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Computer Vision and Action Recognition : A Guide for Image Processing and Computer Vision Community for Action Understanding / by Md. Atiqur Rahman Ahad
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Atlantis Ambient and Pervasive Intelligence ; 5 | SpringerLink BücherPublisher: Paris : Atlantis Press, 2011Description: Online-Ressource (XXI, 211p. 48 illus., 38 illus. in color, digital)ISBN:- 9789491216206
- 006.6
- 300 500
- T385 TA1637-1638 TK7882.P3
- T385
- TA1637-1638
- TK7882.P3
Contents:
Summary: Md. Atiqur Rahman AhadSummary: Human action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed and evaluated subsequently by many researchers. Among those methods, some methods get significant attePPN: PPN: 1651227365Package identifier: Produktsigel: ZDB-2-SCS
Computer Vision and Action Recognition; Preface; Foreword; Acknowledgments; Contents; List of Figures; List of Tables; 1 Introduction; 1.1 Introduction; 1.2 What is Action?; 1.3 Action Recognition in Computer Vision; 1.4 Application Realms of Action Recognition; 1.5 Categorization; 1.6 Think Ahead!; 2 Low-level Image Processing for ActionRepresentations; 2.1 Low-level Image Processing for Action Representations; 2.2 Pre-processing Steps; 2.3 Segmentation and Extraction; 2.3.1 Feature Detection from an Image; 2.3.2 Edge Detection; 2.3.3 Corner Points; 2.3.4 Blob Detectors
2.3.5 Feature Descriptors2.3.6 Segmentation; 2.3.6.1 Background Subtraction; 2.3.6.2 Frame Subtraction; 2.3.6.3 DenseMotion Estimation; 2.4 Local Binary Pattern; 2.5 Structure fromMotion (SFM); 2.5.1 Constraints of FM; 2.5.2 Improvements of FM; 2.6 Other Issues; 2.6.1 Intensity Normalization; 2.6.2 ImageMatching and Correspondence Problem; 2.6.2.1 Model-based ImageMatching; 2.6.2.2 Feature-based ImageMatching; 2.6.3 Camera Calibration; 2.7 Think Ahead!; 3 Action Representation Approaches; 3.1 Action Representation Approaches; 3.2 Classification of Various Dimensions of Representations
3.2.1 Bag-of-Features (BoF) or Bag-of-Visual-Words (BoVW)3.2.1.1 Constraints of BoF; 3.2.2 ProductManifold Approaches; 3.3 Action Recognition Approaches; 3.3.1 Interest-point-based Approaches; 3.3.2 HiddenMarkovModel-based Approaches; 3.3.2.1 Star Skeleton; 3.3.2.2 Non-parametric HMM Approach; 3.3.2.3 Factorial Hidden Markov Models; 3.3.2.4 DynamicallyMulti-Linked HMM; 3.3.2.5 CRF and MEMM-based; 3.3.2.6 Hierarchical HMM; 3.3.2.7 Others; 3.3.3 Eigenspace-based Approach; 3.3.4 Approaches to Manage Occlusion; 3.3.5 Other Approaches; 3.3.5.1 Actom SequenceModel
3.3.5.2 Spatio-temporal Silhouettes3.3.5.3 Robustness in Camera Movement; 3.4 View-invariantMethods; 3.5 Gesture Recognition and Analysis; 3.6 Action Segmentation and Other Areas; 3.7 Affective Computing and Expression Analysis; 3.7.1 Games with Emotional Involvement; 3.7.2 Interactive Arts; 3.7.3 Anatomically-based Talking Head; 3.8 Action Segmentation; 3.8.1 Gestures; 3.8.2 BasicMotion; 3.8.3 Fundamental Gesture; 3.8.4 Motion Alphabet; 3.8.5 AtomicMovement; 3.8.6 Direction-based BasicMotion; 3.8.7 Distinct Behaviors; 3.8.8 Motion Patterns based on Symbols
3.8.9 BasicMovement Transition Graph3.9 Gait Analysis; 3.10 Action Recognition in Low-resolution; 3.10.1 Application Areas; 3.10.2 RelatedWorks on Low-Resolution Video Processing; 3.11 Discussion; 3.11.1 Salient Region and Its Associated Salient Region Construction; 3.11.2 Biologically-inspired Visual Representations; 3.12 Conclusion; 3.13 Think Ahead!; 4 MHI - A Global-based Generic Approach; 4.1 Motion History Image (MHI); 4.2 Why MHI?; 4.3 Various Aspects of the MHI-A Tutorial; 4.3.1 Formation of an MHI Image; 4.3.2 Motion Energy Image (MEI); 4.3.3 Parameter-t; 4.3.4 Parameter-d
4.3.5 Temporal Duration vs. Decay Parameter
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