Cantitate/Preț
Produs

Texture Feature Extraction Techniques for Image Recognition: SpringerBriefs in Applied Sciences and Technology

Autor Jyotismita Chaki, Nilanjan Dey
en Limba Engleză Paperback – 6 noi 2019
The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based. It also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and practitioners in different application areas.
Citește tot Restrânge

Din seria SpringerBriefs in Applied Sciences and Technology

Preț: 34485 lei

Preț vechi: 43107 lei
-20% Nou

Puncte Express: 517

Preț estimativ în valută:
65100 6855$ 5482£

Carte tipărită la comandă

Livrare economică 03-17 februarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9789811508523
ISBN-10: 9811508526
Pagini: 100
Ilustrații: XIV, 100 p. 75 illus., 12 illus. in color.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.17 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Seriile SpringerBriefs in Applied Sciences and Technology, SpringerBriefs in Computational Intelligence

Locul publicării:Singapore, Singapore

Cuprins

Introduction.- Statistical Texture Features.- Structural Texture Features.- Signal Processed Texture Features.- Model Based Texture Features.- Applications of Texture Features.

Notă biografică

Dr. Jyotismita Chaki is currently an Assistant Professor in School of Information Technology and Engineering at Vellore Institute of Technology, Vellore, India. She has done her PhD (Engg) from Jadavpur University, Kolkata, India. Her research interests include: Computer Vision and Image Processing, Machine learning, Pattern Recognition, Medical Imaging, Soft computing and Artificial Intelligence. She is an author of 2 Authored books and many research publications in reputed international journals and conference proceedings. She is an editor of 2 edited books. She has served as a reviewer of Applied Soft Computing (Elsevier), Biosystem Engineering (Elsevier), Pattern Recognition Letters (Elsevier), Journal of Visual Communication and Image Representation (Elsevier), Signal Image and Video Processing (Springer), IEEE ACCESS journals and also served as Program Committee member of many International Conferences.
Dr. Nilanjan Dey is an Assistant Professor in the Department of Information Technology at Techno International New Town (Formerly known as Techno India College of Technology), Kolkata, India. He is a visiting fellow of the University of Reading, UK. He is a Visiting Professor at Duy Tan University, Vietnam. He was an honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD. from Jadavpur University in 2015.                                                                                              He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, IGI Global. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing, Springer Nature, Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier, Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC. He has authored/edited more than 50 books with Springer, Elsevier, Wiley, CRC Press and published more than 300 peer-reviewed research papers.                                                                                  His main research interests include Medical Imaging, Machine learning, Computer Aided Diagnosis, Data Mining etc. He is the Indian Ambassador of the International Federation for Information Processing (IFIP) – Young ICT Group.

Caracteristici

Provides details of the need for image shape features in CBIR Highlights different texture extraction methods, like statistical, structural, model-based and signal-processed Addresses various applications of image analysis using texture feature