0000000000494476
AUTHOR
Christian M. D. Trinh
showing 3 related works from this author
Biometric Fish Classification of Nordic Species Using Convolutional Neural Network with Squeeze-and-Excitation
2018
Master's thesis Information- and communication technology IKT590 - University of Agder 2018 Squeeze-and-Excitation (SE) is a technique within convolutional neural networks (CNN) that can be applied to existing CNNs by applying fullyconnected layers between convolutional layers and merging the outputs. SE was the winning architecture of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2017. In this thesis, we propose a CNN using the SE architecture for classifying images of sh. Previous work in the eld relies on applying lters to the images to separate the sh from the background or sharpen the images by removing background noise. The images from the dataset are extracted fro…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
2019
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
2019
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…