1 citations · 3 across the 3 of their papers we have counts for
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Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration
Dominique Beaini, Sofiane Achiche, Maxime Raison
Current research in convolutional neural networks (CNN) focuses mainly on changing the architecture of the networks, optimizing the hyper-parameters and improving the gradient desc…
Saliency Enhancement using Gradient Domain Edges Merging
Dominique Beaini, Sofiane Achiche, Alexandre Duperre +1
In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the boundaries of an image and salient object…
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks
Dominique Beaini, Sofiane Achiche, Alexandre Duperré +1
Current saliency methods require to learn large scale regional features using small convolutional kernels, which is not possible with a simple feed-forward network. Some methods so…
Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution
Dominique Beaini, Sofiane Achiche, Fabrice Nonez +4
In computer vision, the gradient and Laplacian of an image are used in different applications, such as edge detection, feature extraction, and seamless image cloning. Computing the…
Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism
Dominique Beaini, Sofiane Achiche, Yann-Seing Law-Kam Cio +1
Computer vision is a growing field with a lot of new applications in automation and robotics, since it allows the analysis of images and shapes for the generation of numerical or a…
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications
Dominique Beaini, Sofiane Achiche, Fabrice Nonez +1
In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. Other r…