction method

Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson, Marcel Arditi, Adrien Besson

The present invention concerns a pulse wave image reconstruction method to be used for example in ultrasound imaging. The proposed method is based on an image measurement model and its adjoint operator. The proposed method introduces matrix-free formulatio ...

2019

Inpainting in Omnidirectional Images for Privacy Protection

Touradj Ebrahimi, Evgeniy Upenik, Pinar Akyazi

Privacy protection is drawing more attention with the advances in image processing, visual and social media. Photo sharing is a popular activity, which also brings the concern of regulating permissions associated with shared content. This paper presents a ...

2019

Geometry in active learning for binary and multi-class image segmentation

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

We propose an active learning approach to image segmentation that exploits geometric priors to speed up and streamline the annotation process. It can be applied for both background foreground and multi-class segmentation tasks in 2D images and 3D image vol ...

2019

Color Image Segmentation Using a Fuzzy Inference System

Matin Macktoobian

A novel method is proposed in the scope of image segmentation that solves this problem by breaking it into two main blocks. The first block's functionality is a method to anticipate the color basis of each segment in segmented images. One of the challenges ...

IEEE2019

Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

Mohammad Khaja Nazeeruddin

Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classification o ...

2019

Joint Learning of Semantic Alignment and Object Landmark Detection

Seungryong Kim

Convolutional neural networks (CNNs) based approaches for semantic alignment and object landmark detection have improved their performance significantly. Current efforts for the two tasks focus on addressing the lack of massive training data through weakly ...

IEEE2019

Page 1 sur 2