Data/hora: 01/06/2024 04:22:26

Provedor de dados: 	5	
País: 	France	
Título: 	Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG plus SVM Methods	
Autores: 	Villon, Sebastien; Chaumont, Marc; Subsol, Gerard; Villeger, Sebastien; Claverie, Thomas; Mouillot, David.	
Data: 	2016	
Ano: 	2016	
Palavras-chave: 	Support Vector Machine; Feature Vector; Coral Reef; Deep Learn; Convolutional Neural Network.	
Resumo: 	In this paper, we present two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos. The first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and weaknesses.	
Tipo: 	Text	
Idioma: 	Inglês	
Identificador: 	https://archimer.ifremer.fr/doc/00387/49860/74458.pdf

DOI:10.1007/978-3-319-48680-2_15

https://archimer.ifremer.fr/doc/00387/49860/	
Editor: 	Blanc-Talon J., Distante C., Philips W., Popescu D., Scheunders P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016.Lecture Notes in Computer Science, vol 10016. Springer, Cham. pp.160-171	
Formato: 	application/pdf	
Fonte: 	info:eu-repo/semantics/openAccess

restricted use