Cantitate/Preț
Produs

Big Visual Data Analysis: Scene Classification and Geometric Labeling: SpringerBriefs in Electrical and Computer Engineering

Autor Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
en Limba Engleză Paperback – 3 mar 2016
This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoorscene classification, and outdoor scene layout estimation. It is illustrated with numerous naturaland synthetic color images,and extensive statistical analysis is provided to help readers visualize big visualdata distribution and the associatedproblems. Although therehas been some research on big visual data analysis, little workhas been published on big image data distribution analysis using the modernstatistical approach described in thisbook. By presenting a complete methodology on big visual data analysis withthree illustrative scene comprehensionproblems, it provides ageneric framework that canbe applied to other big visual data analysis tasks.
Citește tot Restrânge

Din seria SpringerBriefs in Electrical and Computer Engineering

Preț: 36815 lei

Nou

Puncte Express: 552

Preț estimativ în valută:
7045 7410$ 5887£

Carte tipărită la comandă

Livrare economică 08-22 ianuarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9789811006296
ISBN-10: 9811006296
Pagini: 125
Ilustrații: X, 122 p. 94 illus., 12 illus. in color.
Dimensiuni: 155 x 235 x 7 mm
Greutate: 0.2 kg
Ediția:1st ed. 2016
Editura: Springer Nature Singapore
Colecția Springer
Seriile SpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Signal Processing

Locul publicării:Singapore, Singapore

Cuprins

Introduction.-Scene Understanding Datasets.- Indoor/Outdoor classification with MultipleExperts.- Outdoor Scene Classification Using Labeled Segments.- Global-AttributesAssisted Outdoor Scene Geometric Labeling.- Conclusion and Future Work.

Notă biografică

Chen Chen received his B.S. degree in Electrical Engineering from Beijing University of Posts and Telecommunications (BUPT) in 2010. He received his M.S. degree in Electrical Engineering from University of Southern California (USC) in 2012. At the same year, he joined the Media Communication Lab led by Professor Kuo in University of Southern California (USC), where he is pursuing her Ph.D degree in Electrical Engineering and serving as a research assistant. His research interests include image classification, image tagging and image/video processing.

Yu-Zhuo Ren received her B.S. degree in Hebei University of Technology (HUT), China, in 2011 and the M.S. degree in Electrical Engineering from University of Southern California (USC) in 2013. She is now working as a research assistant in the Media Communication Lab led by Professor Kuo. Her research interests include image understanding related problems, in the field of computer vision and machine learning.

C.-C. Jay Kuo Dr. C.-C. Jay Kuo received the B.S. degree from the National Taiwan University, Taipei, in 1980 and the M.S. and Ph.D. degrees from the Massachusetts Institute of Technology, Cambridge, in 1985 and 1987, respectively, all in Electrical Engineering. From October 1987 to December 1988, he was Computational and Applied Mathematics Research Assistant Professor in the Department of Mathematics at the University of California, Los Angeles. Since January 1989, he has been with the University of Southern California (USC).
He is presently Director of the Multimedia Communication Lab. and Professor of Electrical Engineering and Computer Science at the USC. His research interests are in the areas of multimedia data compression, communication and networking, multimedia content analysis and modeling, and information forensics and security. Dr. Kuo has guided 119 students to their Ph.D. degrees and supervised 23 postdoctoral research fellows. Currently, his research group at the USC has around 30Ph.D. students, which is one of the largest academic research groups in multimedia technologies. He is coauthor of about 220 journal papers, 850 conference papers and 12 books. He delivered over 550 invited lectures in conferences, research institutes, universities and companies.

Caracteristici

Presents a comprehensive big visual data analysis methodology that helps readers understand the topic quickly and fully Includes abundant insightful data analysis results and comparisons that can be used for other related computer-vision tasks such as scene analysis and image comprehension Provides source codes of data analysis applications(indoor/outdoor classification and vanishing point detection) so that readerscan test the algorithms and develop more advanced applications Writtenby leading experts in the field Includes supplementary material: sn.pub/extras