Ray Ptucha

Bio  -   Publications  -   Projects  -   Patents  -   Teaching  -   Awards

   
Ray Ptucha
Contact Infomation:
Email: rwpeec@rit.edu
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Address: 221 Scofield Rd
  Honeoye Falls
  NY 14472
   
   
Bio
Ray is a an Assistant Professor in Computer Engineering at Rochester Institute of Technology specializing in machine learning, computer vision, robotics, and embedded control. Ray was a research scientist with Eastman Kodak Company for 20 years where he worked on computational imaging algorithms and was awarded 27 U.S. patents with another 19 applications on file. He graduated from SUNY/Buffalo with a B.S. in Computer Science (1988) and a B.S. in Electrical Engineering (1989). He earned a M.S. in Image Science from RIT in 2002. He earned a Ph.D. in Computer Science from RIT in 2013. Ray was awarded an NSF Graduate Research Fellowship in 2010 and his Ph.D. research earned the 2014 Best RIT Doctoral Dissertation Award. Ray is a passionate supporter of STEM education and an active member of his local IEEE chapter.
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Publications
Journal Articles and Book Chapters
  • R.W. Ptucha, A. Savakis, “LGE-KSVD: Robust Sparse Representation Classification”, IEEE Transactions on Image Processing, Volume 23, Issue 4, 2014.
  • R.W. Ptucha, A. Savakis, “Manifold Based Sparse Representation for Facial Understanding in Natural Images”, Image and Vision Computing, vol. 31, pp. 365-378, 2013.
  • R.W. Ptucha, A. Savakis, “Facial Expression Recognition”, IGI Global Encyclopedia of Information Science and Technology, 3rd Edition, 2013.
  • R.W. Ptucha, A. Savakis, “Keypoint Matching and Image Registration Using Sparse Representations”, IEEE Transactions on Image Processing, in preparation.
Conference Articles
  • M. Dushkoff and R. Ptucha, “Adaptive activation functions for deep networks”, Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2016.
  • V. Chennupati, S. Nooka, S. Sah, R. Ptucha, “Hierarchical decomposition of large deep networks”, Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2016.
  • A. Rajanna, R. Ptucha, S. Sinha, N. Rao, “Prostate cancer detection using photoacoustic imaging and deep learning”, Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2016.
  • N. Schrading, C. Alm, R. Ptucha, C. Homan, “An Analysis of Domestic Abuse Discourse on Reddit”, Proceedings of Empirical Methods in Natural Language Processing (EMNLP), Lisbon, Portugal, 2015.
  • A. Rajanna, K. Aryafar, A. Shokoufandeh, R. Ptucha, “Deep Neural Networks: A Case Study for Music Genre Classification,” Proceedings of 14th IEEE International Conference on Machine Learning and Applications, Miami, FL, 2015.
  • N. Briviano, M. Daigneau, J. Danko, C. Goss, A. Hintz, S. Kuhr, B. Tarloff, J. Kaemmerlen, R. Ptucha, “Autonomous People Mover: Adding Sensors”, NY Cyber Security & Eng. Tech. Assoc., Rochester, NY, 2015.
  • H. Kumar, R. Ptucha, “Gesture Recognition Using Active Body Parts and Active Difference Signatures,” Proceedings of International Conference on Image Processing, Quebec City, Canada, 2015.
  • N. Schrading, C. Alm, R. Ptucha, and C. Homan, “#WhyIStayed, #WhyILeft: Microblogging to Make Sense of Domestic Abuse,” Proceedings of North American Chapter of the Association for Computational Linguistics- Human Language Technologies (NAACL-HLT), Denver, CO, 2015.
  • K. Knowles, N. Bovee, P. Gelose, D. Le, K. Martin, M. Pressman, J. Zimmerman, R. Lux, R. Ptucha, “Autonomous People Mover,” Proceedings of American Society for Engineering Education, Syracuse, NY, 2015.
  • A. Wong, M. Yousefhussien, R.W. Ptucha, “Localization Using Omnivision-based Manifold Particle Filters”, Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2015.
  • M. Yousefhussien, et al., “Flatbed Scanner Simulation to Analyze the Effect of Detector’s Size on Color Artifacts”, Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2015.
  • A. Savakis, R. Rudra, R.W. Ptucha, “Gesture Control Using Active Difference Signatures and Sparse Learning”, Proceedings of International Conference on Pattern Recognition, Stockholm, Sweden, 2014.
  • C. Merkel, D. Kudithipudi, R.W. Ptucha, “Heterogeneous CMOS/Memristor Neural Networks for Real-time Target Classification”, SPIE Machine Intelligence and Bio-inspired Computation, STA14-ST141-28, 2014.
  • R.W. Ptucha, D. Kloosterman, B. Mittelstaedt, A. Loui, “Automatic Image Assessment from Facial Attributes”, Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2014.
  • P. Hays, R.W. Ptucha, R. Melton, “Mobile Device to Cloud Co-processing of ASL Finger Spelling to Text Conversion”, Proceedings of IEEE Western NY Image Processing Workshop, Rochester, NY, 2013.
  • R.W. Ptucha, S. Azary, A. Savakis, “Keypoint Matching and Image Registration Using Sparse Representations”, Proceedings of International Conference on Image Processing, Melbourne, Australia, 2013.
  • C. Bellmore, R.W. Ptucha, A. Savakis, “Fusion of Depth and Color for an Improved Active Shape Model”, Proceedings of International Conference on Image Processing, Melbourne, Australia, 2013.
  • R.W. Ptucha, A. Savakis, “LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse Representation Classification”, Proceedings of AMFG Workshop, Computer Vision and Pattern Recognition, Portland, OR, 2013.
  • R.W. Ptucha, A. Savakis, “Joint Optimization of Manifold Learning and Sparse Representations”, Proceedings of Automatic Face and Gesture Recognition, Shanghai, China, 2013.
  • R.W. Ptucha, D. Rhoda, B. Mittelstaedt, “Auto Zoom Crop from Face Detection and Facial Features”, Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2013.
  • R.W. Ptucha, A. Savakis, “Fusion of Static and Temporal Predictors for Unconstrained Facial Expression Recognition”, Proceedings of International Conference on Image Processing, Orlando, FL, 2012.
  • R.W. Ptucha, A. Savakis, “Towards the Usage of Optical Flow Temporal Features for Facial Expression Classification”, Proceedings of International Symposium on Visual Computing, Crete, Greece, 2012.
  • R.W. Ptucha, A. Savakis, “How Connections Matter: Factors Affecting Student Performance in STEM Disciplines”, Proceedings of IEEE Integrated STEM Education Conference, Princeton, NJ, 2012.
  • C. Bellmore, R.W. Ptucha, A. Savakis, “Interactive Display Using Depth and RGB Sensors for Face and Gesture Control”, IEEE Western NY Image Processing Workshop, Rochester, NY, 2011.
  • R.W. Ptucha, G. Tsagkatakis, A. Savakis, “Manifold Based Sparse Representation for Robust Expression Recognition without Neutral Subtraction”, Proceedings of BeFIT Workshop, International Conference on Computer Vision, Barcelona, Spain, 2011.
  • R.W. Ptucha, G. Tsagkatakis, A. Savakis, “Manifold Learning for Simultaneous Pose and Facial Expression Recognition”, Proceedings of International Conference on Image Processing , Brussels, Belgium, 2011.
  • R.W. Ptucha, A. Savakis, “Facial Expression Recognition Using Facial Features and Manifold Learning”, Proceedings of International Symposium on Visual Computing, Las Vegas, NV, 2010.
  • R.W. Ptucha, A. Savakis, “Pose Estimation Using Facial Feature Points and Manifold Learning”, Proceedings of International Conference on Image Processing, Hong Kong, 2010.
  • R.W. Ptucha, A. Savakis, “Facial Pose Tracking for Interactive Display”, IEEE Western NY Image Processing Workshop, Rochester, NY, 2009.
  • R.W. Ptucha, A. Savakis, “Facial Pose Estimation Using a Symmetrical Feature Model”, Proceedings of ICME- Workshop on Media Information Analysis for Personal and Social Applications, New York, NY, 2009.
  • R.W. Ptucha, “Correction of High Frequency Smear in Thermal Printers”, Proceedings of IS&T 22nd Annual Non-Impact Printing (NIP22) Conference, Denver, CO, 2006.
  • R.W. Ptucha, “Image Quality Assessment of Digital Scanners and Electronic Still Cameras”, Proceedings of IS&T Image Processing, Image Quality, Image Capture Systems (PICS) Conference, Savannah GA, 1999.
  • C.M. Daniels, R.W. Ptucha, L. Schaefer, “The Necessary Resolution to Zoom and Crop Hardcopy Images”, Proceedings of IS&T Image Processing, Image Quality, Image Capture Systems (PICS) Conference, Savannah GA, 1999.
Competitions and Presentations
  • A. Bhatt, M. Dushkoff, S. Echefu, M. Shoaib, K. Wieszchowski, R.W. Ptucha, "Voice Activated Wheelchair", Effective Access Technology Conference, Rochester, NY, 2015.
  • R.W. Ptucha, “Enabling Ubiquitous Computing,” keynote speaker at Rochester Institute of Technology’s Graduate Research and Creativity Symposium, Rochester, NY, 2015.
  • R.W. Ptucha, “Deep Belief Networks,” IEEE Society for Imaging Science & Technology, Rochester, NY, 2015.
  • R.W. Ptucha, “Machine Learning with Deep Belief Networks,” Invited tutorial: Western New York Image and Signal Processing Workshop, Rochester, NY, 2014.
  • R. Rudra, H. Kumar, R.W. Ptucha, “Sparse Based Gesture Recognition”, 2014 ChaLearn Gesture Challenge, 2014.
  • R.W. Ptucha, “Machine Learning for Intelligent Behavior”, IEEE Rochester Section Joint Chapters Meeting, Rochester, NY, 2014.
  • C. Bellmore, R.W. Ptucha, A. Savakis, “Interactive Storefront Display with Face and Gesture Control”, ChaLearn Gesture Challenge entry at International Conference on Pattern Recognition, Tsukuba, Japan, 2012.
  • R.W. Ptucha, “Joint Optimization of Manifold Learning and Sparse Representations for Face and Gesture Analysis”, Artificial Intelligence Seminar, Cornell University, 2013.
  • S. Azary, R.W. Ptucha, A. Savakis, “Pervasive Intelligence Aided by Depth Sensors”, International Conference on Computational Photography, Cambridge, MA, 2013.
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Projects
  • Deep Learning: Inspired by the human brain, deep learning methods have revolutionized the computer vision, natural language processing, and pattern recognition communities. The Machine Intelligence Laboratory, directed by Dr. Ptucha, is researching advanced deep learning algorithms: hierarchical architectures, adaptive activation functions, statistical convolutional networks, and temporal recurrence. This algorithm research is yielding state-of-the-art results in still and video understanding, natural language processing, and graph theory. Applications of this research include medical imaging, image annotation, activity understanding, object recognition, voice recognition, advanced human-computer interaction, and autonomous driving.
  • Voice Activated Wheelchair: An effective access wheelchair is being designed to help elderly and disabled patients safely navigate through busy environments while smartly recognizing objects and surroundings. Motorized wheelchairs have improved the lives of countless individuals. For those who have trouble with a joystick, wheelchairs have been retrofitted to move based upon eye blinks, breathing tubes, and head pose. This project is developing a natural wheelchair interface, offering the patient the ability to control their wheelchair using typical and intuitive human-to-human methods such as voice and facial expression. The voice commands give visually impaired or wheelchair patients with restricted hand mobility, such as stroke, arthritis, limb injury, Parkinson’s, cerebral palsy or multiple sclerosis, the freedom to move independently. The advanced object and scene recognition give patients the ability and confidence to move safely and reliably through complex environments.
  • Autonomous People Mover: Self-driving cars will make our roadways safer, our environment cleaner, our roads less congested, and our lifestyles more efficient. Imagine a people mover on the RIT campus that stands ready at all times to give people rides across campus. A person needing a lift sends a text to the people mover service. The people mover (golf cart) drives over to the location of the person, asks where they want to go, and then drives them to the destination of their choosing. The key differentiator with this project is there is no human driver- the people mover is driving fully autonomously. The hardware and software for high speed highway driving and low speed campus driving are very similar. The algorithms, including localization, obstacle avoidance, and navigation, are almost identical. The autonomous people mover project is a multi-year senior design project. Phase I converted the golf cart into a remote control vehicle. Phase II is robustifying the control systems and introducing advanced sensors. Phase III is currently working towards autonomous driving in restricted settings. Future phases will enable the vehicle to drive autonomously in real-world scenarios. Key tasks involve LIDAR and vision integration, localization, path planning, path following, and object detection/recognition/tracking.
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Patents
  • “Group Display System”, U.S. Patent 9,179,102, Nov. 3, 2015.
  • “Image Recomposition from Face Detection and Facial Features”, U.S. Patent 9,025,836, May 5, 2015.
  • “Image Recomposition from Face Detection and Facial Features”, U.S. Patent 9,025,835, May 5, 2015.
  • “Image Recomposition from Face Detection and Facial Features”, U.S. Patent 9,008,436, April 14, 2015.
  • “Image Recomposition from Face Detection and Facial Features”, U.S. Patent 8,811,747, Jan. 20, 2015.
  • “Method for Producing Artistic Image Template Designs”, U.S. Patent 8,854,395, Oct. 7, 2014.
  • “Method for Matching Artistic Attributes of a Template and Secondary Images to a Primary Image”, U.S. Patent 8,849,853, Sept. 30, 2014.
  • “System for Matching Artistic Attributes of Secondary Image and Template to a Primary Image, U.S. Patent 8,849,043, Sept. 30, 2014.
  • “Image Recomposition from Face Detection and Facial Features”, U.S. Patent 8,811,747, Aug. 19, 2014.
  • “Method for Controlling Interactive Display System”, U.S. Patent 8,810,513, Aug. 19, 2014.
  • “Multi-user Interactive Display System”, U.S. Patent 8,723,796, May. 13, 2014.
  • “System for Coordinating User Images in an Artistic Design”, U.S. Patent 8,538,986, Sept. 17, 2013.
  • “System for Matching Artistic Attributes of Secondary Image and Template to a Primary Image”, U.S. Patent 8,422,794, April 16, 2013.
  • “Display System for Personalized Consumer Goods”, U.S. Patent 8,390,648, March 5, 2013.
  • “Context Coordination for an Artistic Digital Template for Image Display”, U.S. Patent 8,345,057, Jan 1, 2013.
  • “Method of Generating Artistic Template Designs”, U.S. Patent 8,332,427, Dec 11, 2012.
  • “Method of Making an Artistic Digital Template for Image Display”, U.S. Patent 8,289,340, Oct 16, 2012.
  • “Processing Digital Templates for Image Display”, U.S. Patent 8,274,523, Sept 25, 2012.
  • “Image Capture Method with Artistic Template Design”, U.S. Patent 8,237,819, Aug 7, 2012.
  • “Artistic Digital Template for Image Display”, U.S. Patent 8,212,834, July 3, 2012.
  • “Printer Having Differential Filtering Smear Correction”, U.S. Patent 7,847,979, Dec 7, 2010.
  • “Selection of Alternative Image Processing Operations to Maintain High Image Quality”, U.S. Patent 7,570,829 B2, Aug 4, 2009.
  • “Applying an Adjusted Image Enhancement Algorithm to a Digital Image”, U.S. Patent 7,428,332, Sept 23, 2008.
  • “Sharpening a Digital Image in Accordance With Magnification Values”, U.S. Patent 7,269,300, Sept 11, 2007.
  • “Method for Constructing Extended Color Gamut Digital Images From Limited Color Gamut Digital Images”, U.S. Patent 7,308,135, Dec 11, 2007.
  • “Method of Color Transformation for Processing Digital Images”, U.S. Patent 6,956,967, Oct 18, 2005.
  • “Method For Determining Necessary Resolution For Zoom And Crop Of Images”, U.S. Patent 6,643,416, Nov. 4, 2003.
  • 19 US Patents Pending.
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Teaching: University Courses
  • Machine Intelligence, CMPE-789, CE Dept, RIT, 2014/15.
  • Interface & Digital Electronics, CMPE-460, CE Dept, RIT, 2014/15.
  • Introduction to Computer Engineering, CMPE-110, CE Dept, RIT, 2013.
  • Robotics, 0306-675, 0306-775, CE Dept, RIT, Adjunct Professor: 2007-2013.
  • CE Multi-Disciplinary Senior Design, 0306-656, CE Dept, RIT, Adjunct Professor: 2009.
  • Engineering Fundamentals of Computer Systems, 0306-340, CE Dept, RIT, Adjunct Professor: 2007, 2008.
Teaching: Kodak Courses
  • Facial Processing, Invited Speaker for Kodak Science & Technology Conference, 2010.
  • Halftoning, Tutorial for Kodak Engineering Conference, 2008.
  • Bayesian Nets & Classifications, Tutorial for Kodak Engineering Conference, 2008.
  • Digital Imaging Algorithms- Under the Hood- Parts I & II, Tutorial for Kodak Engineering Conference, 2005.
  • Introduction to Digital Imaging, 12 hour class for Kodak Camera Club, 2005.
  • Digital Technology Module- Input, 8 hour internal class, 1x/year 1998-2006.
  • Digital Imaging Technology, 16 hour internal class, 3-4x/year 1998-2004.
  • Rendered Digital Input Path Algorithms- Under the Hood, Tutorial for Kodak Digital Imaging Conference, 2004.
  • Digital Imaging Algorithms- Under the Hood- Parts I & II, Tutorial for Kodak Digital Imaging Conference, 2003.
  • Digital Image Processing Within the Image State Diagram, Tutorial for Kodak Digital Imaging Conference, 2002.
  • How Can We Use Color To Our Advantage, Tutorial for Kodak Digital Imaging Conference, 2001.
  • Digital Scanner Best Practices: Image Processing Path, Kodak video training series, 1997.
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Awards
  • 2015 Keynote speaker for RIT Graduate Research Symposium.
  • 2014 Best RIT Doctoral Dissertation.
  • 2013 International Conference on Image Processing- Top 10% Paper Award.
  • 2013 Automatic Face and Gesture Recognition Doctoral Consortium recipient.
  • 2012 Computer Vision and Pattern Recognition Doctoral Consortium recipient.
  • Best presentation/paper, RIT Graduate Research Symposium, 2012.
  • 2010 National Science Foundation Graduate Research Fellowship recipient.
  • Best paper, RIT Graduate Research Symposium, 2009.
  • RIT Outstanding Adult Student Award, 2009.
  • Gold Program for Future Kodak Leaders, 2000-2004.
  • Kodak instructor of the year nominee, 1998-2005.
  • 2003 R&D Team Leadership finalist.
  • SOGP Masters Program, 2001-2002.
  • The DIMA Innovative Digital Product of PIMA ’98 for the Advantix FD-300 Film Scanner.
  • Image Science Career Development Program, 1994-1996.
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