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.
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Jason J. Corso
Associate Professor
Computer Science and Engineering
SUNY at Buffalo
Email: jcorso@buffalo.edu
Office: Davis Hall 332
Phone: 716-645-4754
Bio: [txt]
Vita: [pdf]
Hours: R 1200-1400
Cal: Availability
Job Openings
Publication Tag Cloud
active clustering  activity recognition  artificial intelligence  augmented reality  belief propagation  bioinformatics  biomarkers  biometrics  braintumor  computational finance  computer forensics  computer graphics  computer vision  computer-aided diagnosis  cosegmentation  data mining  deformable  dictionary transfer  document imaging  domain adaptation  dynamic linear models  endoscopy  evaluation  event recognition  facade detection  face detection  face recognition  feature extraction  fusion  gesture recognition  gpu  grammar  graph cuts  graph-based  graphical models  haptics  hierarchical  higher-order  human pose estimation  human-computer interaction  image denoising  image processing  image retrieval  image understanding  localization  lung imaging  machine learning  mapping  max-margin  medical imaging  metric learning  mobile robotics  mosaicking  motion estimation  mrf  multimedia  natural language  navigation  neuroimaging  object detection  ontology  probabilistic ontology  protein structure prediction  random forest  reconstruction  segmentation  semantic segmentation  slam  spectral clustering  spine imaging  stereo  streaming  supervoxel  surgical robotics  tomographic reconstruction  tracking  video summarization  video understanding  volume rendering  voxel maps 

Dr. Jason J. Corso is currently [was] an associate professor of Computer Science and Engineering Department at SUNY at Buffalo.He received his Ph.D. in Computer Science at The Johns Hopkins University in 2005. He received the M.S.E Degree from The Johns Hopkins University in 2002 and the B.S. Degree with honors from Loyola College In Maryland in 2000, both in Computer Science. He spent two years as a post-doctoral research fellow at the University of California, Los Angeles. He is a recipient of the NSF CAREER award, ARO Young Investigator award, on the DARPA CSSG, UB Young Investigator award and a UB Innovator award.
His main research thrust is high-level computer vision and data science, primarily focusing on problems in video understanding such as video segmentation, activity recognition, and video-to-text. From biomedicine to recreational video, imaging data is ubiquitous. Yet, imaging scientists and intelligence analysts are without an adequate language and set of tools to fully tap the information-rich image and video. He works to provide such a language; specifically, he studies the coupled problems of segmentation and recognition from a Bayesian perspective emphasizing the role of statistical models in efficient visual inference. His long-term goal is a comprehensive and robust methodology of automatically mining, quantifying, and generalizing information in large sets of projective and volumetric images and video. The following four questions drive his current research inquiries:
  1. How to use principled hierarchical structures to model complex real-world phenomena?
  2. How to handle the massive data glut for machine learning while appropriately incorporating the user and yet requiring little labeling?
  3. How to incorporate prior high-level knowledge (semantics, ontology, context, etc.) during both learning and inference?
  4. What is the relationship between vision and language and action and reasoning?
More broadly, his research interests are in the fields of computer vision, robot perception, data sciences and medical imaging. More information on these topics can be found in the research pages.
Selected Publications     [complete list here]
[1] C. Xiong, S. McCloskey, and J. J. Corso. Latent domains for visual domain adaptation. In Proceedings of AAAI Conference on Artificial Intelligence, 2014. [ bib ]
[2] S. Kumar, V. Dhiman, and J. J. Corso. Learning compositional sparse models of bimodal percepts. In Proceedings of AAAI Conference on Artificial Intelligence, 2014. [ bib | code | .pdf ]
[3] W. Chen, C. Xiong, R. Xu, and J. J. Corso. Actionness ranking with lattice conditional ordinal random fields. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014. [ bib ]
[4] V. Dhiman, A. Kundu, F. Dellaert, and J. J. Corso. Modern MAP inference methods for accurate and faster occupancy grid mapping on higher order factor graphs. In Proceedings of International Conference on Robotics and Automation, 2014. [ bib | code | .pdf ]
[5] C. Xiong, W. Chen, G. Chen, D. Johnson, and J. J. Corso. Adaptive quantization: An information-based approach to learning binary codes. In Proceedings of SIAM International Conference on Data Mining, 2014. [ bib | code | .pdf ]
[6] C. Xu, R. F. Doell, S. J. Hanson, C. Hanson, and J. J Corso. A study of actor and action semantic retention in video supervoxel segmentation. International Journal of Semantic Computing, 2014. (In Press) Selected as a Best Paper from ICSC; an earlier version appeared as arXiv:1311.3318. [ bib | .pdf ]
[7] C. Xu, S. Whitt, and J. J. Corso. Flattening supervoxel hierarchies by the uniform entropy slice. In Proceedings of the IEEE International Conference on Computer Vision, 2013. [ bib | poster | project | video | .pdf ]
[8] V. Dhiman, J. Ryde, and J. J. Corso. Mutual localization: Two camera relative 6-dof pose estimation from reciprocal fiducial observation. In Proceedings of International Conference on Intelligent Robots and Systems, 2013. [ bib | slides | code | .pdf ]
[9] L. Zhao, W. Wu, and J. J. Corso. Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories. In Proceedings of Medical Image Computing and Computer Aided Intervention, 2013. [ bib | .pdf ]
[10] P. Das, C. Xu, R. F. Doell, and J. J. Corso. A thousand frames in just a few words: Lingual description of videos through latent topics and sparse object stitching. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013. [ bib | poster | data | .pdf ]
[11] P. Das, R. K. Srihari, and J. J. Corso. Translating related words to videos and back through latent topics. In Proceedings of Sixth ACM International Conference on Web Search and Data Mining, 2013. [ bib | .pdf ]
[12] C. Xu, C. Xiong, and J. J. Corso. Streaming hierarchical video segmentation. In Proceedings of European Conference on Computer Vision, 2012. [ bib | code | project | .pdf ]
[13] C. Xiong, D. Johnson, R. Xu, and J. J. Corso. Random forests for metric learning with implicit pairwise position dependence. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012. [ bib | slides | code | .pdf ]
[14] S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012. [ bib | code | project | .pdf ]
[15] J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, and A. Yuille. Efficient Multilevel Brain Tumor Segmentation with Integrated Bayesian Model Classification. IEEE Transactions on Medical Imaging, 27(5):629-640, 2008. [ bib | .pdf ]
Code and Data Downloads
Video2Text.net: A website and web-service for automatic conversion of videos to natural language sentences based on the video content. This website showcases our work in the vision+language domain.

YouCook data set: 88 challenging videos of various cooking (third-person viewpoint, different backgrounds, dynamic camera and person movement) with natural language annotations (about 8 per video) and object and action annotations. Includes a benchmark ROUGE scoring evaluation. The data set was published with our CVPR 2013 paper.

Hierarchy Agreement Index: implementation of our AAAI LBP 2013 cross-hierarchy evaluation tool for general use.

Random Forest Distance -- tree-structured metric learning that implicitly adapts the metric over the sample space based on our KDD 2012 paper. (Code updated 2/28/14)

Action Bank full code and processed data sets  [direct link to code]

LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing. Implements a suite of supervoxel video segmentation methods as well as a quantitative set of 2D and 3D metrics for good supervoxels.

Graph-Shifts Code (Java) and example data.

Video label propagation code and benchmark data set.

UB/College Park stereo building facade dataset. [more information].

Current Grants
ARO YIP (PI): GBS: Guidance By Semantics-Using High-Level Visual Inference to Improve Vision-based Mobile Robot Localization
DARPA MINDSEYE (PI): ISTARE: Intelligent Spatio-Temporal Activity Reasoning Engine
DARPA CSSG-III (PI): Transferring ACE to the Analyst
FHWA (CUBRC Sub) (PI): Computer Vision and Mobile Robot Technologies for Advanced Emergency Response
IARPA ALADDIN (Kitware Sub) (PI): Ontology, Event Agents and Event Recounting for ALADDIN
NIH (HRI Sub) (PI): Objective Imaging-Based Assessment of Smoking Behavior from Used Filters
NSF CAREER (PI): CAREER: Generalized Image Understanding with Probabilistic Ontologies and Dynamic Adaptive Graph Hierarchies

Recently Expired Grants

DARPA CSSG-II (PI): ACE -- Active Clustering for Exploitation and Defense Forensics
Naval PS (PI): Comprehensive Object Detection Library for Large-Scale Image Analytics
ARO DURIP (PI): Two-Rank Mobile Robot Fleet for Swarm Surveillance, WarFighter Assistance, and other Army-related Research and Research-Related Education
CIA (PI): Semantic Video Summarization With Ontology-Driven Probabilistic Inference on Massive Multimedia Collections
Professional Service
Associate Editor: Computer Methods and Programs in Biomedicine 2009-Currently
Area Chair: CVPR 2012  CVPR 2013,   WACV 2014,   ECCV 2014
Program Committee/Reviewer:
     CVPR 2003  2006  2007  2009  2010  2011 
     ECCV  2006  2010 
     EMMCVPR  2007  2009  2011  2013 
     ICCV  2007  2009  2013
     ICRA  2005  2009  2011  2012  2013
     IROS  2007  2012;  2013 
     MICCAI  2003,   2006  2007  2008  2009  2012  2013 
Teaching
CSE 555: Introduction to Pattern Recognition -- Spring 2009--2013
CSE 672: Bayesian Vision -- Spring 2008, Fall 2010, Fall 2012
CSE 642: Techniques in AI: Vision for HCI -- Fall 2009
Seminar in Image Semantics -- Fall 2009
Seminar in Pattern Theory -- Fall 2008
Seminar: Topics in Medical Image Segmentation -- Fall 2007
Seminar: Readings in Joint Visual, Lingual and Physical Models and Inference Algorithms -- Spring 2014
Miscellaneous
Comments on using an iPad as a professor.
Home Webpage
Notebook
Camera Calibration Lecture Notes from Computer Vision
 
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last updated: Tue Jul 29 10:11:43 2014; copyright jcorso
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