Sparse Representations and Compressive Sensing for Imaging and Vision (Record no. 10846)

000 -LEADER
fixed length control field 02303nam a22004095i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140310143339.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130220s2013 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781461463818
978-1-4614-6381-8
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.382
Edition number 23
264 #1 -
-- New York, NY :
-- Springer New York :
-- Imprint: Springer,
-- 2013.
912 ## -
-- ZDB-2-ENG
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Patel, Vishal M.
Relator term author.
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title Sparse Representations and Compressive Sensing for Imaging and Vision
Medium [electronic resource] /
Statement of responsibility, etc by Vishal M. Patel, Rama Chellappa.
300 ## - PHYSICAL DESCRIPTION
Extent X, 102 p. 41 illus.
Other physical details online resource.
440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE
Title SpringerBriefs in Electrical and Computer Engineering,
International Standard Serial Number 2191-8112
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction -- Compressive Sensing -- Compressive Acquisition -- Compressive Sensing for Vision -- Sparse Representation-based Object Recognition -- Dictionary Learning -- Concluding Remarks.
520 ## - SUMMARY, ETC.
Summary, etc Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal.  These measurements are usually much smaller than the number of samples that define the signal.  From these small numbers of measurements, the signal is then reconstructed by non-linear procedure.  Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways.  In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Computer vision.
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Signal, Image and Speech Processing.
Topical term or geographic name as entry element Image Processing and Computer Vision.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Chellappa, Rama.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9781461463801
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4614-6381-8
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type E-Book
Copies
Price effective from Permanent location Date last seen Not for loan Date acquired Source of classification or shelving scheme Koha item type Damaged status Lost status Withdrawn status Current location Full call number
2014-03-31AUM Main Library2014-03-31 2014-03-31 E-Book   AUM Main Library621.382