DOCSLIB.ORG

Imaging and Artificial Intelligence: Intersection and Synergy

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Imaging and Artificial Intelligence: Intersection and Synergy IEEE Applied Imagery Pattern Recognition Workshop Dedicated to facilitating the interchange of ideas between government, industry, and academia 45th AIPR Workshop Imaging and Artificial Intelligence: Intersection and Synergy Cosmos Club, Washington, D.C. October 18-20, 2016 AIPR 2016 is sponsored by the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence with generous support from Cybernet Systems Corporation, Descartes Labs, Inc., and The Charles Stark Draper Laboratory, Inc. AIPR Workshop Committee Chairman Peter Doucette, U.S. Geological Survey Deputy Chair Daniela Moody, Los Alamos National Laboratory Program Chairs Murray H. Loew, George Washington University Kannappan Palaniappan, University of Missouri at Columbia Secretary Carlos Maraviglia, Naval Research Laboratory Treasurer Al Williams, Self-employed Engineer Local Arrangements Donald J. Gerson, Gerson Photography Publicity Peter Costianes, Air Force Research Laboratory, Emeritus Webmaster Charles J. Cohen, Cybernet External Support John Irvine, Draper Laboratory Christoph Borel-Donohue, Army Research Laboratory Registration Chair Jeff Kretsch, Raytheon BBN Technologies Proceedings Chair Franklin Tanner, Hadron Industries Student Paper Award Chair Paul Cerkez, DCS Corp. AIPR Committee Members: Jim Aanstoos, Mississippi State University Robert Mericsko, Booz Allen Hamilton Eamon Barrett, Retired Keith Monson, FBI Bob Bonneau, AFOSR Carsten Oertel, MITRE Filiz Bunyak, University of Missouri- William Oliver, Brody School of Medicine Columbia Kannappan Palaniappan, University of John Caulfield, Cyan Systems Industries Missouri-Columbia Charles Cohen, Cybernet James Pearson, Remote Sensing Consultant Peter Costiannes, AFRL, Emeritus Surya Prasath, University of Missouri-Columbia Paul Cerkez, DCS Corp. Amit Puntambekar, Intel Corporation Peter Doucette, U.S. Geological Survey Mike Pusateri, LSI Corporation Roger Gaborski, RIT Harvey Rhody, RIT Donald J Gerson, Gerson Photography David Schaefer, GMU Neelam Gupta, ARL Guna Seetharaman, NRL Mark Happel, Johns Hopkins University Karl Walli, AFIT John Irvine, Draper Lab Elmer "Al" Williams, Self-Employed Engineer Steve Israel, Integrity Applications/ONR Alan Schaum, NRL Michael D. Kelly, IKCS Jeff Kretsch, Raytheon BBN Technologies Emeritus Murray H. Loew, GWU Robert Haralick, City University of New York Carlos Maraviglia, NRL Joan Lurie, GCC Inc. Paul McCarley, AFRL Eglin AFB J. Michael Selander, MITRE 2016 AIPR Visionary Sponsors We are tackling the huge problem of teaching computers how to see. Our underlying technology, deep learning, models the way the human brain works. Though this was just science fiction even five years ago, advances in the speed of computers and the underlying software make it possible now. Further, because sensors can detect beyond the human visual spectrum, computers can see things that humans simply cannot. Our first project is building a living atlas of the world. We process petabytes of satellite imagery and extract daily insights that drive decision making in agriculture, finance, natural resources, public policy, and more. We work to create real-time global awareness to better understand changes over time and what is happening in our world right now. In the Press Deep-Learning, Image-Analysis Startup Descartes Labs Raises $3.3M After Spinning Out Of Los Alamos National Lab techcrunch.com – http://tcrn.ch/1bKgXIH After being incubated in a government lab for seven years, Descartes Labs — a deep-learning image analysis startup — has raised $3.3 million of its own. A Cloud-Free Satellite Map of Earth technologyreview.com – http://bit.ly/1p2yzXy Stitching together a live world map from many different satellite images lets algorithms keep an eye on the health of crops and problems like flooding. We’re Hiring! Our team includes cosmologists, physicists, mathematicians, engineers, computer scientists, and even a philosopher. We need people who are brilliant coders and stellar problem solvers - we welcome different backgrounds. We are headquartered in the fabulous mountains of Los Alamos, NM, and also have an office in San Francisco, CA. If this intrigues you, please reach out to us at [email protected]. We’d love to hear from you! 45th IEEE AIPR Workshop Program, October 2016 Tuesday October 18 Deep Learning & AI 8:00 AM Registration opens Note: For brevity, this schedule lists only the presenter of each paper; all authors and their affiliations are included with each abstract, indexed using the # in the schedule. Jason Matheny, Director, IARPA: Value of Machine Learning 9:00-9:45 AM for National Security AI and Image Processing I #20 Real-time detection and classification of traffic light signals; 9:45-10:05 AM Asaad Said, Intel #79 Machine vision algorithms for robust pallet engagement and 10:05-10:25 AM stacking; Charles Cohen, Cybernet Systems Corporation 10:25-10:40 AM Coffee Break Deep Learning for Image Processing I #6 SPARCNN: SPAtially Related Convolutional Neural Networks; 10:40-11:00 AM J.T. Turner, Knexus Research Corporation #47 Crossview convolutional networks; Nathan Jacobs, University of 11:00-11:20 AM Kentucky Trevor Darrell, EECS, University of California-Berkeley: 11:25-12:00 PM Perceptual representation learning across diverse modalities and domains 12:00-1:30 PM Lunch on your own Christopher Rigano, Office of Science and Technology, 1:30-2:15 PM National Institute of Justice: Applied Computer Science in Criminal Justice AI and Image Processing II #19 Trajectory based event classification from UAV videos and its 2:15-2:35 PM evaluation framework; Sung Chun Lee, Object Video, Inc. #56 Transfer learning for high resolution aerial image classification; 2:35-2:55 PM Yilong Liang, Rochester Institute of Technology #22 Visualization of high dimensional image features for 2:55-3:15 PM classification; Katie Rainey, SPAWAR Systems Center Pacific 3:15-3:30 PM Coffee Break 3:30-4:05 PM John Kaufhold, Deep Learning Analytics: Deep Learning Past, Present and Near Future Deep Learning for Image Processing II #60 Hierarchical temporal memory for visual pattern recognition; 4:05-4:25 PM Jianghao Shen, George Washington University #52 Hierarchical autoassociative polynomial network (hap net) for 4:25-4:45 PM robust face recognition; Theus Aspiras, University of Dayton #63 Convolutional neural network application to plant detection, 4:45-5:05 PM based on synthetic imagery; Larry Pearlstein, The College of New Jersey 5:10-5:30 PM Poster Preview 5:30-6:00 PM Cosmos Club Tour 6:00 PM Poster Session and Reception Tuesday Poster Session: Poster numbers, titles, and presenting author (full list of authors appears on the Abstract page) 2 CSANG: continuous scale anisotropic Gaussians for robust linear structure extraction; V. B. Surya Prasath, University of Missouri-Columbia 7 Seismic signal analysis using multi-scale/multi-resolution transformations; Millicent Thomas, Northwest University 9 Assessment of three-dimensional printing using visible light sensing and a CAD model; Jeremy Straub, University of North Dakota 17 Object identification for inventory management using convolutional neural network; Nishchal K. Verma, Indian Institute of Technology Kanpur, India 30 Multi-scale transformation based image super-resolution; Soundararajan Ezekiel, IUP 33 Keypoint density-based region proposal for fine-grained object detection and classification using regions with convolutional neural network features; JT Turner, Knexus Research Corporation 35 General purpose object counting algorithm using cascaded classifier and learning; Ricardo Fonseca, Universidad Tecnica Federico Santa Maria 44 Adding a selective multiscale and color feature to loft base line; Noor Al-Shakarji, University of Missouri Columbia 45 Auto-calibration of multi-projector systems on arbitrary shapes; Mahdi Abbaspour Tehrani, UC- Irvine 54 Boosted ringlet features for robust object tracking; Evan Krieger, University of Dayton 58 Video haze removal and Poisson blending based mini-mosaics for wide area motion imagery; Rumana Aktar, University of Missouri-Columbia 62 Deep Learning and Artificial Intelligence Completeness; Mihaela Quirk, Quirk & Quirk Consulting 66 Stream implementation of the flux tensor motion flow algorithm using Gstreamer and CUDA; Dardo Kleiner, CIPS, Corp. @ NRL 78 Enhanced GroupSAC: Efficient and Reliable RanSAC scheme in the presence of depth variation; Kyung-Min Han, University of Missouri Wednesday High-Performance Computing & Biomedical October 19 8:00 AM Registration opens Richard Linderman, SES, Office of the Assistant Secretary of Defense, 8:45-9:30 AM Research and Engineering: DoD Interests in Autonomy and Artificial Intelligence Scalable Image Processing #26 Scaling analytics for scientific images from experimental instruments; 9:30-9:50 AM Daniela Ushizima, Lawrence Berkeley National Laboratory #73 Computationally efficient scene categorization in complex dynamic 9:50-10:10 AM environments; Allison Mathis, Army Research Laboratory 10:10-10:30 #53 Uncertainty of image segmentation algorithms for forensic attribution of AM materials; Diane Oyen, Los Alamos National lab 10:30-10:45 Coffee Break AM Earth & Space Sensing Applications 10:45-11:05 #49 Sparse multi-image control for remotely sensed planetary imagery: the AM Apollo 15 metric camera; Jason Laura, United States Geological Survey 11:05-11:25 #57 On the utility of temporal data cubes for analyzing land dynamics; Peter AM Doucette, USGS 11:25-11:45 #14 Training and evaluating detection pipelines with connected components;
Load more

Recommended publications
  • LD5655.V856 1984.M843.Pdf (13.98Mb)
    Analyzing Perspective Line Drawings using Hypothesis Based Reasoning by Prasanna Govind Mulgaonkar Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of DOCTOROF PHILOSOPHY in Computer Science and Applications APPROVED: Linda G. Shapiro Robert M. Haralick J'ohn W. Roach Roger W. Ehrich Ezra A. Brown August, 1984 Blacksburg, Virginia ANALYSIS OF PERSPECTIVE LINE DRAWINGS USING HYPOTHESIS BASED REASONING by Prasanna Govind Mulgaonkar Committee Chairman: Dr. Linda G. Shapiro Computer Science (ABSTRACT) One of the important is~ues in the middle levels of com- puter vision is how world knowledge should be gradually in- serted into the reasoning process. In this dissertation, we develop a technique which uses hypothesis based reasoning to reason about perspective line drawings using only the const- raints supplied by the equations of perspective geometry. We show that the problem is NP complete, and that it can be solved using modular inference engines for propagating constraints over the set of world level entities. We also show that theorem proving techniques, with their attendant complexity, are not necessary because the real valued attri- butes of the world can be computed in closed form based only on the spatial relationships between world entities and mea- surements from the given image. ACKNOWLEDGEMENTS ...... For they were the best of times and they were the worst of times A dissertation is not created in isolation. It is the product of the interaction between the people surrounding the author, and the times and the places involved. I would like to thank everyone who in one way or the other contri- buted to making my time as a graduate student worthwhile and in some direct or indirect way helped make this dissertation what it is.
    [Show full text]
  • Scheme and Syllabus
    Scheme and Syllabus III – VI Semester Master of Computer Applications (MCA) 2015-2018 P.E.S. COLLEGE OF ENGINEERING Mandya - 571401. Karnataka (An Autonomous Institution under VTU Belagavi) Grant-in-aid Institution Accredited by NBA, New Delhi Approved by AICTE, New Delhi. Ph: 08232 - 220043 Fax: 08232 - 222075 Web: www.pescemandya.org P.E.S COLLEGE OF ENGINEERING, MANDYA – 571 401 (An Autonomous Institution Under VTU Belgaum) MASTER OF COMPUTER APPLICATIONS (MCA) SCHEME OF TEACHING AND EXAMINATION 2015-18 III SEMESTER Examination Sl. Hrs/Week Course Code Course Title Credits Marks No L :T : P : H CIE SEE Total 1 P15MCA31 Computer Networks 4 : 0 : 0 : 4 4 50 50 100 2 P15MCA32 Programming with Java 3 : 2 : 0 : 5 4 50 50 100 3 P15MCA33 Software Engineering 4 : 0 : 0 : 4 4 50 50 100 4 P15MCA34 Computer Graphics 3 : 2 : 0 : 5 4 50 50 100 5 P15MCA35X Elective - I 4 : 0 : 0 : 4 4 50 50 100 6 P15MCA36 Network Lab 0 : 0 : 2 : 2 1 50 50 100 7 P15MCA37 Java Programming Lab 0 : 0 : 3 : 3 1.5 50 50 100 8 P15MCA38 Computer Graphics Lab 0 : 0 : 3 : 3 1.5 50 50 100 Aptitude and Reasoning 9 P15MHU301 Development-BEGINNER 2 : 0 : 0 : 2 1 50 50 100 (ARDB)* Total 32 25 450 450 900 L: Lecture, T: Tutorial, P: Practical, H: Total Hours; CIE: Continuous Internal Evaluation; SEE: Semester End Examinations * Taught by Humanities Science and Management Department ELECTIVES Sl. Course code Elective Group - I No. 1 P15MCA351 Accountancy and Financial Management 2 P15MCA352 System Software 3 P15MCA353 Basics of MIS & E-Commerce 4 P15MCA354 Graph Theory and Combinatorics 5 P15MCA355 Business Data Processing Using COBOL SYLLABUS 2015 – 2018 Page 1 P.E.S COLLEGE OF ENGINEERING, MANDYA – 571 401 (An Autonomous Institution Under VTU Belgaum) MASTER OF COMPUTER APPLICATIONS (MCA) SCHEME OF TEACHING AND EXAMINATION 2015-18 IV SEMESTER Examination Hrs/Week Sl.
    [Show full text]
  • EECS 490 DIGITAL IMAGE PROCESSING December 2004
    EECS 490 DIGITAL IMAGE PROCESSING December 2004 SESSION 1 Document Deskewing Mike Adams Algorithm for Text Document De-Skewing Nattapon Chaimanonart Document Skew Detection Using the Hough Transformation Wen-Teng Chang Skew Correction for Text Document Using Fourier Domain Analysis Dmitriy Goldman Image De-Skewing Using Fourier Transform Issac Hirt Image Skew Detection and Correction using Morphology Ruchi Kothari Detection and Correction of Skew in Document Images Michael K. Lee Optical Character Recognition Preparation Using MATLAB Deng-Hung Liu Skew Correction for Document Images Using Gradient and Fourier Analysis Daniel Pendergast Multi-technique Algorithm for De-skewing and Text Bounding for OCR Applications Iouri Petriaev OCR Preparation: Design of an Algorithm for Document Analysis Using the Global Processing via Hough Transform Algorithm for Text Document De-Skewing Mike Adams Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, Email: [email protected] ABSTRACT 2) Threshold the Sobel result to create a binary im- This paper presents a method for the skew correction of age. text documents in digital image format, using primarily the 3) Perform morphological ‘closing’ and ‘thickening’ Hough Transform. The Hough Transform provides a good so text is thinned out and resembles many points estimate of line angles in an image and this can be extended on a line. The Hough Transform is more accurate, to lines of text. Positive angles between 0 and 90 degrees if it operates on clear lines in the document im- are estimated correctly in both test images while negative age[1]. A pre processed image is shown below.
    [Show full text]
  • Cb D0e0f Sut5thgvyx Cthgv V
    IMPROVEMENT OF ZONE CONTENT CLASSIFICATION BY USING BACKGROUND ANALYSIS ¡ Yalin Wang , Robert Haralick , and Ihsin T. Phillips ¢ Department of Electrical Engineering University of Washington Seattle, WA 98195 U.S.A. £ Department of Computer Science/Software Engineering Seattle University, Seattle, WA 98122 U.S.A. ¤ ylwang, haralick, [email protected] ¥ Abstract. This paper presents an improved zone content classification method. Motivated by our novel background-analysis-based table identification research, we added two new features to the feature vector from one previously published method [7]. The new features are the total area of large horizontal and large ver- tical blank blocks and the number of text glyphs in the zone. A binary decision tree is used to assign a zone class on the basis of its feature vector. The train- ing and testing data sets for the algorithm include ¦¨§ ©¨ images drawn from the UWCDROM-III document image database. The classifier is able to classify each given scientific and technical document zone into one of the nine classes, text ¦¨ classes (of font size ¦ pt and font size pt), math, table, halftone, map/drawing, ruling, logo, and others. The improved zone classification method ¨ ¨© ©¨ raised the accuracy rate to ¨ from and reduced the median false ¨ alarm rate to ¦ from . 1 Problem Statement " Let ! be a set of zone entities. Let be a set of content labels, such as text, table, * math, etc. The function #%$&!(')" associates each element of with a label. The ! / function +,$-!.'0/ specifies measurements
    [Show full text]
  • AIPR 2014: Democratization of Imagery Cosmos Club, Washington DC October 14-16, 2014
    1 Applied Imagery Pattern Recognition Workshop Dedicated to facilitating the interchange of ideas between government, industry and academia in an elegant setting conducive to technical interchange across a broad range of disciplines 2014 IEEE Applied Imagery Pattern Recognition Workshop AIPR 2014: Democratization of Imagery Cosmos Club, Washington DC October 14-16, 2014 AIPR 2014 is financially sponsored by the IEEE Computer Society Technical sponsorship and support is provided by IEEE and the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence The AIPR Committee thanks Cybernet Systems Corporation, Integrity Applications Incorporated and ObjectVideo Incorporated for their generous support of this year's workshop. 1 2 The AIPR Officers: Chairman: Neelam Gupta, ARL Deputy Chair: John Irvine, Draper Laboratory Program Chairs: Guna Seetharaman, AFRL/RI, Rome, NY Karl Walli, Col USAF Secretary: James Aanstoos, Mississippi State University Treasurer: Al Williams, Self Employed Engineering Consultant Associate Treasurer: Carlos Maraviglia, NRL Local Arrangements: Donald J. Gerson, Gerson Photography Publicity: Peter Costianes, AFRL, Emeritus Web Master: Charles J. Cohen, Cybernet External Support: Pete Doucette, Integrity Applications Incorporated Registration Chair: Jeff Kretsch, Raytheon BBN Technologies Proceedings Chair: Franklin Tanner, Hadron Industries Student Paper Award Murray Loew, GWU Chair: Paul Cerkez, DCS Corp. AIPR Committee Members: Jim Aanstoos, Mississippi State University Guna Seetharaman,
    [Show full text]
  • GC Bulletin 2020-21
    Bulletin 2020–21 Bulletin The Graduate Center The City University of New York 2020–21 Volume Forty-Three / NUMBER ONE 365 Fifth Avenue, New York, NY 10016-4309 www.gc.cuny.edu General: 1.212.817.7000 Admissions Office: 1.212.817.7470 (TDD users should call the New York Relay Center at 1.800.662.1220.) 2020–21 Bulletin, The Graduate Center, City University of New York NOTICE OF POSSIBLE CHANGES The City University of New York reserves the right, because of changing conditions, to make modifications of any nature in the academic programs and requirements of the University and its constituent colleges without advance notice. Tuition and fees set forth in this publication are similarly subject to change by the Board of Trustees of The City University of New York. The Uni- versity regrets any inconvenience this may cause. ACCREDITATION The City University of New York is registered by the New York State Department of Education: Office of Higher Education and the Professions, Cultural Education Center, Room 5B28, Albany, NY 12230; Telephone: 1.518.474.5851; http://www.nysed.gov/heds/IRPSL1.html. The Graduate Center has been accredited by the Commission on Higher Education of the Middle States As- sociation of Colleges and Schools since 1961, last reaffirmed in 2010. Seehttp://www.gc.cuny. edu/CUNY_GC/media/CUNY-Graduate-Center/PDF/Policies/General/Accreditation.pdf. 2 THE GRADUATE CENTER CALENDAR 2020–21 FALL 2020 July 31 (Fri.) .............................. Financial aid registration deadline. Last day for returning students to register full time in order to ensure fellowship payment during the first week of classes.
    [Show full text]
  • IAPR Newsletter, Pag. 34, ICPR 2016 Chalearn LAP
    THE INTERNATIONAL ASSOCIATION FOR PATTERN RECOGNITION www.iapr.org Volume 39, Number 1 NewsletterFebruary 2017 Special Issue with highlights of the 23rd International Conference on Pattern Recognition IssueIN THIS Letter from the President Letter from the President Simone Marinai CALLS for PAPERS It is an honor for me to have IAPR event, in particular the ICPR Calls from IAPR Committees been elected President of the (International Conference on Pattern IAPR. In the next two years, I Recognition), a unique opportunity. Getting to Know... will have the privilege of working It is non-trivial to recall that the “I” Petia Radeva, IAPR Fellow with an exceptional Executive in IAPR stands for International. Committee: Massimo Tistarelli (1st IAPR...The Next Generation: Within the IAPR, scholars from a Vice President), Edwin Hancock Sungmin Eum wide range of countries freely take (2nd Vice President), Alexandra ICPR Highlights: on different responsibilities: holding Branzan Albu (Secretary), Apostolos From the General Chair offices, serving on committees, Antonacopoulos (Treasurer), and Traditional Dance of Mexico organizing events that take place in the Past President Ingela Nyström. Coffee Break/Lunch for Women nearly every area of the world. This is a great team, and I am sure Plenary Talk Abstracts that we will do our best to serve our With the admission of two new Invited Talk Abstracts association. members (Pakistan and Vietnam) Workshops/Tutorials/Contests there are now 49 member societies 2016 IAPR Fellows The IAPR has been my scientific represented on the Governing ICPR 2016 Awards family since I first participated in an Board. And, in 2018, the IAPR will 2016 Governing Board Meeting IAPR conference in 1995.
    [Show full text]
  • Computer Vision and Remote Sensing – Lessons Learned
    Photogrammetric Week '09 Dieter Fritsch (Ed.) Wichmann Verlag, Heidelberg, 2009 Förstner 241 Computer Vision and Remote Sensing – Lessons Learned WOLFGANG FÖRSTNER, Bonn ABSTRACT Photogrammetry has significantly been influenced by its two neigbouring fields, namely Computer Vision and Remote Sensing. Today, Photogrammetry has been become a part of Remote Sensing. The paper reflects its growing relations with Computer Vision, based on a more than 25 years experience of the author with the fascinating field between cognitive, natural and engineering science, which stimulated his own research and transferred him into a wanderer between two worlds. “Two roads diverged in a wood, and I – I took the one less traveled by, and that has made all the difference.” Robert Frost 1. INTRODUCTION My first contact with a scientific community not being of geodetic nature was in 1983. Franz Leberl had organized the Specialist Workshop on Pattern Recognition in Photogrammetry in Graz (Leberl 1984) on behalf of the International Society of Photogrammtry and Remote Sensing by its Working Group III/5 Mathematical Pattern Recognition and Image Analysis. Well known photogram- metrists presented papers, such as Ed Mikhail or Ian Dowman. But also researches from a – that time – foreign world, such as Harlyn Baker or Robert Haralick, gave a flavour of techniques, not known in the core photogrammetric community, though adressing the same topics, specifically automatic surface reconstruction. The stimulating discussions with people from Pattern Recognition were the starting point for regular travels into and through a fascinating world around the own origin, geodesy and photogrammetry, which is still ongoing, full in the spirit of the workshop: “ ..
    [Show full text]
  • Piecewise-Smooth Optical Flow and Motion-Based Detection and Tracking
    Robust Visual Motion Analysis: Piecewise-Smooth Optical Flow and Motion-Based Detection and Tracking Ming Ye A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2002 Program Authorized to Offer Degree: Electrical Engineering University of Washington Abstract Robust Visual Motion Analysis: Piecewise-Smooth Optical Flow and Motion-Based Detection and Tracking by Ming Ye Co-Chairs of Supervisory Committee: Professor Robert M. Haralick Electrical Engineering Professor Linda G. Shapiro Computer Science and Engineering This thesis describes new approaches to optical flow estimation and motion-based detection and tracking. Statistical methods, particularly outlier rejection, error analysis and Bayesian inference, are extensively exploited in our study and are shown to be crucial to the robust analysis of visual motion. To recover optical flow, or 2D velocity fields, from image sequences, certain models of brightness conservation and flow smoothness must be assumed. Thus how to cope with model violations especially motion discontinuities becomes a very challenging issue. We first tackle this problem from a local approach, that is, finding the most representative flow vector for each small image region. We recast the popular gradient-based method as a two-stage regression problem and apply adaptive robust estimators to both stages. The estimators are adaptive in the sense that their complexity increases with the amount of outlier contamination. Due to the limited contextual information, the local approach has spatially varying uncertainty. We evaluate the uncertainty systematically through covari- ance propagation. Pointing out the limitations of local and gradient-based methods, we further propose a matching-based global optimization technique.
    [Show full text]
  • Graphics Recognition a Historical Perspective and Recent Advances Rangachar Kasturi
    Graphics Recognition A Historical Perspective and Recent Advances Rangachar Kasturi Computer Science and Engineering University of South Florida Tampa, Florida, USA The copyright for the illustrations and other objects used in this presentation are owned by the respective copyright holders; they are included here to acknowledge their contributions to the Document Analysis and Recognition research. Computer Vision and Pattern Recognition Laboratory IAPR/ICDAR Award Sargur Srihari, Nominator Lawrence O’Gorman, Endorser Gabriella Sanniti di Baja, Endorser Karl Tombre, Endorser Computer Vision and Pattern Recognition Laboratory IAPR/ICDAR Award Committee Dimosthenis Karatzas (Award Chair) Bart Lamiroy (Award Chair) Dan Lopresti Jean-Marc Ogier Josep Llados Simone Marinai Cheng-Lin Liu Marcus Liwicki Venu Govindaraju ICDAR 2017 Organizing Committee Koichi Kise and Team Computer Vision and Pattern Recognition Laboratory IAPR/ICDAR Award I would like to Dedicate this award to My 70+ Students Who Performed Much of the Research I am Recognized for Over the Past 35 Years Computer Vision and Pattern Recognition Laboratory Thank You Some of those who made it all possible Computer Vision and Pattern Recognition Laboratory Representing them all here is Sameer Antani, Ph.D. Sameer Antani: Video Content Characterization via Robust Recognition of Scene and Caption Text, Ph.D., 2001 Computer Vision and Pattern Recognition Laboratory IAPR Leaders, Freeman, Fu, Sakai, and Pavlidis at First Meeting of the IAPR Governing Board, Kyoto, 1978 Computer Vision and Pattern Recognition Laboratory What is Graphics Recognition? A Typical Document: Three Components Text Regions - Recognized by OCR Images - Processed by Image Processing Graphics- Analyzed by Graphics Recognition Graphics include Block Diagram, Chart, Graph, Line Art, Map, Schematic, Table, etc.
    [Show full text]