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  • Produktbild: Stochastic Algorithms for Visual Tracking
  • Produktbild: Stochastic Algorithms for Visual Tracking

Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.09.2011

Abbildungen

IX, 174 p.

Verlag

Springer London

Seitenzahl

174

Maße (L/B/H)

23,5/15,5/1,1 cm

Gewicht

295 g

Auflage

Softcover reprint of the original 1st edition 2002

Sprache

Englisch

ISBN

978-1-4471-1176-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.09.2011

Abbildungen

IX, 174 p.

Verlag

Springer London

Seitenzahl

174

Maße (L/B/H)

23,5/15,5/1,1 cm

Gewicht

295 g

Auflage

Softcover reprint of the original 1st edition 2002

Sprache

Englisch

ISBN

978-1-4471-1176-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Stochastic Algorithms for Visual Tracking
  • Produktbild: Stochastic Algorithms for Visual Tracking
  • 1 Introduction and background.- 1.1 Overview.- 1.2 Active contours for visual tracking.- 2 The Condensation algorithm.- 2.1 The basic idea.- 2.2 Formal definitions.- 2.3 Operations on particle sets.- 2.4 The Condensation theorem.- 2.5 The relation to factored sampling, or “where did the proof go?”.- 2.6 “Good” particle sets and the effective sample size.- 2.7 A brief history of Condensation.- 2.8 Some alternatives to Condensation.- 3 Contour likelihoods.- 3.1 A generative model for image features.- 3.2 Background models and the selection of measurement lines.- 3.3 A continuous analogue of the contour likelihood ratio.- 4 Object localisation and tracking with contour likelihoods.- 4.1 A brief survey of object localisation.- 4.2 Object localisation by factored sampling.- 4.3 Estimating the number of targets.- 4.4 Learning the prior.- 4.5 Random sampling: some traps for the unwary.- 4.6 Tracker initialisation by factored sampling.- 4.7 Tracking using Condensation and the contour likelihoods.- 5 Modelling occlusions using the Markov likelihood.- 5.1 Detecting occluded objects.- 5.2 The problem with the independence assumption.- 5.3 The Markov generative model.- 5.4 Prior for occlusions.- 5.5 Realistic assessment of multiple targets.- 5.6 Improved discrimination with a single target.- 5.7 Faster convergence using importance sampling.- 5.8 Random samples using MelvIe.- 5.9 Calculating the partition functions.- 5.10 Further remarks.- 6 A probabilistic exclusion principle for multiple objects.- 6.1 Introduction.- 6.2 A generative model with an exclusion principle.- 6.3 Tracking multiple wire-frame objects.- 6.4 Tracking multiple opaque objects.- 7 Partitioned sampling.- 7.1 The need for partitioned sampling.- 7.2 Weighted resampling.- 7.3 Basic partitioned sampling.-7.4 Branched partitioned sampling.- 7.5 Performance of partitioned sampling.- 7.6 Partitioned sampling for articulated objects.- 8 Conelusion?.- Appendix A.- A.1 Measures and Metrics on the configuration space.- A.2 Proof of the interior-exterior likelihood.- A.3 Del Moral’s resampling lemma and its consequences.- Appendix B.- B.1 Summary Of Notation.