Machine Learning for Vision-Based Motion Analysis Theory and Techniques /
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and...
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
London :
Springer London : Imprint: Springer,
2011.
|
| Σειρά: | Advances in Pattern Recognition,
|
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Part I: Manifold Learning and Clustering/Segmentation
- Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis
- Riemannian Manifold Clustering and Dimensionality Reduction for Vision-based Analysis
- Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models
- Part II: Tracking
- Mixed-state Markov Models in Image Motion Analysis
- Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate
- Discriminative Multiple Target Tracking
- A Framework of Wire Tracking in Image Guided Interventions
- Part III: Motion Analysis and Behavior Modeling
- An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos
- Video-based Human Motion Estimation by Part-whole Gait Manifold Learning
- Spatio-temporal Motion Pattern Models of Extremely Crowded Scenes
- Learning Behavioral Patterns of Time Series for Video-surveillance
- Part IV: Gesture and Action Recognition
- Recognition of Spatiotemporal Gestures in Sign Language using Gesture Threshold HMMs
- Learning Transferable Distance Functions for Human Action Recognition.
