Prof. Corso moved to the Electrical Engineering and Computer Science department at the University of Michigan in the 8/2014. He continues his work and research group in high-level computer vision at the intersection of perception, semantics/language, and robotics. Unless you are looking for something specific, historically, here, you probably would rather go to his new page.
Jason J. Corso
Research Pages
Snippets by Topic
* Active Clustering
* Activity Recognition
* Medical Imaging
* Metric Learning
* Semantic Segmentation
* Video Segmentation
* Video Understanding
Selected Project Pages
* Action Bank
* LIBSVX: Supervoxel Library and Evaluation
* Brain Tumor Segmentation
* CAREER: Generalized Image Understanding
* Summer of Code 2010: The Visual Noun
* ACE: Active Clustering
* ISTARE: Intelligent Spatiotemporal Activity Reasoning Engine
* GBS: Guidance by Semantics
* Semantic Video Summarization
Data Sets
* YouCook
* Chen
* UB/College Park Building Facades
Other Information
* Code/Data Downloads
* List of Grants
Multi-Class Label Propagation in Videos
The effective propagation of pixel labels through the spatial and temporal domains is vital to many computer vision and multimedia problems, yet little attention has been paid to the temporal/video domain propagation in the past. We have begun to explore this problem in the context of general real-world "videos in the wild" and surveillance videos. Our current efforts primarily focus on mixing motion information with appearance information Previous video label propagation algorithms largely avoided the use of dense optical flow estimation due to their computational costs and inaccuracies, and relied heavily on complex (and slower) appearance models.

Label Propagation Benchmark Dataset
We used a subset of the videos from as the basis of our benchmark dataset for label propagation. Existing datasets either restricted the study to two classes or were taken in restricted settings, such as from the dash of a moving vehicle. Our new data set has general motion and presents stratified levels of complexity. We continue to add to the labels and will release additional videos in the future. For more information, see Albert Chen's page.

Download the full data set.

Code from our WNYIPW paper is here with the config file. Or you can get the dataset above and the code is in the package.
If you use the dataset or the code, the associated cite is below.
[1] A. Y. C. Chen and J. J. Corso. Propagating multi-class pixel labels throughout video frames. In Proceedings of Western New York Image Processing Workshop, 2010. [ bib | .pdf ]
This work is partially support by NSF CAREER IIS-0845282 [project page].

last updated: Tue Jul 29 10:11:57 2014; copyright jcorso