Dynamic Textures

Research

Dynamic Textures

One of the most important elements of modern Computer Vision is the concept of image texture, or simply texture. Depending on the task at hand (e.g. image-ba...

Resources

Dynamic Textures Dataset

Collection of video datasets for dynamic texture research, including UCLA-50 and others.

Posts

Dynamic Texture Editing

5 minute read

The operation that by processing video data allows producing new video data in Computer Graphics is known as Video-Based Rendering (VBR). Developing new VBR ...

Dynamic Texture Segmentation

2 minute read

Segmenting the image plane of video sequences is often one of the first steps towards the analysis of video. A lot of effort has been spent on developing ima...

Dynamic Texture Recognition

2 minute read

Recognition of objects based on their images is one of the central problems in modern Computer Vision. Objects can be characterized by their geometric, photo...

Publications

AVSS
A model change detection approach to dynamic scene modeling Kim, S. J., Doretto, G., Rittscher, J., Tu, P., Krahnstoever, N., and Pollefeys, M. In IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. Oral abstract bibTeX pdf
In this work we propose a dynamic scene model to provide information about the presence of salient motion in the scene, and that could be used for focusing the attention of a pan/tilt/zoom camera, or for background modeling purposes. Rather than proposing a set of saliency detectors, we define what we mean by salient motion, and propose a precise model for it. Detecting salient motion becomes equivalent to detecting a model change. We derive optimal online procedures to solve this problem, which enable a very fast implementation. Promising results show that our model can effectively detect salient motion even in severely cluttered scenes, and while a camera is panning and tilting.
```
@inproceedings{kimDRTKP09avss,
  abbr = {AVSS},
  author = {Kim, S. J. and Doretto, G. and Rittscher, J. and Tu, P. and Krahnstoever, N. and Pollefeys, M.},
  title = {A model change detection approach to dynamic scene modeling},
  booktitle = {IEEE International Conference on Advanced Video and Signal Based
  	Surveillance},
  year = {2009},
  pages = {490--495},
  month = sep,
  bib2html_pubtype = {Conferences},
  bib2html_rescat = {Video Analysis, Dynamic Textures, Visual Motion Detection},
  file = {kimDRTKP09avss.pdf:doretto\\conference\\kimDRTKP09avss.pdf:PDF},
  keywords = {dynamic scene modeling, video analysis, PTZ camera, focus-ofattention, background
  	modeling, sequential generalized likelihood ratio, model change detection,
  	linear dynamical systems, dynamic textures},
  owner = {doretto},
  timestamp = {2009.09.28},
  wwwnote = {Oral}
}
```
A saliency model for active camera control Kim, S. J., Doretto, G., Rittscher, J., Tu, P., and Pollefeys, M. Technical Report 2005GRC591, GE Global Research, 2005. Visualization and Computer Vision Laboratory abstract bibTeX
The goal of this work is to provide information for the automatic control of an active camera. One way to achieve this is to run a set of saliency detectors in the scene which is tuned to a specific class of objects. As opposed to this, we propose a formal definition for what we mean for salient motion by means of the concept of stationarity of stochastic processes. We propose a hierarchy of generative models for salient and nuisance motion, along with recursive learning procedures for on-line processing. By doing so, we turn the problem of detecting salient motion into the problem of model change detection, which we solve optimally online using the sequential generalized likelihood ratio test. Our system is designed for realtime applications, and is able to detect salient motion even in severely cluttered scenes.
```
@techreport{kimDRTP05tr,
  author = {Kim, S. J. and Doretto, G. and Rittscher, J. and Tu, P. and Pollefeys, M.},
  title = {A saliency model for active camera control},
  institution = {GE Global Research},
  year = {2005},
  number = {2005GRC591},
  address = {Niskayuna, NY, USA},
  month = nov,
  note = {Visualization and Computer Vision Laboratory},
  bib2html_pubtype = {Tech Reports},
  bib2html_rescat = {Video Analysis, Dynamic Textures, Visual Motion Detection},
  file = {kimDRTP05tr.pdf:doretto\\report\\kimDRTP05tr.pdf:PDF;kimDRTP05tr.pdf:doretto\\report\\kimDRTP05tr.pdf:PDF},
  keywords = {salient activity detection, linear dynamic systems, motion detection,
  	pan tilt zoom, camera, foreground/background segmentation, activity/novelty
  	detection, dynamic textures, sequential likelihood ratio},
  owner = {doretto},
  timestamp = {2006.11.29}
}
```