output
20082026
most citedVine disease detection in UAV multispectral images with deep learning segmentation approach

315 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 233 cited

Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images

Reenul Reedha, Eric Dericquebourg, Raphael Canals +1

Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agricul…

cs.CV2021

Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images

Tarek Ben Charrada, Hedi Tabia, Aladine Chetouani +1

We propose a novel deep reinforcement learning-based approach for 3D object reconstruction from monocular images. Prior works that use mesh representations are template based. Thus…

eess.IV2021★ 10 cited

A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis

Salima Bourbia, Ayoub Karine, Aladine Chetouani +1

This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method. In the field of stereoscopic vision, th…

math.OC2021★ 1 cited

Conic nonholonomic constraints on surfaces and control systems

Timothée Schmoderer, Witold Respondek

This paper addresses the equivalence problem of conic submanifolds in the tangent bundle of a smooth 2-dimensional manifold. Those are given by a quadratic relation between the vel…

stat.ML2021

Sample selection from a given dataset to validate machine learning models

Bertrand Iooss

The selection of a validation basis from a full dataset is often required in industrial use of supervised machine learning algorithm. This validation basis will serve to realize an…

math.OC2021

The stochastic Auxiliary Problem Principle in Banach spaces: measurability and convergence

Thomas Bittar, Pierre Carpentier, Jean-Philippe Chancelier +1

The stochastic Auxiliary Problem Principle (APP) algorithm is a general Stochastic Approximation (SA) scheme that turns the resolution of an original optimization problem into the…