H
Hamid Laga
Researcher at Murdoch University
Publications - 126
Citations - 3218
Hamid Laga is an academic researcher from Murdoch University. The author has contributed to research in topics: Shape analysis (digital geometry) & Computer science. The author has an hindex of 22, co-authored 122 publications receiving 2035 citations. Previous affiliations of Hamid Laga include Institut Mines-Télécom & University of South Africa.
Papers
More filters
Journal ArticleDOI
A Comprehensive Survey of Deep Learning for Image Captioning
TL;DR: A comprehensive review of deep learning-based image captioning techniques can be found in this article, where the authors discuss the foundation of the techniques to analyze their performances, strengths, and limitations.
Journal ArticleDOI
Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era
TL;DR: A comprehensive survey of the recent developments in 3D reconstruction using convolutional neural networks, focusing on the works which use deep learning techniques to estimate the 3D shape of generic objects either from a single or multiple RGB images.
Posted Content
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users
TL;DR: This tutorial provides deep learning practitioners with an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian neural networks, i.e., stochastic artificial neural networks trained using Bayesian methods.
Journal ArticleDOI
Detection and analysis of wheat spikes using Convolutional Neural Networks.
TL;DR: Deep learning techniques can achieve high accuracy in detecting and counting spikes from complex wheat field images and the proposed robust R-CNN model, which has been trained on spike images captured during different growth stages, is optimized for application to a wider variety of field scenarios.
Journal ArticleDOI
A survey of deep learning techniques for weed detection from images
A S M Mahmudul Hasan,Ferdous Sohel,Dean Diepeveen,Dean Diepeveen,Hamid Laga,Michael G. K. Jones +5 more
TL;DR: In this article, a review of existing deep learning-based weed detection and classification techniques is presented, which includes data acquisition, dataset preparation, DL techniques employed for detection, location and classification of weeds in crops, and evaluation metrics approaches.