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Cognitive Vision 4th edition by Markus Vincze ISBN 3540927808 978-3540927808

  • SKU: BELL-2039360
Cognitive Vision 4th edition by Markus Vincze ISBN 3540927808 978-3540927808
$ 31.00 $ 45.00 (-31%)

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Cognitive Vision 4th edition by Markus Vincze ISBN 3540927808 978-3540927808 instant download after payment.

Publisher: Springer-Verlag Berlin Heidelberg
File Extension: PDF
File size: 6.55 MB
Pages: 149
Author: Erez Berkovich, Hillel Pratt, Moshe Gur (auth.), Barbara Caputo, Markus Vincze (eds.)
ISBN: 9783540927808, 3540927808
Language: English
Year: 2008
Edition: 1

Product desciption

Cognitive Vision 4th edition by Markus Vincze ISBN 3540927808 978-3540927808 by Erez Berkovich, Hillel Pratt, Moshe Gur (auth.), Barbara Caputo, Markus Vincze (eds.) 9783540927808, 3540927808 instant download after payment.

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ISBN 10: 3540927808
ISBN 13: 978-3540927808
Author: Markus Vincze

Weareverypleasedtopresenttheproceedingsofthe4thInternationalCognitive Vision Workshop,held as part of the 6th InternationalConference on Computer Vision Systems on Santorini,Greeceduring May12–15,2008.The aim of ICVW 2008 was to document the progress of the relatively young ?eld of cognitive computer vision, bringing together researchers working and interested in this ?eld and giving them a platform to discuss the results of the di?erent European cognitive vision projects as well as international projects in this area. Original research papers were solicited in all aspects of cognitive vision, targeting the following areas in particular: – Memory: The coupling between visual perception, tasks, knowledge and the visualsystemrequiresmemory.Issuesthatareofspecialimportanceforin- grating memory into vision systems include: how to manage representations with limited resources;modelfor attention;integrationofinformationacross representations and time. – Learning and Adaptation: A system whose goal is that of interacting with the real world must be capable of learning from experience and adapting to unexpected changes. Also, there is a need for integration of multiple - sual features to enable generation of stable hypotheses, and for methods for combination of cues in the presence of uncertainty. – Categorization: Research has in particular focused on recall of speci?c - ject instances, events and actions. Whereas recently some progress has been achieved in systems that allow limited recognition of object classes, events and scenes across visual appearance, new methods are needed to enable abstractions and e?ective categorization across variations in color, surface markings, geometry, temporal scenes, context and tasks.


Cognitive Vision 4th Table of contents:

  1. Introduction
  2. Methods
  3. Biologically Motivated Low-Level Features
  4. AdaBoost (Adaptive Boosting)
  5. Boosting with the Pseudo Quadratic Discriminant Classifier (PQDC)
  6. Results
  7. Discussion and Conclusion
  8. References
  9. A New Method for Synthetic Face Generation Using Spline Curves
  10. Introduction
  11. Face Sampling
  12. Synthetic Face Generation
  13. Experiments
  14. Face Similarity Tasks
  15. Gender Discrimination Task
  16. Conclusions
  17. References
  18. Epipolar Geometry for Humanoid Robotic Heads
  19. Introduction
  20. Background
  21. The Pinhole Camera Model
  22. Epipolar Geometry
  23. Epipolar Kinematics
  24. Epipolar Kinematics for a Humanoid
  25. Calibration Algorithm
  26. Updating the Essential Matrix
  27. Tests
  28. Platform
  29. Test Setup
  30. Results
  31. Conclusion
  32. References
  33. Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures
  34. Introduction
  35. State of the Art
  36. Human Posture Recognition by Video Cameras
  37. Activity Recognition
  38. The Proposed Activity Recognition Approach
  39. Overview
  40. Video Analysis
  41. Activity Recognition
  42. Results and Evaluation
  43. Experimental Site
  44. Experimental Results
  45. Conclusions and Future Works
  46. References
  47. Attention, Search and Maps
  48. Remembering Pictures of Real-World Images Using Eye Fixation Sequences in Imagery and in Recognition
  49. Introduction
  50. Experiment 1: Retrieving an Object
  51. Method
  52. Results
  53. Experiment 2: Retrieving a Picture of a Real-World Scene
  54. Method
  55. Results
  56. Discussion
  57. References
  58. Towards a Model of Information Seeking by Integrating Visual, Semantic and Memory Maps
  59. Introduction
  60. Information Seeking: 3 Components
  61. Visual Information
  62. Semantic Information
  63. Memory Mechanism
  64. Model
  65. Task
  66. Maps
  67. Comparison to Experimental Data
  68. Experiment
  69. Comparison
  70. Conclusion
  71. References
  72. An Entropy-Based Approach to the Hierarchical Acquisition of Perception-Action Capabilities
  73. Introduction
  74. Methodology
  75. Acquisition of Primitive Percept-Motor Capabilities via Information-Theoretic Saliency
  76. Experimental Results
  77. Conclusions
  78. References
  79. The Role of Implicit Context Information in Guiding Visual-Spatial Attention
  80. Introduction
  81. Contextual Cueing Experiments with Natural Stimuli
  82. Methods
  83. Participants
  84. Stimuli and Apparatus
  85. Procedure
  86. Data Analysis
  87. Experimental Results
  88. Contextual Cueing Effects with Natural Stimuli
  89. Contextual Cueing in Cognitive Technical Systems
  90. Conclusion
  91. References
  92. Scene Interpretation
  93. Probabilistic Pose Recovery Using Learned Hierarchical Object Models
  94. Introduction
  95. Hierarchical Model
  96. Inference
  97. Belief Propagation
  98. Nonparametric Representation
  99. Nonparametric Belief Propagation
  100. Importance Sampling
  101. Efficient Importance Sampling of Message Products
  102. Representational Constraints
  103. Implicit Messages
  104. Two-Level Importance Sampling
  105. Evaluation
  106. Pose Estimation
  107. Experiments
  108. Discussion
  109. Related Work
  110. Conclusion
  111. References
  112. Semantic Reasoning for Scene Interpretation
  113. Introduction
  114. Hierarchical Architecture
  115. Linear and Non-linear Filtering
  116. Symbolic Representation in 2D
  117. Symbolic Representation in 3D
  118. Semantic Graphs
  119. Applications
  120. Lane Finding Using Bayesian Reasoning
  121. Associating Actions to Co-planar Groups
  122. Discussion
  123. References
  124. Object Detection for a Humanoid Robot Using a Probabilistic Global Workspace
  125. Introduction
  126. Related Work
  127. Global Workspace Architecture
  128. Probabilistic Depth Map
  129. Part-of-Scene Label Map
  130. Behaviour System
  131. Five-Channel Prototype
  132. Experimental Results
  133. Scenarios
  134. Experiments
  135. Discussion
  136. Conclusions and Future Work
  137. References
  138. Author Index


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