A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Performance Comparison of Convolutional Neural Network-based model using Gradient Descent Optimization algorithms for the Classification of Low Quality Underwater Images
2020
Journal of science and technolgy
Underwater imagery and analysis plays a major role in fisheries management and fisheries science helping developing efficient and automated tools for cumbersome tasks such as fish species identification, stock assessment and abundance estimation. Majority of the existing tools for analysis still leverage conventional statistical algorithms and handcrafted image processing techniques which demand human interventions and are inefficient and prone to human errors. Computer vision based automated
doi:10.46243/jst.2020.v5.i5.pp227-236
fatcat:ygz4pj37xzcv5eaa2wrixz3boe