Results 101 to 110 of about 3,882,996 (203)

CNN-BASED SOLUTION FOR MANGO CLASSIFICATION IN AGRICULTURAL ENVIRONMENTS

Telemática
This article exemplifies the design of a fruit detection and classification system using Convolutional Neural Networks (CNN). The goal is to develop a system that automatically assesses fruit quality for farm inventory management. Specifically, a method
Español (España) Español (España), Jorge Torres Gómez, Ariel Fajardo Márquez   +2 more
doaj  

Architecture of the proposed convolutional neural networks-based classifier.

, 2019
Architecture of the proposed convolutional neural networks-based classifier.
Panikos Heracleous (7251320), Akio Yoneyama (3909751)   +1 more
core   +1 more source

Boosted Convolutional Neural Networks [PDF]

Procedings of the British Machine Vision Conference 2016, 2016
Mohammad Moghimi, Serge J. Belongie, Mohammad J. Saberian, Jian Yang 0003, Nuno Vasconcelos, Li-Jia Li 0001   +5 more
openaire   +2 more sources

Behavior Modeling by Neural Networks

, 2009
Modeling of human and animal behavior is of interest for a number of diagnostic purposes. Convolutional neural networks offer a constructive approach allowing learning on a limited number of examples.
Spaanenburg, Lambert, Peter B. L. Meijer, Lambert Spaanenburg, Meijer, Peter B. L., Tehrani, Mona Akbarniai, Mona Akbarniai Tehrani, Kleihorst, Richard, Richard Kleihorst, Spaanenburg, Lambert,, Lund University.   +9 more
core   +1 more source

Deep Learning 04 - Convolutional Neural Networks

, 2018
Convolutional Neural Networks are introduced, a powerful tool for managing images, videos and speech and also the historical foundation of deep networks.
Cristina De Castro, IEIITCNR
core  

On the Efficiency of Convolutional Neural Networks

CoRR
Since the breakthrough performance of AlexNet in 2012, convolutional neural networks (convnets) have grown into extremely powerful vision models. Deep learning researchers have used convnets to perform vision tasks with accuracy that was unachievable a decade ago. Confronted with the immense computation that convnets use, deep learning researchers also
openaire   +3 more sources

Hybrid quantum-classical-quantum convolutional neural networks

Scientific Reports
Deep learning has achieved significant success in pattern recognition, with convolutional neural networks (CNNs) serving as a foundational architecture for extracting spatial features from images.
Changzhou Long, Meng Huang, Xiucai Ye, Yasunori Futamura, Tetsuya Sakurai   +4 more
doaj   +1 more source

Facial Expression Analysis using Shape and Motion Information Extracted by Convolutional Neural Networks

In this paper we discuss a neural networks-based face analysis approach that is able to cope with faces subject to pose and lighting variations. Especially head pose variations are difficult to tackle and many face analysis methods require the use of ...
Fasel, B., B. Fasel
core   +1 more source

Invertibility of convolutional generative networks from partial measurements

, 2019
The problem of inverting generative neural networks (i.e., to recover the input latent code given partial network output), motivated by image inpainting, has recently been studied by a prior work that focused on fully-connected networks. In this work, we
Ayaz, Ulas, Karaman, Sertac, Ma, Fangchang   +2 more
core  

Adaptive Detrending for Accelerating the Training of Convolutional Recurrent Neural Networks [PDF]

Convolutional recurrent neural networks (ConvRNNs) provide robust spatio-temporal information processing capabilities for contextual video recognition, but require extensive computation that slows down training. Inspired by detrending methods, we propose
Tani, Jun, Jung, Minju
core   +1 more source