The document summarizes the research capabilities and projects of the Cognitive Ubiquitous Computing Systems (CUBS) lab at SUNY Buffalo, led by Distinguished Professor Venu Govindaraju. The lab has strengths in multiple disciplines relevant to biometrics, document analysis, and security. It has a strong track record of technology transfer to industry partners and government sponsors such as the US Postal Service. Current research projects include biometric identification, document recognition, and enhancing security through techniques such as soft biometrics and detecting human versus synthetic inputs. The lab also has over 10 current PhD students and has graduated over 20 PhD alumni.
2. Overview Unique Capabilities Sponsors Technology Transfer Record Projects Biometrics Document Recognition and Retrieval Security People
3. Unique Capabilities Faculty strengths in multiple disciplines Behavioral Sciences, Social Issues Computer Vision, Visualization Chemical and Biological Sensors Pattern Recognition, Machine Learning Smart Environments, Pervasive Computing Spectroscopy Solid record of transferring of technology to field Large pool of current PhD students (10) Growing pool of PhD alumni in industry (25) Several current projects with industry
19. Q: The suspect is male 1st Iteration Pruned Set Q: The suspect has a beard 2nd Iteration Pruned Set Q: The suspect wears spectacles 3rd Iteration SUSPECT Soft BiometricsSemantic Face Retrieval Original Set
20. Unobtrusive People Tracking Freedom from Continuous Surveillance RECOGNIZE REASON Evolutionary Recognition RETRIEVE Did Bob and Frank meet at the library yesterday? Given building map, occupants, schedules, sensor locations
24. Multilingual Information Retrieval Q: Can we have a searchable archive of world’s newspapers? Q: All newspapers in any language translated to a common language? Central Repository Searchable database (digitized)
29. Soft BiometricsExpressions + = AU 6 AU 12 AU 6 & 12 + + = AU 1 AU 2 AU 4 AU 1, 2 & 4 + + = AU 1 AU 4 AU 15 AU 1, 4 & 15 FACS FEAR HAPPY ANGER SAD FEAR HAPPY SAD Facial Expression Manifold
34. PeopleFaculty Frank Bright SUNY Distinguished Professor Biological, Chemical Sensors VenuGovindaraju SUNY Distinguished Professor Machine Learning Director Mark Frank Professor Behavioral Sciences Raymond Fu Assistant Professor Computer Vision, Visualization Alex Cartwright Professor Spectroscopy, Photonics Bharat Jayaraman Professor Cyber Physical Systems
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36. PeopleCurrent PhD students; Research topics Xi Cheng Biometric Fusion Gaurav Kumar Mobile Device Apps UtkarshPoruwal People Tracking ChetanRamaiah Writer Identification Manavender Reddy Gesture Recognition Ricardo N. Rodriguez Multimodal Fusion ArtiShivaram Mobile Device Apps SafwanWshah Arabic Handwriting Recognition Daekeun You Medical Analytics Yingbou Zhou Image Segmentation
39. Contact VenuGovindaraju venu@cubs.buffalo.edu University at Buffalo State University of New York
Notas del editor
Some more detail concerning the impact of ruled line removal on word recognition:We extracted all the test word images from lined pages and measured the top choice recognition performance. Here are the numbers: -- Total word images in test set : 848 from a total of 274 pages. Of these: -- Number of word images from pages with ruled lines: 460, from 146 lined pages. -- The ratio of words and pages with ruled lines in the 34 PAW data set: 460/848 = 54.25% (word), 146/274=53.28% (pages).Recognition performance on words from lined pages: -- Top1: Earlier: 318/460 = 69.13% Now: 349/460 = 75.87% The ruled line removal improves the word recognition for top 1 by 6.74% (evaluated on words from lined pages). Overall improvement for top 1 is by 4.13% (evaluated using test set including all word images from lined or non-lined pages - which we had reported earlier).Also the PAW recognizer is a straightforward implementation using a k-nearest neighbor classifier. The features used are CUBS Gradient, Structure and ConcavityFeatures. The classifier is a very simple implementation that can be improved and its purpose was for testing the effectiveness of our features.
