Computer Vision and Pattern Recognition in Environmental Informatics

Computer Vision and Pattern Recognition in Environmental Informatics

Jun Zhou, Xiao Bai, Terry Caelli
Indexed In: SCOPUS
Release Date: October, 2015|Copyright: © 2016 |Pages: 407
DOI: 10.4018/978-1-4666-9435-4
ISBN13: 9781466694354|ISBN10: 1466694351|EISBN13: 9781466694361
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Description & Coverage
Description:

Computer Vision and Pattern Recognition (CVPR) together play an important role in the processes involved in environmental informatics due to their pervasive, non-destructive, effective, and efficient natures. As a result, CVPR has made significant contributions to the field of environmental informatics by enabling multi-modal data fusion and feature extraction, supporting fast and reliable object detection and classification, and mining the intrinsic relationship between different aspects of environmental data.

Computer Vision and Pattern Recognition in Environmental Informatics describes a number of methods and tools for image interpretation and analysis, which enables observation, modelling, and understanding of environmental targets. In addition to case studies on monitoring and modeling plant, soil, insect, and aquatic animals, this publication includes discussions on innovative new ideas related to environmental monitoring, automatic fish segmentation and recognition, real-time motion tracking systems, sparse coding and decision fusion, and cell phone image-based classification and provides useful references for professionals, researchers, engineers, and students with various backgrounds within a multitude of communities.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • 3D Plant Modeling
  • Computer Vision
  • Environmental Analysis
  • Image Processing
  • Machine Learning
  • Pattern Recognition
Reviews & Statements

Computer scientists and engineers from a number of fields describe how computer vision and pattern recognition provide tools for interpreting and analyzing data in environmental informatics. Focusing in turn on detecting and monitoring aquatic animals, recognizing and modeling insects, and analyzing plants and soil, they consider such topics as hierarchical decomposition for unusual fish trajectory detection, automatic fish segmentation and recognition for trawl-based cameras, insect recognition using sparse code and decision fusion, the three-dimensional modeling for environmental informatics parametric manifold of an object under different viewing directions, and a large margin learning method for matching images of natural objects with different dimensions.

– ProtoView Reviews
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Editor/Author Biographies
Jun Zhou received the B.S. degree in computer science and the B.E. degree in international business from Nanjing University of Science and Technology, Nanjing, China, in 1996 and 1998, respectively, the M.S. degree in computer science from Concordia University, Montreal, Canada, in 2002, and the Ph.D. degree from the University of Alberta, Edmonton, Canada, in 2006. He joined the School of Information of Communication Technology at Grif?th University, Nathan, Australia June 2012, first as a Lecturer, and then promoted to senior lecturer. Previously, he had been a Research Fellow in the Research School of Computer Science in the Australian National University, Canberra, Australia, and a Researcher in the Canberra Research Laboratory, NICTA, Australia. His research interests include pattern recognition, computer vision, and machine learning with human in the loop, with their applications to spectral imaging and environmental informatics.
Xiao Bai received the B.Eng. degree in computer science from Beihang University of China, Beijing, China, in 2001, and the Ph.D. degree from the University of York, York, U.K., in 2006. He was a Research Officer (Fellow, Scientist) in the Computer Science Department, University of Bath, until 2008. He is currently an Associate Professor in the School of Computer Science and Engineering, Beihang University. He has published more than forty papers in journals and refereed conferences. His current research interests include pattern recognition, image processing and remote sensing image analysis. He has been awarded New Century Excellent Talents in University in 2012.
Terry Caelli is a Professorial Fellow at the University of Melbourne. Previous to this he has held a number of senior positions with National ICT Australia’s (NICTA) including Laboratory Director and Director of NICTA Health Program. His interests lie in Signal Processing, Human and Machine Vision, Pattern Recognition, Machine Learning and their applications in Health, Environment and Defense. He has a Ph.D. in Human and Machine Vision from the University of Newcastle, Australia. He is a Fellow of the International Association for Pattern Recognition (FIAPR) and a Fellow of the Institute for Electronic and Electrical Engineers (FIEEE). He is also a Convocation Medalist from the University of Newcastle. He has spent 15 years in North American universities and research institutes (Bell Laboratories and a NASA funded Centre for Mapping at Ohio State University), has been a DFG Professor in Germany, and Killam Professor of Science at the University of Alberta.
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