Froth image analysis by use of transfer learning and convolutional neural networks

QR Code

Froth image analysis by use of transfer learning and convolutional neural networks

Deep learning constitutes a significant recent advance in machine learning and has been particularly successful in applications related to image processing, where it can already surpass human accuracy in some cases. In this paper, the use of a convolutional neural network, AlexNet, pretrained on a d...

Full description

Bibliographic Details
Main Authors:	Fu, Y., Aldrich, Chris
Format:	Journal Article
Published:	Elsevier 2018
Online Access:	http://hdl.handle.net/20.500.11937/68118

Similar Items

Using Convolutional Neural Networks to Develop State-of-the-Art Flotation Froth Image Sensors
by: Fu, Y., et al.
Published: (2018)

Recognition of flotation froth conditions with k-shot learning and convolutional neural networks
by: Liu, Xiu, et al.
Published: (2023)

Performance of Convolutional Neural Networks for Feature Extraction in Froth Flotation Sensing
by: Horn, Z., et al.
Published: (2017)

Flotation Froth Image Analysis by Use of a Dynamic Feature Extraction Algorithm
by: Fu, Y., et al.
Published: (2016)

The estimation of platinum flotation grade from froth image features by using artificial neural networks
by: Marais, C., et al.
Published: (2011)

Recent Advances in Flotation Froth Image Analysis
by: Aldrich, Chris, et al.
Published: (2022)

Multivariate image analysis of realgar–orpiment flotation froths
by: Aldrich, Chris, et al.
Published: (2017)

Estimation of platinum flotation grades from froth image data
by: Marais, C., et al.
Published: (2011)

The Relationship between Froth Image Features and Platinum Flotation Grade
by: Marais, C., et al.
Published: (2010)

Motion Estimation in Flotation Froth Images Based on Edge Detection and Mutual Information
by: Amankwah, Anthony, et al.
Published: (2012)

Motion estimation in flotation froth using the Kalman filter
by: Amankwah, A., et al.
Published: (2015)

Multivariate image analysis of a realgar-orpiment batch froth flotation system
by: Aldrich, Chris, et al.
Published: (2014)

Robust Block-Matching Motion Estimation of Flotation Froth Using Mutual Information
by: Amankwah, A., et al.
Published: (2012)

Machine vision-based motion estimation of flotation froth using mutual information
by: Amankwah, A., et al.
Published: (2011)

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions
by: Munanday, Anbananthan Pillai, et al.
Published: (2023)

Grade estimation in realgar-orpiment batch flotation systems by froth image analysis and kernel methods
by: Aldrich, Chris, et al.
Published: (2014)

Review of deep convolution neural network in image classification
by: Al-Saffar, Ahmed Ali Mohammed, et al.
Published: (2017)

Image Splicing Detection With Constrained Convolutional Neural Network
by: Lee, Yang Yang
Published: (2019)

Automatic flotation froth bubble size distribution estimation using mean shift and watershed transforms.
by: Amankwah, A., et al.
Published: (2014)

Relationship between solids flux and froth features in batch flotation of sulphide ore
by: Yang, X., et al.
Published: (2005)

Matching fingerprint images for biometric authentication using convolutional neural networks
by: Najih, Abdulmawla, et al.
Published: (2019)

A review of Convolutional Neural Networks in Remote Sensing Image
by: Liu, Xinni, et al.
Published: (2019)

Automatic estimation of bubble size distributions in flotation froths by use of a mean shift algorithm and watershed transforms
by: Amankwah, A., et al.
Published: (2014)

Classification of corals in reflectance and fluorescence images using convolutional neural network representations
by: Xu, L., et al.
Published: (2018)

Detection of proliferative diabetic retinopathy in fundus images using convolution neural network
by: Hasliza, Abu Hassan, et al.
Published: (2020)

Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
by: Md Hasim, Siti Mastura
Published: (2017)

Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
by: Hasim, Siti Mastura Binti Md
Published: (2017)

Online monitoring and control of froth flotation systems with machine vision:A review
by: Aldrich, Chris, et al.
Published: (2010)

Convolutional neural network-based finger vein recognition using near infrared images
by: Fairuz, Subha, et al.
Published: (2018)

Image-based oil palm leaf disease detection using convolutional neural network
by: Jia, Heng Ong, et al.
Published: (2022)

Performance analysis of convolutional neural networks extended with predefined kernels in image classification / Arash Fatehi
by: Arash , Fatehi
Published: (2022)

Leaf condition analysis using convolutional neural network and vision transformer
by: Yong, Wai Chun, et al.
Published: (2024)

Convolutional neural network model in machine learning methods and computer vision for image recognition: a review
by: R. M. Q. R., Jaapar, et al.
Published: (2018)

A Super-Resolution Convolutional-Neural-Network-Based Approach for Subpixel Mapping of Hyperspectral Images
by: Ma, X., et al.
Published: (2019)

Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning
by: Giglia, Keith Carmelo
Published: (2022)

Handwritten character recognition using convolutional neural network
by: Khandokar, I., et al.
Published: (2021)

Calf posture recognition using convolutional neural network
by: Tung, Tan Chen, et al.
Published: (2022)

Pattern recognition for magnetic resonance knee imaging using convolutional neural network / Zhang Xinyu
by: Zhang, Xinyu
Published: (2018)

Identification of microscopy cell images by using convolutional neural network application / Norfatin Farisya Ajis
by: Norfatin Farisya , Ajis
Published: (2020)

Bleeding classification of enhanced wireless capsule endoscopy images using deep convolutional neural network
by: Rosdiana, Shahril, et al.
Published: (2020)