| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000cam a2200000Ka 4500 |
| 001 |
in00003510621 |
| 006 |
m o d |
| 007 |
cr bn||||||abp |
| 007 |
cr bn||||||ada |
| 008 |
100413s2002 enka ob 001 0 eng d |
| 005 |
20260420210244.2 |
| 019 |
|
|
|a 300792060
|
| 020 |
|
|
|z 1852336013 (alk. paper)
|
| 020 |
|
|
|z 9781852336011 (alk. paper)
|
| 020 |
|
|
|a 9781447106791 (electronic bk.)
|
| 020 |
|
|
|a 1447106792 (electronic bk.)
|
| 035 |
|
|
|a (OCoLC)606822323
|z (OCoLC)300792060
|
| 040 |
|
|
|a OCLCE
|b eng
|e pn
|c OCLCE
|d OCLCQ
|d OCLCO
|d OCLCQ
|d GW5XE
|d OCLCF
|d UtOrBLW
|
| 042 |
|
|
|a dlr
|
| 049 |
|
|
|a TXAM
|
| 050 |
|
4 |
|a TA1634
|b .M315 2002
|
| 072 |
|
7 |
|a TA
|2 lcco
|
| 082 |
0 |
4 |
|a 006.3/7
|2 21
|
| 084 |
|
|
|a 54.10
|2 bcl
|
| 100 |
1 |
|
|a MacCormick, John,
|d 1972-
|
| 245 |
1 |
0 |
|a Stochastic algorithms for visual tracking :
|b probabilistic modelling and stochastic algorithms for visual localisation and tracking /
|c John MacCormick.
|
| 264 |
|
1 |
|a London ;
|a New York :
|b Springer,
|c [2002]
|
| 264 |
|
4 |
|c ©2002
|
| 300 |
|
|
|a 1 online resource (ix, 174 pages) :
|b illustrations.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Distinguished dissertations
|
| 504 |
|
|
|a Includes bibliographical references (pages 155-164 and index.
|
| 538 |
|
|
|a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
|u http://purl.oclc.org/DLF/benchrepro0212
|5 MiAaHDL
|
| 583 |
1 |
|
|a digitized
|c 2010
|h HathiTrust Digital Library
|l committed to preserve
|2 pda
|5 MiAaHDL
|
| 588 |
|
|
|a Description based on print version record.
|
| 505 |
0 |
|
|a Introduction and Background -- The Condensation Algorithm -- Contour Likelihoods -- Object Localisation and Tracking with Contour Likelihoods -- Modelling Occlusions using the Markov Likelihood -- A Probabilistic Exclusion Principle for Multiple Objects -- Partitioned Sampling -- Conclusion -- Appendices.
|
| 520 |
|
|
|a A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory.
|
| 500 |
|
|
|a Electronic resource.
|
| 650 |
|
0 |
|a Computer vision.
|
| 650 |
|
0 |
|a Stochastic analysis.
|
| 650 |
1 |
7 |
|a Stochastische analyse.
|2 gtt
|
| 650 |
1 |
7 |
|a Algoritmen.
|2 gtt
|
| 650 |
1 |
7 |
|a Patroonherkenning.
|2 gtt
|
| 650 |
|
7 |
|a Computer vision.
|2 fast
|0 (OCoLC)fst00872687
|
| 650 |
|
7 |
|a Stochastic analysis.
|2 fast
|0 (OCoLC)fst01133499
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 776 |
1 |
8 |
|i Print version:
|a MacCormick, John, 1972-
|t Stochastic algorithms for visual tracking.
|d London ; New York : Springer, ©2002
|w (DLC) 2002021734
|w (OCoLC)49421689
|
| 830 |
|
0 |
|a CPHC/BCS distinguished dissertations.
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://link.springer.com/10.1007/978-1-4471-0679-1
|z Connect to the full text of this electronic book
|t 0
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
|
|
|a MARS
|
| 999 |
f |
f |
|s 3e3ef1ad-72c1-3ef8-92ba-2621a0ac3b2b
|i 55d91499-2fdd-378f-be45-909ceab9a06b
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|e TA1634 .M315 2002
|h Library of Congress classification
|
| 998 |
f |
f |
|a TA1634 .M315 2002
|t 0
|l Available Online
|