Book Chapter10.1007/978-3-319-62428-0_11
Neural-Network Based Algorithm for Algae Detection in Automatic Inspection of Underwater Pipelines
Edgar Medina,Mariane R. Petraglia,Jose Gabriel R. C. Gomes +2 more
- 23 Oct 2016
- pp 141-148
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TL;DR: The algorithm comprises a neural network and a wavelet-based feature extractor that takes into account an appropriate algae texture description, as well as the neural network architecture that results in the optimal classifier performance.
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Abstract: Automatic inspection of underwater pipelines has been a growing challenge for the detection and classification of events, most often performed by Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). This article describes an algorithm for algae detection in underwater pipelines. The algorithm comprises a neural network and a wavelet-based feature extractor. Statistical parameters of the wavelet coefficients that take into account an appropriate algae texture description, as well as the neural network architecture that results in the optimal classifier performance, are selected. A post-processing algorithm, based on clustering of neighboring detection positions, was implemented to enhance the system response. The success rate of the resulting neural network classifier is 93.60%. When compared to support-vector machines (SVMs), the proposed classifier presents similar performance with the advantage of running significantly faster.
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Citations
Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments
TL;DR: A systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms is presented in this paper, which investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time.
Pipeline tracking and event classification for an automatic inspection vision system
Felipe R. Petraglia,Roberto Campos,Jose Gabriel R. C. Gomes,Mariane R. Petraglia +3 more
- 01 May 2017
TL;DR: Algorithms designed to segment the pipelines and to classify some important events are described, including a deep convolutional neural network algorithm and a wavelet-based multilayer perceptron that outperforms the perceptron algorithm for different event classes and without requiring manual feature extraction.
41
Advances in Computational Intelligence
Grigori Sidorov,Oscar Herrera-Alcántara +1 more
- 01 Jan 2017
TL;DR: The results show that as a tendency the presence of the NE helps to classify, but there are specific authors when NE do not help and even make the classification worse (about 10% of experimental data).
19
Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning
TL;DR: In this paper , a multi-label classification method based on multiple instance learning was proposed to further shorten the diagnosis time by analyzing polyculture images, which achieved an ROC AUC above 0.9.
•Dissertation
Classification of underwater pipeline events using deep convolutional neural networks
José Gabriel R. C. Gomes
- 28 Feb 2017
TL;DR: A inspecao automatica de dutos submarinos tem sido uma tarefa de crescente importância para a deteccao de diferentes tipos oficiais de eventos, dos quais destacam-se armadura exposta, presenca de algas, flanges e manta.
References
Wavelet transform in image recognition
Ales Prochazka,A. Gavlasova,K. Volka +2 more
- 08 Jun 2005
TL;DR: Texture segmentation and classification form a very important topic of the interdisciplinarg area of signal processing with many applications in diflerent areas including satellite image processing, biomedical image analysis and microscopic image processing.
•Book
Feature Extraction Image Processing For Computer Vision
Mark S. Nixon,Alberto S. Aguado +1 more
- 09 Oct 2012
TL;DR: This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab, and contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking.
•Book
Handbook of Texture Analysis
Majid Mirmehdi,Xianghua Xie,Jasjit Suri +2 more
- 01 Aug 2008
TL;DR: This collection of chapters brings together in one handy volume the major topics of importance, and categorizes the various techniques into comprehensible concepts of texture analysis.
Update of the Brazilian floristic list of Algae and Cyanobacteria
Mariângela Menezes,Carlos Eduardo de Mattos Bicudo,Carlos Wallace do Nascimento Moura,Aigara Miranda Alves,Alana Araújo dos Santos,Alexandre de Gusmão Pedrini,Andréa Araújo,Andréa Tucci,Aurelio Fajar,Camila Malone,Cecília H. Kano,Célia Leite Sant'Anna,Ciro Cesar Zanini Branco,Clarisse Odebrecht,Cleto Kaveski Peres,Emanuel B. Neuhaus,Enide Eskinazi-Leça,Eveline Pinheiro de Aquino,Fabio Nauer,Gabriel do Nascimento Santos,Gilberto M. Amado Filho,Goia de Mattos Lyra,Gyslaine C.P. Borges,Iara Oliveira Costa,Ina de Souza Nogueira,Ivania Batista de Oliveira,Joel Campos De Paula,José Marcos de Castro Nunes,Jucicleide Cabral de Lima,Kleber R.S. Santos,Leandro Cabanez Ferreira,Lísia Mônica de Souza Gestinari,Luciana S. Cardoso,Marcia Abreu de Oliveira Figueiredo,Marcos H. Silva,Maria Beatriz Barbosa de Barros Barreto,Maria C.O. Henriques,Maria da G.G.S. Cunha,Maria E. Bandeira-Pedrosa,Maria F. Oliveira-Carvalho,Maria Teresa Menezes de Széchy,Maria Teresa de P. Azevedo,Mariana Cabral de Oliveira,Mariê M. Cabezudo,Marilene F. Santiago,Marli Bergesh,Mutue T. Fujii,Norma Catarina Bueno,Orlando Necchi,Priscila Barreto de Jesus,Ricardo G. Bahia,Samir Khader,Sandra Maria Alves-da-Silva,Silvia M. P. B. Guimarães,Sonia Maria Barreto Pereira,Taiara Aguiar Caires,Thamis Meurer,Valéria Cassano,Vera Regina Werner,Watson Arantes Gama,Weliton José da Silva +60 more
- 01 Jan 2015
TL;DR: An updated synthesis of cyanobacteria and algae information is presented for Brazil aiming to refine the data gathered to date and evaluate the progress of the biodiversity knowledge about these organisms in the country since the publication of the Catalogo de Plantas e Fungos do Brasil.