Bilal Mokhtari

• PhD on Computer Graphics
• PhD at Université Bourgogne Europe

Agriculture is critical in global food security but faces challenges like declining arable land and climate change. Machine learning, particularly deep learning with Convolutional Neural Networks (CNN), has shown promise in addressing agricultural problems. Efficient and accurate identification of plant diseases is essential for guaranteeing the lo...

In machine learning, data augmentation stands out as a potent strategy to overcome the constraints imposed by limited training data, as well as unbalanced and low-quality data, ultimately enhancing model accuracy. This work introduces a novel data augmentation technique rooted in optimization, seamlessly integrating a genetic algorithm. It unlocks...

Although there is a wide range of shape descriptors available in the literature, most of them are restricted to a specific class of shapes and no one can achieve satisfactory shape retrieval results when used with different classes of shapes. Introducing new descriptors, improving, or merging existing descriptors are potential strategies for enhanc...

3D Shape retrieval algorithms use shape descriptors to identify shapes in a database that are the most similar to a given key shape, called the query. Many shape descriptors are known but none is perfect. Therefore, the common approach in building 3D Shape retrieval tools is to combine several descriptors with some fusion rule. This article propose...

Dissimilarity or distance metrics are the cornerstone of shape matching and retrieval algorithms. As there is no unique dissimilarity measure that gives good performances in all possible configurations, these metrics are usually combined to provide reliable results. In this paper we propose to compute the best linear convex, or weighted, combinatio...

The majority of shape matching and retrieval methods use only one single shape descriptor. Unfortunately, no shape descriptor is sufficient to provide suitable results for all kinds of shapes. The most common way to improve the performance of shape descriptors is to fuse them. In this paper, we propose a new 3D matching and retrieval approach based...

The majority of shape matching and retrieval methods use only one single shape descriptor. Unfortunately, no shape descriptor is sufficient to provide suitable results for all kinds of shapes. The most common way to improve the performance of shape descriptors is to fuse them. In this paper, we propose a new 3D matching and retrieval approach based...

Cette thèse porte sur l’appariement des formes, et la recherche par forme clef. Elle décrit quatrecontributions à ce domaine. La première contribution est une amélioration de la méthode des nuéesdynamiques pour partitionner au mieux les voxels à l’intérieur d’une forme donnée ; les partitionsobtenues permettent d’apparier les objets par un couplage...

This paper presents a shape matching framework based on a new shape decomposition approach. A new region-based shape descriptor is proposed to compute the best match between given 2D or 3D shapes. In order to find similar shapes in a database, we first split the interior of each shape into the adequate set of parts, classes, or ellipsoids, then fin...