Stephen Aylward, Ph.D.

Senior Director of Strategic Initiatives

Stephen Aylward, Ph.D., is the senior director of strategic initiatives and founder of Kitware’s North Carolina office. He helps drive multiple research and open source software development projects at Kitware. Over the past 25+ years, Stephen has conducted medical image analysis research covering nearly every aspect of health care, including screening, diagnosis, treatment planning, guidance, and outcome assessment for mammography, neurosurgery, partial liver transplantation, retinopathy of prematurity, stroke, traumatic brain injury, pre-clinical cancer studies, and others. He has also been instrumental in the creation of the Insight Toolkit (ITK), major updates to 3D Slicer, and the development of new technologies and the VTK.js library for web-based scientific visualization.

Stephen’s current research is focused on the development of point-of-care ultrasound (POCUS) applications, the characterization of vascular morphology for disease assessment, and the advancement of multiple open source platforms such as MONAI, the open source deep learning library for medical imaging.

Stephen’s POCUS projects aim to improve the tools used by first-responders and far-forward medics to positively impact patient morbidity and mortality. Injuries such as abdominal and brain traumas require immediate, yet patient-specific management. These projects are providing novel methods for identifying internal bleeding and increases in intracranial pressure. Stephen has been working closely with both academic and commercial organizations to develop systems for applying those methods without having to be an ultrasound expert. 

Much of Stephen’s early career focused on extracting models of vasculature from MRA images for surgical planning. These models were registered with intraoperative ultrasound for surgical guidance. They were also studied over time to monitor the effects of surgery and radiation therapy and quantify vessel tortuosity as a measure of tumor malignancy and response to therapy. This work continues to shape much of his ongoing research. In particular, by coupling his early vessel tortuosity research with innovative tumor micro-environment imaging systems in academia (Paul Dayton, BME, UNC) and industry (SonoVol), we are fostering new discoveries in angiogenesis, tumor growth, and biomarkers for the early detection of micro-tumors. 

In addition to his projects, Stephen is also involved in promoting cross-team initiatives within Kitware. These initiatives combine and deploy our technologies to enable our communities to more effectively solve larger and more complex problems. For example, integrating AI into Kitware’s web-based visualization applications, pairing our tools with innovative hardware (e.g. holographic displays), and distributing Kitware’s tools with industry-leading platforms (e.g. Python, cloud services, mobile devices, etc.).

Prior to joining Kitware, Stephen was a tenured associate professor of Radiology, Computer Science, and Surgery at UNC. He led the Computer-Aided Diagnosis and Display Laboratory that consisted of 13 faculty and staff members pursuing research projects in computer-aided diagnosis for mammography, neurosurgical planning and guidance, partial liver transplant planning, and cancer assessment based on vascular morphology.

During his time at UNC, Stephen saw the large gap between most medical imaging research and clinical needs/practice. This led to his ongoing work on intra-subject abdominal image registration in the presence of sliding organs, atlas-to-image registration in the presence of large pathologies (low-rank atlases), and registration in the presence of changing pathologies that may displace and/or infiltrate surrounding tissues (geometric metamorphosis). The resulting research and publications preceded a recent surge of activities in these areas. Stephen continues to be an adjunct associate professor for UNC’s Department of Computer Science where he assists on research projects and serves on dissertation committees. 

Stephen received his Ph.D. in computer science from the University of North Carolina, Chapel Hill in 1997. In 1992, he earned his certification in artificial intelligence from Washington University. Stephen received his master’s degree in artificial intelligence from the Georgia Institute of Technology in 1989 and his bachelor’s degree in computer science from Purdue University in 1988.

Education

Ph.D. in computer science from the University of North Carolina, Chapel Hill, 1997

Cert. in artificial intelligence from Washington University, 1992

M.S. in artificial intelligence from Georgia Institute of Technology, 1989

B.S. in computer science from Purdue University, 1988

Get to Know Stephen

What is your favorite thing about working at Kitware? The impact that we have. We are a small business generating open source software and custom software solutions that are being used to solve problems and advance knowledge in ways that we never imagined! I don’t think it is an exaggeration to say that every one of our developers has written code that has enabled, accelerated, or directly advanced research and products around the world, for the betterment of mankind.

What do you love most about what you do? The variety of work that we and the amazingly talented employees and collaborators we work with results in me learning something new almost every day. Our environment inspires learning and innovation and makes me excited to come to work every day.

Share something interesting about yourself that is not on your resume. I seek out collaborators and co-workers who are passionate about and very successful at something outside of their work: photography, mountain biking, mountain climbing, dogs, cats, music, weightlifting, running, whatever. If someone has the passion and perseverance to discover, pursue, and excel at something that they enjoy outside of work, they are likely to discover, pursue and excel at something that they enjoy at work.

Professional Associations & Service

  • Chair, Medical Open Network for AI (MONAI) Advisory Board, 2019-present
  • Associate editor, Society of Photo-Optical Instrumentation Engineers (SPIE) Journal of Medical Imaging, 2013-present
  • Member, Johns Hopkins University’s Laboratory for Computational Sensing and Robotics Strategic Advisory Board, 2013-present
  • Co-Organizer, Medical Image Computing and Computer-Assisted Intervention (MICCAI) Society Young Scientist Publication Impact Award, 2012-Present
  • Associate editor, Institute of Electrical and Electronics Engineers (IEEE) Transactions on Medical Imaging, 2002-present
  • Treasurer, MICCAI Society’s Executive Board of Directors, 2015 – 2021
  • Member, MICCAI Society’s Board of Directors, 2013-2021
  • Organizer, Point-of-Care Ultrasound MICCAI Workshop, 2017-2018, 2020
  • Fellow, MICCAI Society, 2019
  • Senior member, IEEE Computer Society, 2017
  • Venue chair, Information Processing in Medical Imaging (IPMI) Conference, 2017
  • Co-Chair, IEEE’s International Symposium on Biomedical Imaging Challenges, 2013, 2015-2017
  • Co-Chair, SPIE Medical Imaging Live Demonstrations Workshop, 2010-2015
  • Member, Common Toolkit Advisory Board, 2009-2015
  • President, Insight Software Consortium, 2003-2014
  • Co-Chair, SPIE Medical Imaging Computer-Aided Diagnosis Conference, 2013-2014

 

Publications

  1. K. Corona, K. Osterdahl, R. Collins, and A. Hoogs, "MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021. [URL]
  2. B. Richards, A. Hoogs, M. Dawkins, J. Taylor, S. Smith, J. Ault, and M. Seki, "Advanced Camera Technologies and Artificial Intelligence to Improve Marine Resource Surveys," in Ocean Sciences Meeting, 2020. [URL]
  3. A. Hoogs, M. Dawkins, B. Richards, G. Cutter, D. Hart, M. Clarke, W. Michaels, J. Crall, L. Sherrill, N. Siekierski, M. Woehlke, and K. Edwards, "An Open-Source System for Do-It-Yourself AI in the Marine Environment," in Ocean Sciences Meeting, 2020. [URL]
  4. A. Powell, M. Clarke, M. Dawkins, B. Richards, and A. Hoogs, "Moving Towards Machine Learning for the Analysis of Deep-Sea Imagery Collected by Autonomous Underwater Vehicle," in Ocean Sciences Meeting, 2020. [URL]
  5. A. Islam, C. Long, A. Basharat, and A. Hoogs, "Dual-Order Attentive Generative Adversarial Network for Image Copy-Move Forgery Detection and Localization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020.
  6. B. Vasu, J. Barnett, R. Collins, and A. Hoogs, "Explainability for Content-Based Image Retrieval," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2019.
  7. B. Dong, R. Collins, and A. Hoogs, "Explainability for Content-Based Image Retrieval," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop on Explainable Artificial Intelligence (AI), 2019.
  8. M. Brown, K. Fieldhouse, E. Swears, P. Tunison, A. Romlein, and A. Hoogs, "Multi-Modal Detection Fusion on a Mobile UGV for Wide-Area, Long-Range Surveillance," in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019. [URL]
  9. B. Richards, O. Beijbom, M. Campbell, M. Clarke, G. Cutter, M. Dawkins, D. Edington, D. Hart, M. Hill, A. Hoogs, D. Kriegman, E. Moreland, T. Oliver, W. Michaels, M. Placentino, A. Rollo, C. Thompson, F. Wallace, I. Williams, and K. Williams, "Automated Analysis of Underwater Imagery: Accomplishments, Products, and Vision," NOAA technical memorandum NMFS PIFSC, 2019. [URL]
  10. C. Funk, J. Crall, W. Hicks, C. Law, P. Tunison, R. Blue, A. Hoogs, T. Rovito, and A. Maltenfort, "WEFT Feature Detection and Mensuration for Airplane Classification in Satellite Imagery," in MSS National Symposium on Sensor and Data Fusion, 2019.
  11. C. Long, A. Basharat, and A. and Hoogs, "A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Forged Videos," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.
  12. C. Long, R. Collins, E. Swears, and A. Hoogs, "Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.
  13. M. Phillips, R. Blue, M. Turek, A. Hoogs, and T. Rovito, "Deep Learning for Classification of Satellite Imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
  14. C. Law, R. Blue, D. Stoup, P. Tunison, A. Hoogs, B. Vasu, J. Van Cor, J. Kerekes, A. Savakis, T. Rovito, C. Stansifer, and S. Thomas, "Deep Learning for Object Detection and Classification in Satellite Imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
  15. J. Crall, J. Becker, P. Tunison, M. Dawkins, A. Basharat, R. Blue, M. Turek, and A. Hoogs, "Deep Learning for Small Object Detection in Satellite Infrared Imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
  16. M. Brown, K. Fieldhouse, E. Swears, P. Tunison, A. Romlein, and A. Hoogs, "Multi-Modal Detection Fusion on Mobile UGV for Squad-Level Threat Alerting," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
  17. A. Basharat, P. Tunison, and A. Hoogs, "Rapid Learning of Maritime Scenes Through Query Refinement," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
  18. Z. Sun and A. Hoogs, "Compact image representation by binary component analysis," in Proceedings of the IEEE International Conference on Image Processing, 2017. [URL]
  19. E. Wengrowski, Z. Sun, and A. Hoogs, "Reflection correspondence for exposing photograph manipulation," in Proceedings of the IEEE International Conference on Image Processing, 2017. [URL]
  20. M. Dawkins, L. Sherrill, K. Fieldhouse, A. Hoogs, B. Richards, D. Zhang, L. Prasad, K. Williams, N. Lauffenburger, and G. Wang, "An open-source platform for underwater image and video analytics," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2017. Winner, Best Paper Honorable Mention. [URL]
  21. C. Long, E. Smith, A. Basharat, and A. Hoogs, "A C3D-based convolutional neural network for frame dropping detection in a single video shot," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop on Media Forensics, 2017. [URL]
  22. Z. Sun and A. Hoogs, "Object insertion and removal in images with mirror reflection," in Proceedings of the IEEE Workshop on Information Forensics and Security, 2017. [URL]
  23. K. Fieldhouse, A. Hoogs, and E. Swears, "Linking Unmanned Systems, Visible and IR Video, Computer Vision, and Humans Together for Real-Time, Squad-Level, Battlefield Situational Awareness: THreat Reconnaissance and Exploitation from Audio-video Target eXtraction (THREAT X)," in AUVSI XPONENTIAL Conference, 2017.
  24. J. Moeller, E. Smith, A. Basharat, M. Turek, A. Hoogs, and E. Blasch, "Automatic pattern of life learning in satellite images through graph kernels," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2017.
  25. C. Law, J. Parham, M. Dawkins, P. Tunison, D. Stoup, R. Blue, K. Fieldhouse, M. Turek, A. Hoogs, S. Han, A. Farafard, J. Kerekes, E. Lentilucci, M. Gartley, T. Savakis, T. Rovito, S. Thomas, and C. Stansifer, "Deep learning for object detection and object-based change detection in satellite imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2017.
  26. K. Fieldhouse, A. Hoogs, and E. Swears, "Fusing Visible and Infrared (IR) Video on Mobile Robots, UAVs and Warfighters for Real-Time, Squad-Level Situational Awareness," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2017.
  27. M. Dawkins, R. Collins, and A. Hoogs, "Using Convolutional Neural Networks for Content-Based FMV Retrieval," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2017.
  28. R. Porter, A. Basharat, R. Collins, M. Turek, and A. Hoogs, "Training and evaluating object detection pipelines with connected components," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2016. [URL]
  29. S. Oh, M. Pandey, I. Kim, and A. Hoogs, "Image-oriented economic perspective on user behavior in multimedia social forums: An analysis on supply, consumption, and saliency," Pattern Recognition Letters, vol. 72, pp. 33-40, Mar. 2016. [URL]
  30. M. Dawkins, A. Basharat, J. Becker, M. Turek, and A. Hoogs, "Deep architecture for small mover detection in overhead infrared imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2016.
  31. P. Tunison, M. Turek, and A. Hoogs, "Functional scene element modeling for ISR data," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2016.
  32. Z. Sun, J. Baumes, P. Tunison, M. Turek, and A. Hoogs, "Tattoo detection and localization using region-based deep learning," in Proceedings of the IEEE International Conference on Pattern Recognition, 2016. [URL]
  33. M. Dawkins, Z. Sun, J. Becker, A. Basharat, A. Hoogs, and M. Turek, "Track Object Type Classification Across a Range of Scales and Types of Video," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2016.
  34. M. Turek, A. Basharat, K. Fieldhouse, P. Tunison, D. Stoup, C. Atkins, and A. Hoogs, "Real-time, full-frame wide area motion imagery analytics," in Proceedings of the MSS National Symposium on Passive Sensors, 2015.
  35. A. Hoogs, A. Perera, R. Collins, A. Basharat, K. Fieldhouse, C. Atkins, L. Sherrill, B. Boeckel, R. Blue, M. Woehlke, C. Greco, Z. Sun, E. Swears, N. Cuntoor, J. Luck, B. Drew, D. Hanson, D. Rowley, J. Kopaz, T. Rude, D. Keefe, A. Srivastava, S. Khanwalkar, A. Kumar, C. Chen, J. Aggarwal, L. Davis, Y. Yacoob, A. Jain, D. Liu, S. Chang, B. Song, A. Roy-Chowdhury, K. Sullivan, J. Tesic, S. Chandrasekaran, B. Manjunath, X. Wang, Q. Ji, K. Reddy, J. Liu, M. Shah, K. Chang, T. Chen, and M. Desai, "An end-to-end system for content-based video retrieval using behavior, actions, and appearance with interactive query refinement," in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2015. [URL]
  36. K. Fieldhouse, M. Leotta, A. Basharat, R. Blue, D. Stoup, C. Atkins, L. Sherrill, B. Boeckel, P. Tunison, J. Becker, M. Dawkins, M. Woehlke, R. Collins, M. Turek, and A. Hoogs, "KWIVER: An open source cross-platform video exploitation framework," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2014. [URL]
  37. E. Swears, A. Basharat, A. Hoogs, and E. Blasch, "Probabilistic sub-GRAph Matching (PGRAM) for video and text fusion," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2014.
  38. A. Basharat, M. Turek, Y. Xu, C. Atkins, D. Stoup, K. Fieldhouse, P. Tunison, and A. Hoogs, "Real-time multi-target tracking at 210 megapixels/second in Wide Area Motion Imagery," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2014. [URL]
  39. E. Swears, A. Hoogs, Q. Ji, and K. Boyer, "Complex Activity Recognition Using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014. [URL]
  40. E. Swears, A. Hoogs, and K. Boyer, "Pyramid Coding for Functional Scene Element Recognition in Video Scenes," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014.
  41. M. Dawkins, Z. Sun, A. Basharat, A. Perera, and A. Hoogs, "Tracking nautical objects in real-time via layered saliency detection," in SPIE Defense, Security, and Sensing Motion Imagery for ISR and Situational Awareness, 2014. [URL]
  42. E. Blasch, J. Nagy, A. Aved, E. Jones, W. Pottenger, A. Basharat, A. Hoogs, M. Schneider, R. Hammoud, G. Chen, D. Shen, and H. Ling, "Context aided video-to-text information fusion," in International Conference on Information Fusion, 2014.
  43. Z. Sun, M. Leotta, A. Hoogs, R. Blue, R. Neuroth, J. Vasquez, A. Perera, M. Turek, and E. Blasch, "Vehicle change detection from aerial imagery using detection response maps," in SPIE Defense, Security, and Sensing Motion Imagery for ISR and Situational Awareness, 2014. [URL]
  44. Y. Xu, A. Basharat, J. Becker, and A. Hoogs, "Complex algorithm optimization through probabilistic search of its configuration tree," in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2014. [URL]
  45. M. Dawkins, A. Perera, and A. Hoogs, "Real-time heads-up display detection in video," in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2014. [URL]
  46. S. Oh, M. Pandey, I. Kim, A. Hoogs, and J. Baumes, "Personalized Economy of Images in Social Forums: An Analysis on Supply, Consumption, and Saliency," in Proceedings of the IEEE International Conference on Pattern Recognition, 2014. Winner, Best Industry-Related Paper Award. [URL]
  47. Y. Xu, D. Song, and A. Hoogs, "An Efficient Online Hierarchical Supervoxel Segmentation Algorithm for Time-critical Applications," in Proceedings of the British Machine Vision Conference, 2014. [URL]
  48. M. Dawkins and A. Hoogs, "Automatic image-plane aligned obstruction detection in EO and IR video," in Proceedings of the MSS National Symposium on Passive Sensors, 2014.
  49. F. Porikli, F. Bremond, S. Dockstader, J. Ferryman, A. Hoogs, B. Lovell, S. Pankanti, B. Rinner, P. Tu, and P. Venetianer, "Video surveillance: past, present, and now the future [DSP Forum]," IEEE Signal Processing Magazine, vol. 30, no. 3, pp. 190-198, May 2013. [URL]
  50. E. Swears, A. Hoogs, and R. Blue, "Recognizing Activity Based Scene Elements in Video," in Automatic Target Recognition Working Group, 2013.
  51. Y. Xu, S. Oh, and A. Hoogs, "A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013. [URL]
  52. E. Swears, A. Hoogs, and K. Boyer, "Pyramid Coding for Functional Scene Element Recognition in Video Scenes," in Proceedings of the IEEE International Conference on Computer Vision, 2013. [URL]
  53. Y. Xu, S. Oh, F. Yang, Z. Jiang, N. Cuntoor, A. Hoogs, and L. Davis, "System and algorithms on detection of objects embedded in perspective geometry using monocular cameras," in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2013. [URL]
  54. M. Ryoo, A. Hoogs, A. Basharat, and S. Oh, "Human activity recognition for visual surveillance," presented at 2012 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2012. [URL]
  55. K. Reddy, N. Cuntoor, A. Perera, and A. Hoogs, "Human Action Recognition in Large-Scale Datasets Using Histogram of Spatiotemporal Gradients," in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2012. [URL]
  56. N. Cuntoor, R. Collins, and A. Hoogs, "Human-robot teamwork using activity recognition and human instruction," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. [URL]
  57. E. Swears and A. Hoogs, "Learning and recognizing complex multi-agent activities with applications to american football plays," in Proceedings of the IEEE Workshop on Applications of Computer Vision, 2012. [URL]
  58. E. Swears, M. Turek, R. Collins, A. Perera, and A. Hoogs, "Automatic activity profile generation from detected functional regions for video scene analysis," in Video Analytics for Business Intelligence. Springer, 2012, pp. 241-269. [URL]
  59. A. Perera, S. Oh, M. P, T. Ma, A. Hoogs, A. Vahdat, K. Cannons, G. Mori, S. Mccloskey, B. Miller, S. Venkatesh, P. Davalos, P. Das, C. Xu, J. Corso, R. Srihari, I. Kim, Y. Cheng, Z. Huang, C. Lee, K. Tang, L. Fei-fei, and D. Koller, "TRECVID 2012 GENIE: multimedia event detection and recounting," in NIST TRECVID Workshop, 2012.
  60. S. Oh, A. Hoogs, A. Perera, N. Cuntoor, C. Chen, J. Lee, S. Mukherjee, J. Aggarwal, H. Lee, L. Davis, E. Swears, X. Wang, Q. Ji, K. Reddy, M. Shah, C. Vondrick, H. Pirsiavash, D. Ramanan, J. Yuen, A. Torralba, B. Song, A. Fong, A. Roy-Chowdhury, and M. Desai, "A large-scale benchmark dataset for event recognition in surveillance video," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011. [URL]
  61. E. Swears and A. Hoogs, "Complex Activity Recognition using Granger Constrained Dynamic Bayesian Network," in Learning Workshop, Fort Lauderdale Florida, 2011.
  62. M. Turek, A. Hoogs, and R. Collins, "Unsupervised Learning of Functional Categories in Video Scenes," in Proceedings of the European Conference on Computer Vision, 2010. [URL]
  63. Z. Sun and A. Hoogs, "Image comparison by compound disjoint information with applications to perceptual visual quality assessment, image registration and tracking," International Journal of Computer Vision, vol. 88, no. 3, pp. 461-488, Jul. 2010. [URL]
  64. N. Cuntoor, A. Basharat, A. Perera, and A. Hoogs, "Track Initialization in Low Frame Rate and Low Resolution Videos," in Proceedings of the IEEE International Conference on Pattern Recognition, 2010. [URL]
  65. S. Oh and A. Hoogs, "Unsupervised Learning of Activities in Video Using Scene Context," in Proceedings of the IEEE International Conference on Pattern Recognition, 2010. [URL]
  66. S. Oh, A. Hoogs, M. Turek, and R. Collins, "Content-Based Retrieval of Functional Objects in Video Using Scene Context," in Proceedings of the European Conference on Computer Vision, 2010. [URL]
  67. E. Swears and A. Hoogs, "Functional scene element recognition for video scene analysis," in Proceedings of the IEEE Workshop on Motion and Video Computing, 2009. [URL]
  68. A. Perera, R. Collins, and A. Hoogs, "Evaluation of compression schemes for wide area video," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2008. [URL]
  69. K. Barnard, Q. Fan, R. Swaminathan, A. Hoogs, R. Collins, P. Rondot, and J. Kaufhold, "Evaluation of localized semantics: data, methodology, and experiments," International Journal of Computer Vision, vol. 77, no. 1, pp. 199-217, May 2008. [URL]
  70. A. Hoogs and A. Perera, "Video Activity Recognition in the Real World," in Proceedings of the AAAI National Conference on Artificial Intelligence, 2008. [URL]
  71. A. Hoogs, S. Bush, G. Brooksby, A. Perera, M. Dausch, and N. Krahnstoever, "Detecting Semantic Group Activities Using Relational Clustering," in Proceedings of the IEEE Workshop on Motion and Video Computing, 2008. [URL]
  72. E. Swears, A. Hoogs, and A. Perera, "Learning Motion Patterns in Surveillance Video using HMM Clustering," in Proceedings of the IEEE Workshop on Motion and Video Computing, 2008. [URL]
  73. F. Wheeler and A. Hoogs, "Moving Vehicle Registration and Super-Resolution," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2007. [URL]
  74. M. Chan, A. Hoogs, Z. Sun, J. Schmiederer, R. Bhotika, and G. Doretto, "Event recognition with fragmented object tracks," in Proceedings of the IEEE International Conference on Pattern Recognition, 2006. [URL]
  75. A. Perera, A. Hoogs, C. Srinivas, G. Brooksby, and W. Hu, "Evaluation of Algorithms for Tracking Multiple Objects in Video," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2006. [URL]
  76. M. Chan, A. Hoogs, R. Bhotika, A. Perera, J. Schmiederer, and G. Doretto, "Joint Recognition of Complex Events and Track Matching," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006. [URL]
  77. A. Perera, G. Brooksby, A. Hoogs, and G. Doretto, "Moving Object Segmentation using Scene Understanding," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2006. [URL]
  78. J. Kaufhold, R. Collins, A. Hoogs, and P. Rondot, "Recognition and Segmentation of Scene Content using Region-Based Classification," in Proceedings of the IEEE International Conference on Pattern Recognition, 2006. [URL]
  79. Z. Sun and A. Hoogs, "Image comparison by compound disjoint information," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006. [URL]
  80. A. Perera, C. Srinivas, A. Hoogs, G. Brooksby, and W. Hu, "Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006. [URL]
  81. A. Hoogs and R. Collins, "Object Boundary Detection in Images using a Semantic Ontology," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2006. [URL]
  82. A. Hoogs, M. Chan, R. Bhotika, and J. Schmiederer, "Recognizing complex behaviors in aerial video," in Internation Conference on Interlligence Analysis, 2005.
  83. R. Kaucic, A. Perera, G. Brooksby, J. Kaufhold, and A. Hoogs, "A Unified Framework for Tracking through Occlusions and across Sensor Gaps," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005. [URL]
  84. M. Chan, A. Hoogs, J. Schmiederer, and M. Petersen, "Detecting rare events in video using semantic primitives with HMM," in Proceedings of the IEEE International Conference on Pattern Recognition, 2004. [URL]
  85. J. Kaufhold and A. Hoogs, "Learning to segment images using region-based perceptual features," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004. [URL]
  86. A. Perera and A. Hoogs, "Bayesian object-level change detection in grayscale imagery," in Proceedings of the IEEE International Conference on Pattern Recognition, 2004. [URL]
  87. J. Rittscher, A. Blake, A. Hoogs, and G. Stein, "Mathematical modelling of animate and intentional motion," Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 358, no. 1431, pp. 475-490, Mar. 2003. [URL]
  88. G. Stein, J. Rittscher, and A. Hoogs, "Enabling video annotation using a semantic database extended with visual knowledge," in International Conference on Multimedia and Expo, 2003. [URL]
  89. A. Hoogs, R. Collins, R. Kaucic, and J. Mundy, "A common set of perceptual observables for grouping, figure-ground discrimination, and texture classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 4, pp. 458-474, Apr. 2003. [URL]
  90. A. Hoogs, J. Rittscher, G. Stein, and J. Schmiederer, "Video content annotation using visual analysis and a large semantic knowledgebase," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2003. [URL]
  91. A. Hoogs, R. Collins, and R. Kaucic, "Classification of 3D macro texture using perceptual observables," in Proceedings of the IEEE International Conference on Pattern Recognition, 2002. [URL]
  92. A. Hoogs, R. Kaucic, and R. Collins, "Using video for recovering texture," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2001. [URL]
  93. A. Hoogs, J. Mundy, and G. Cross, "Multi-modal fusion for video understanding," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2001. [URL]
  94. A. Hoogs and J. Mundy, "Rapid data reduction and target detection in literal imagery," in Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, 2000. [URL]
  95. A. Hoogs and J. Mundy, "Information fusion for EO object detection and delineation," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2000.
  96. A. Hoogs and J. Mundy, "An integrated boundary and region approach to perceptual grouping," in Proceedings of the IEEE International Conference on Pattern Recognition, 2000. [URL]
  97. J. Mundy and A. Hoogs, "Data representations for object-level change detection in eo/ir imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 1999.
  98. A. Hoogs and J. Mundy, "Segmentation and geometry: an integrated representation," in Workshop on the Integration of Appearance and Geometric Methods in Object Recognition, 1999.
  99. A. Hoogs, D. Hackett, and D. Dadd, "Image Understanding at Lockheed Martin Management and Data Systems," in Proceedings of the DARPA Image Understanding Workshop, 1998.
  100. A. Hoogs, "Analysis of Learning Using Segmentation Models," in Conference on Computer Analysis of Images and Patterns, 1997.
  101. A. Hoogs, D. Hackett, and T. Barrett, "Image Understanding at Lockheed Martin Valley Forge," in Proceedings of the DARPA Image Understanding Workshop, 1997.
  102. A. Hoogs, B. Bremner, and D. Hackett, "The RADIUS Phase II Program," in Proceedings of the DARPA Image Understanding Workshop, 1997.
  103. M. Puscar and A. Hoogs, "User Interface Representations for Image Understanding," in Proceedings of the DARPA Image Understanding Workshop, 1997.
  104. A. Hoogs, "RADIUS: Imagery Understanding for Imagery Intelligence," RADIUS, Jul. 1997.
  105. A. Hoogs, "Combining Geometric and Appearance Models for Change Detection," in Proceedings of the DARPA Image Understanding Workshop, 1997.
  106. R. Cardenas and A. Hoogs, "The RADIUS Testbed Database: Temporal Queries and Optimization," in Proceedings of the SPIE Workshop on Applied Imagery and Pattern Recognition, 1996.
  107. A. Hoogs and R. Bajcsy, "Model-Based Learning of Segmentations," in Proceedings of the IEEE International Conference on Pattern Recognition, 1996.
  108. B. Kniffin and A. Hoogs, "Database Support for Exploitation Image Understanding," in Proceedings of the ARPA Image Understanding Workshop, 1996.
  109. B. Bremner, A. Hoogs, and J. Mundy, "Integration of Image Understanding Exploitation Algorithms in the RADIUS Testbed," in Proceedings of the ARPA Image Understanding Workshop, 1996.
  110. A. Hoogs, "Pose Adjustment Using a Parameter Hierarchy," in Proceedings of the ARPA Image Understanding Workshop, 1996.
  111. A. Hoogs and R. Bajcsy, "Segmentation Modeling," in Conference on Computer Analysis of Images and Patterns, 1995.
  112. B. Kniffin and A. Hoogs, "Combining Database Support for Image Understanding and Model-Supported Exploitation," in Proceedings of the SPIE Workshop on Applied Imagery and Pattern Recognition, 1995.
  113. A. Hoogs, "Object Position Refinement Using Hierarchical Search," in Proceedings of the SPIE Workshop on Applied Imagery and Pattern Recognition, 1995.
  114. A. Hoogs and R. Bajcsy, "Using Scene Context to Model Segmentations," in Workshop on Context-Based Vision, 1995.
  115. A. Hoogs and D. Hackett, "Model-Supported Exploitation as a Framework for Image Understanding," in Proceedings of the ARPA Image Understanding Workshop, 1994.
  116. R. Bajcsy and A. Hoogs, "Segmentation Characterization for Change Detection," in Proceedings of the ARPA Image Understanding Workshop, 1994.
  117. A. Hoogs and B. Kniffin, "The RADIUS Testbed Database: Issues and Design," in Proceedings of the ARPA Image Understanding Workshop, 1994.
  118. J. Mundy, R. Welty, L. Quam, T. Strat, W. Bremner, M. Horwedel, D. Hackett, and A. Hoogs, "The RADIUS Common Development Environment," in Proceedings of the DARPA Image Understanding Workshop, 1992.
  119. J. Ponce, A. Hoogs, and D. Kriegman, "On using CAD models to compute the pose of curved 3D objects," in Workshop on Directions in Automated CAD-Based Vision, 1991. [URL]

Bibliography generated 2021-06-04-04:03:35 (4538)