Computer Vision! Contents! Bildverarbeitung 1! ! Bernd Neumann! WS 2010/11!
|
|
|
- Suzan Boone
- 6 years ago
- Views:
Transcription
1 Computer Vision! Bildverarbeitung 1! ! Bernd Neumann! WS 2010/11! 1! Contents! IMAGE PROCESSING FOR MULTIMEDIA APPLICATIONS!! Introduction!! The digitized image and its properties!! Data structures for image analysis!! Image preprocessing!! Image compression! IMAGE ANALYSIS!! Segmentation!! Shape description!! Mathematical morphology!! Texture analysis!! Motion analysis! SEEING AND ACTING!! 3D image analysis!! Object recognition!! Scene analysis!! Knowledge-based scene interpretation!! Probabilistic scene interpretation! 2! 1!
2 Literature! Image Processing, Analysis and Machine Vision (3. Ed.)! M. Sonka, V. Hlavac, R. Boyle, Thomson 2008! Grundlagen der Bildverarbeitung! K.D. Tönnies, Pearson Studium, 2005! Computer Vision - A Modern Approach! D.A. Forsyth, J. Ponce, Prentice-Hall 2003! Digital Image Processing! R.C. Gonzalez, R.E. Woods, Prentice-Hall 2001! Digitale Bildverarbeitung! B. Jähne, Springer 1997! Computer Vision! R. Klette, A. Koschan, K. Schluns, Vieweg 1996! Computer and Robot Vision, Vol. I+II! R. Haralick, L.G. Shapiro, Addison-Wesley 1993! Robot Vision! B.K.P. Horn, MIT Press 1986! 3! Website! The website for this course can be reached via! You will find! -! a PDF copy of the slides! - the problem sheets for the exercise sessions! -! other information related to the course.! The website will be updated each week before the lectures.! 4! 2!
3 Exercises! Problem sheets related to the current lectures will be usually handed out every week.! Solutions - either as answer texts or program documentations - are due on ThursdayY the next week.! Solutions will be presented and discussed in class.! Active participation is a prerequisit for admission to the oral examination.! 5! Why Study Image Processing, Image Analysis and Image Understanding?! Subfield of Computer Science! History of more than 40 years! Rich methodology! Interesting interdisciplinary ties! Exciting insights into human vision! Important applications! Important information modality in the information age! 6! 3!
4 What is "Image Processing"?! Transforming images as a whole! "Bildverarbeitung" in a narrow sense! E.g. change of resolution, high pass filtering, noise removal! 512 columns x 574 rows! 32 columns x 35 rows! 7! What is "Image Analysis"?! Computing image components and their properties! "Bildanalyse"! E.g. edge finding, object localization, motion tracking! computation of displacement vectors! 8! 4!
5 What is "Image Understanding"?! Computing the meaning of images! "Bildverstehen"! E.g. object recognition, scene interpretation, vision and acting! "Ein heller Opel biegt von der Hartungstraße in die Schlüterstraße ein. Er wartet, bis ein Fußgänger die Hartungstraße überquert hat. Auf der Schlüterstraße steht ein heller Ford vor der Ampel an der Hartungstraße. Ein Fußgänger geht auf dem Gehweg rechts neben der Schlüterstraße in Richtung Hartungsstraße...."! 9! Image Understanding is Silent Movie Understanding! Buster Keaton! "The Navigator" (1924)! Silent movie understanding requires more than object recognition:! - common sense! -! emotionality! -! sense of humour! consequences for vision system architecture! 10! 5!
6 What is "Pattern Recognition"?! In the narrow sense: object classification based on feature vectors!! In the wide sense: similar to Image Analysis, but also applicable to other modalities! "Mustererkennung"! E.g. character recognition, crop classification, quality control! A x 1 = 4.2! x 2 = 2.7! "not A"! "A"! x = [ ]! "The unknown object is an A"! 11! What is "Computer Vision"?!! General term for the whole field, including Image Processing, Image Analysis, Image Understanding! Same as Machine Vision ("Maschinensehen")! Image Processing ("Bildverarbeitung") in the wide sense! Computer Vision! Computer Graphics! images! 12! 6!
7 Computer Vision vs. Biological Vision! Cognitive Science ("Kognitionswissenschaft") investigates vision in biological systems:! empirical models which adequately describe biological vision! describe vision as a computational system! Computer Vision aims at engineering solutions, but research is interested in biological vision:! Biological vision systems have solved problems not yet solved in!!! Computer Vision. They provide ideas for engineering solutions.! Technical requirements for vision systems often match requirements for! biological vision.! Caution: Mimicking biological vision does not necessarily provide the best solution for a technical problem.! 13! Geometry in Human Vision! Frasers Spiral! Zöllner s Deception! Poggendorf! 1860! Hering! 1861! Müller-Lyer! 1889! Delboeuf! 1892! Do we want a vision system to perceive like humans?! 14! 7!
8 Human Object Perception! Grouping preferences! Kanizsa s triangle! Camouflage! The dalmatian! 15! Human Character Recognition! 16! 8!
9 Human Face Recognition! Richard Nixon! Queen Victoria! Charlie Chaplin! Who is who?! Graucho Marx! John F. Kennedy! Winston Churchill! 17! Complexity of Natural Scenes! sky! clouds! water! buildings! vegetation! distances! reflections! shadows! occlusions! context! inferences! 18! 9!
10 The Computer Perspective on Images! 19! Greyvalues of the Section! 20! 10!
11 Street Scene Containing the Section! 21! Computer Vision as an Academic Discipline! Computer Vision is an active research field with many research groups in countries all over the world.! There exists a large body of research results to build on.! Studying Computer Vision is a prerequisite for! - the development of state-of-the-art applications! - corporate research! - an academic career! Recent developments of Cognitive Vision! - towards robust vision systems! - incorporating spatial and temporal context! -! beyond single object recognition! Bildverarbeitung 1 WS 2010! Advanced courses! 22! 11!
12 Important Conferences! ICCV! ECCV! ICPR! CVPR! ICIP! International Conference on Computer Vision! European Conference on Computer Vision! International Conference on Pattern Recognition! Conference on Computer Vision and Pattern recognition! International Conference on Image Processing! DAGM! Symposium der Deutschen Arbeitsgemeinschaft für Mustererkennung! Note: There are many regular conferences and workshops specialized on subtopics of Computer Vision, e.g. document analysis, aerial image analysis, robot vision, medical imagery! 23! Important Journals! IEEE-PAMI! IJPRAI! IVC! IJCV! CVGIP! MVA! PR! IEEE-IP! IEEE Transactions on Pattern Analysis and Machine Intelligence! International Journal of Pattern Recognition and Artificial Intelligence! Image and Vision Computing! International Journal of Computer Vision! Computer Vision, Graphics and Image Processing! Machine Vision and Applications! Pattern Recognition! IEEE Transactions on Image Processing! 24! 12!
13 Important Application Areas! Industrial image processing!! process control, quality control, geometrical measurements,...! Robotics!! assembly, navigation, cooperation, autonomous systems,...! Monitoring!! event recognition, safety systems, data collection, smart homes,...! Aerial image analysis!! GIS applications, ecological issues, defense,...! Document analysis!! handwritten character recognition, layout recognition, graphics recognition,...! Medical image analysis!! image enhancement, image registration, surgical support,...! Image retrieval!! image databases, multimodal information systems, web information retrieval,...! Virtual reality!! image generation, model construction! 25! Image Retrieval! Which of the stored images matches the example image?" 26! 13!
14 Example: Medical Image Analysis! classification of materials in tomographic images of the human head! 27! Example: Driver Assistance! Dickmanns 1996: Autonomous navigation on highways! 28! 14!
15 Example: Monitoring! Hongeng 2003: Criminal acts recognition! 29! History of Computer Vision (1)! A vision of Computer Vision!! Selfridge 1955:"... eyes and ears for the computer"! First image enhancement and image processing applications!! space missions, aerial image processing! Character recognition!! => pattern recognition paradigm! Blocksworld, restricted domains!! Roberts 1965: 2D => 3D! Natural scenes with motion!! Nagel 79: Digitization and analysis of traffic scenes! Visual agents!! Bajcsy 1988: Active Vision! 30! 15!
16 History of Computer Vision (2)! Visual driver assistance!! Dickmanns 1996: Autonomous navigation on highways! Recognizing faces!! Bülthoff 2002: Modelling faces for recognition! Motion tracking and event recognition!! Hongeng 2003: Criminal act recognition! 31! Research History at the Cognitive Systems Laboratory 1971! Image Sequence Analysis! 1980! 1990! 2000! 2010! Natural-language Description of Image Sequences! Model-based Scene Interpretation! Learning for Scene Interpretation! Real-time Scene Interpretation! Radiological Image Analysis! Image Registration! Aerial Image Analysis! Description Logics! RACER! Subpixel Segmentation! Topologypreserving Sampling! Manuscript Analysis! Applications of Knowledge-based! Systems! Ocean Current Analysis! Semantic Information Processing! 16!
17 Scene Interpretation! Recognizing structures in buildings (etrims)! Recognizing service activities (Co-Friend)! 3D Surface Reconstruction! 17!
18 Current Analysis! Day 1! Day 2! Displacement vectors! Computer Vision for Robotics (1)! Research for multimodal interactions of service robots in TAMS! Projects with Computer Vision topics:! International Graduate College "Cross-modal Interaction in Natural and Artificial Cognitive Systems" (CINACS)! Grasping with a anthropomorphic artificial hand (HANDLE)! Intelligent and precise vision systems for the support of service robots (IVUS)! 18!
19 Computer Vision for Robotics (2)! Monocular stereo with a moving camera! Omnivision Camera! Stereo Head-Eye System! Computer Vision at KOGS (1)! The "grand picture": Vision as part of a cognitive system! 38! 19!
Computer Vision. Bildverarbeitung. Ullrich Köthe Bernd Neumann SoSe 05. Contents
Computer Vision Bildverarbeitung Ullrich Köthe Bernd Neumann SoSe 05 1 Contents IMAGE PROCESSING FOR MULTIMEDIA APPLICATIONS Introduction The digitized image and its properties Data structures for image
Computer Vision. Bildverarbeitung Peer Stelldinger WS 2011/12. Contents
Computer Vision Bildverarbeitung 1 64-420 Peer Stelldinger WS 2011/12 1 Contents IMAGE PROCESSING FOR MULTIMEDIA APPLICATIONS Introduction The digitized image and its properties Data structures for image
Image Processing 2 (IP2) More Advanced Methods
MIN-Fakultät Fachbereich Informatik Arbeitsbereich BV Image Processing 2 (IP2) More Advanced Methods Lecture 1 Introduc;on Summer Semester 2015 Prof. Dr.- Ing. H. Siegfried S;ehl, BV Dr. Benjamin Seppke,
Digital image processing vs. computer vision Higher-level anchoring
Digital image processing vs. computer vision Higher-level anchoring Václav Hlaváč Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics Center for Machine Perception
Lecture 1 Introduction to Computer Vision. Lin ZHANG, PhD School of Software Engineering, Tongji University Spring 2014
Lecture 1 Introduction to Computer Vision Lin ZHANG, PhD School of Software Engineering, Tongji University Spring 2014 Course Info Contact Information Room 314, Jishi Building Email: cslinzhang@tongji.edu.cn
Lecture 1 Introduction to Computer Vision. Lin ZHANG, PhD School of Software Engineering, Tongji University Spring 2015
Lecture 1 Introduction to Computer Vision Lin ZHANG, PhD School of Software Engineering, Tongji University Spring 2015 Course Info Contact Information Room 314, Jishi Building Email: cslinzhang@tongji.edu.cn
Lecture 1 Introduction to Computer Vision. Lin ZHANG, PhD School of Software Engineering, Tongji University Spring 2018
Lecture 1 Introduction to Computer Vision Lin ZHANG, PhD School of Software Engineering, Tongji University Spring 2018 Course Info Contact Information Room 408L, Jishi Building Email: cslinzhang@tongji.edu.cn
Master Artificial Intelligence
Master Artificial Intelligence Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability to evaluate, analyze and interpret relevant
CS 534: Computer Vision
CS 534: Computer Vision Spring 2005 Ahmed Elgammal Dept of Computer Science Computer Vision Introduction - 1 Outlines Vision What and Why? Human vision Computer vision General computer vision applications
Computational and Biological Vision
Introduction to Computational and Biological Vision CS 202-1-5261 Computer Science Department, BGU Ohad Ben-Shahar Some necessary administrivia Lecturer : Ohad Ben-Shahar Email address : ben-shahar@cs.bgu.ac.il
Appendices master s degree programme Artificial Intelligence
Appendices master s degree programme Artificial Intelligence 2015-2016 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability
YUMI IWASHITA
YUMI IWASHITA yumi@ieee.org http://robotics.ait.kyushu-u.ac.jp/~yumi/index-e.html RESEARCH INTERESTS Computer vision for robotics applications, such as motion capture system using multiple cameras and
ELE 882: Introduction to Digital Image Processing (DIP)
ELE882 Introduction to Digital Image Processing Course Instructor: Prof. Ling Guan Department of Electrical & Computer Engineering Room 315, ENG Building Tel: (416)979-5000 ext 6072 Email: lguan@ee.ryerson.ca
MSc(CompSc) List of courses offered in
Office of the MSc Programme in Computer Science Department of Computer Science The University of Hong Kong Pokfulam Road, Hong Kong. Tel: (+852) 3917 1828 Fax: (+852) 2547 4442 Email: msccs@cs.hku.hk (The
Urban Feature Classification Technique from RGB Data using Sequential Methods
Urban Feature Classification Technique from RGB Data using Sequential Methods Hassan Elhifnawy Civil Engineering Department Military Technical College Cairo, Egypt Abstract- This research produces a fully
Spring 2018 CS543 / ECE549 Computer Vision. Course webpage URL:
Spring 2018 CS543 / ECE549 Computer Vision Course webpage URL: http://slazebni.cs.illinois.edu/spring18/ The goal of computer vision To extract meaning from pixels What we see What a computer sees Source:
Today I t n d ro ucti tion to computer vision Course overview Course requirements
COMP 776: Computer Vision Today Introduction ti to computer vision i Course overview Course requirements The goal of computer vision To extract t meaning from pixels What we see What a computer sees Source:
This list supersedes the one published in the November 2002 issue of CR.
PERIODICALS RECEIVED This is the current list of periodicals received for review in Reviews. International standard serial numbers (ISSNs) are provided to facilitate obtaining copies of articles or subscriptions.
To be published by IGI Global: For release in the Advances in Computational Intelligence and Robotics (ACIR) Book Series
CALL FOR CHAPTER PROPOSALS Proposal Submission Deadline: September 15, 2014 Emerging Technologies in Intelligent Applications for Image and Video Processing A book edited by Dr. V. Santhi (VIT University,
DIGITAL IMAGE PROCESSING
DIGITAL IMAGE PROCESSING Lecture 1 Introduction Tammy Riklin Raviv Electrical and Computer Engineering Ben-Gurion University of the Negev 2 Introduction to Digital Image Processing Lecturer: Dr. Tammy
Digital Photogrammetry. Presented by: Dr. Hamid Ebadi
Digital Photogrammetry Presented by: Dr. Hamid Ebadi Background First Generation Analog Photogrammetry Analytical Photogrammetry Digital Photogrammetry Photogrammetric Generations 2000 digital photogrammetry
Vision System for a Robot Guide System
Vision System for a Robot Guide System Yu Wua Wong 1, Liqiong Tang 2, Donald Bailey 1 1 Institute of Information Sciences and Technology, 2 Institute of Technology and Engineering Massey University, Palmerston
Introduction. Visual data acquisition devices. The goal of computer vision. The goal of computer vision. Vision as measurement device
Spring 15 CIS 5543 Computer Vision Visual data acquisition devices Introduction Haibin Ling http://www.dabi.temple.edu/~hbling/teaching/15s_5543/index.html Revised from S. Lazebnik The goal of computer
preface Motivation Figure 1. Reality-virtuality continuum (Milgram & Kishino, 1994) Mixed.Reality Augmented. Virtuality Real...
v preface Motivation Augmented reality (AR) research aims to develop technologies that allow the real-time fusion of computer-generated digital content with the real world. Unlike virtual reality (VR)
- Basics of informatics - Computer network - Software engineering - Intelligent media processing - Human interface. Professor. Professor.
- Basics of informatics - Computer network - Software engineering - Intelligent media processing - Human interface Computer-Aided Engineering Research of power/signal integrity analysis and EMC design
Background Pixel Classification for Motion Detection in Video Image Sequences
Background Pixel Classification for Motion Detection in Video Image Sequences P. Gil-Jiménez, S. Maldonado-Bascón, R. Gil-Pita, and H. Gómez-Moreno Dpto. de Teoría de la señal y Comunicaciones. Universidad
Changjiang Yang. Computer Vision, Pattern Recognition, Machine Learning, Robotics, and Scientific Computing.
Changjiang Yang Mailing Address: Department of Computer Science University of Maryland College Park, MD 20742 Lab Phone: (301)405-8366 Cell Phone: (410)299-9081 Fax: (301)314-9658 Email: yangcj@cs.umd.edu
Digital image processing. Árpád BARSI BME Dept. Photogrammetry and Geoinformatics
Digital image processing Árpád BARSI BME Dept. Photogrammetry and Geoinformatics barsi.arpad@epito.bme.hu Part 1: (5/12/) Theory of image processing Part 2: (12/12/) Practice with software examples Main
Prof Trivedi ECE253A Notes for Students only
ECE 253A: Digital Processing: Course Related Class Website: https://sites.google.com/a/eng.ucsd.edu/ece253fall2017/ Course Graduate Assistants: Nachiket Deo Borhan Vasili Kirill Pirozenko Piazza Grading:
Digital Image Processing. Lecture 1 (Introduction) Bu-Ali Sina University Computer Engineering Dep. Fall 2011
Digital Processing Lecture 1 (Introduction) Bu-Ali Sina University Computer Engineering Dep. Fall 2011 Introduction One picture is worth more than ten thousand p words Outline Syllabus References Course
Computer Assisted Image Analysis 1 GW 1, Filip Malmberg Centre for Image Analysis Deptartment of Information Technology Uppsala University
Computer Assisted Image Analysis 1 GW 1, 2.1-2.4 Filip Malmberg Centre for Image Analysis Deptartment of Information Technology Uppsala University 2 Course Overview 9+1 lectures (Filip, Damian) 5 computer
4D-Particle filter localization for a simulated UAV
4D-Particle filter localization for a simulated UAV Anna Chiara Bellini annachiara.bellini@gmail.com Abstract. Particle filters are a mathematical method that can be used to build a belief about the location
IMAGE PROCESSING PAPER PRESENTATION ON IMAGE PROCESSING
IMAGE PROCESSING PAPER PRESENTATION ON IMAGE PROCESSING PRESENTED BY S PRADEEP K SUNIL KUMAR III BTECH-II SEM, III BTECH-II SEM, C.S.E. C.S.E. pradeep585singana@gmail.com sunilkumar5b9@gmail.com CONTACT:
UNIVERSITY OF REGINA FACULTY OF ENGINEERING. TIME TABLE: Once every two weeks (tentatively), every other Friday from pm
1 UNIVERSITY OF REGINA FACULTY OF ENGINEERING COURSE NO: ENIN 880AL - 030 - Fall 2002 COURSE TITLE: Introduction to Intelligent Robotics CREDIT HOURS: 3 INSTRUCTOR: Dr. Rene V. Mayorga ED 427; Tel: 585-4726,
23270: AUGMENTED REALITY FOR NAVIGATION AND INFORMATIONAL ADAS. Sergii Bykov Technical Lead Machine Learning 12 Oct 2017
23270: AUGMENTED REALITY FOR NAVIGATION AND INFORMATIONAL ADAS Sergii Bykov Technical Lead Machine Learning 12 Oct 2017 Product Vision Company Introduction Apostera GmbH with headquarter in Munich, was
Appendices master s degree programme Human Machine Communication
Appendices master s degree programme Human Machine Communication 2015-2016 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability
International Journal of Advanced Research in Computer Science and Software Engineering
Volume 3, Issue 4, April 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Novel Approach
Affiliate researcher, Robotics Section, Jet Propulsion Laboratory, USA
Prof YUMI IWASHITA, PhD 744 Motooka Nishi-ku Fukuoka Japan Kyushu University +81-90-9489-6287 (cell) yumi@ieee.org http://robotics.ait.kyushu-u.ac.jp/~yumi RESEARCH EXPERTISE Computer vision for robotics
Introduction. Ioannis Rekleitis
Introduction Ioannis Rekleitis Why Image Processing? Who here has a camera? How many cameras do you have Point where computers fast/cheap Cameras become omnipresent Deep Learning CSCE 590: Introduction
The Nature of Informatics
The Nature of Informatics Alan Bundy University of Edinburgh 19-Sep-11 1 What is Informatics? The study of the structure, behaviour, and interactions of both natural and artificial computational systems.
Proposers Day Workshop
Proposers Day Workshop Monday, January 23, 2017 @srcjump, #JUMPpdw Cognitive Computing Vertical Research Center Mandy Pant Academic Research Director Intel Corporation Center Motivation Today s deep learning
Computer Vision Introduction
Computer Vision Introduction Ahmed Elgammal Dept of Computer Science Rutgers University Outlines Vision What and Why? Human vision Computer vision General computer vision applications Course Outlines Administrative
CSCE 763: Digital Image Processing
CSCE 763: Digital Image Processing Spring 2018 Yan Tong Department of Computer Science and Engineering University of South Carolina Today s Agenda Welcome Tentative Syllabus Topics covered in the course
The Use of Neural Network to Recognize the Parts of the Computer Motherboard
Journal of Computer Sciences 1 (4 ): 477-481, 2005 ISSN 1549-3636 Science Publications, 2005 The Use of Neural Network to Recognize the Parts of the Computer Motherboard Abbas M. Ali, S.D.Gore and Musaab
Face Detection System on Ada boost Algorithm Using Haar Classifiers
Vol.2, Issue.6, Nov-Dec. 2012 pp-3996-4000 ISSN: 2249-6645 Face Detection System on Ada boost Algorithm Using Haar Classifiers M. Gopi Krishna, A. Srinivasulu, Prof (Dr.) T.K.Basak 1, 2 Department of Electronics
Cognitive robots and emotional intelligence Cloud robotics Ethical, legal and social issues of robotic Construction robots Human activities in many
Preface The jubilee 25th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2016 was held in the conference centre of the Best Western Hotel M, Belgrade, Serbia, from 30 June to 2 July
Appendices Master's Degree Programme Artificial Intelligence
faculty of science and engineering Appendices Master's Degree Programme Artificial Intelligence 2017-2018 Appendix I Learning Outcomes of the Degree Programme (Article 1.3) 1. The master demonstrates knowledge,
Ant? Bird? Dog? Human -SURE
ECE 172A: Intelligent Systems: Introduction Week 1 (October 1, 2007): Course Introduction and Announcements Intelligent Robots as Intelligent Systems A systems perspective of Intelligent Robots and capabilities
Behaviour-Based Control. IAR Lecture 5 Barbara Webb
Behaviour-Based Control IAR Lecture 5 Barbara Webb Traditional sense-plan-act approach suggests a vertical (serial) task decomposition Sensors Actuators perception modelling planning task execution motor
CS 131 Lecture 1: Course introduction
CS 131 Lecture 1: Course introduction Olivier Moindrot Department of Computer Science Stanford University Stanford, CA 94305 olivierm@stanford.edu 1 What is computer vision? 1.1 Definition Two definitions
Perceptual Interfaces. Matthew Turk s (UCSB) and George G. Robertson s (Microsoft Research) slides on perceptual p interfaces
Perceptual Interfaces Adapted from Matthew Turk s (UCSB) and George G. Robertson s (Microsoft Research) slides on perceptual p interfaces Outline Why Perceptual Interfaces? Multimodal interfaces Vision
Improving Image Quality by Camera Signal Adaptation to Lighting Conditions
Improving Image Quality by Camera Signal Adaptation to Lighting Conditions Mihai Negru and Sergiu Nedevschi Technical University of Cluj-Napoca, Computer Science Department Mihai.Negru@cs.utcluj.ro, Sergiu.Nedevschi@cs.utcluj.ro
AN AUTONOMOUS SIMULATION BASED SYSTEM FOR ROBOTIC SERVICES IN PARTIALLY KNOWN ENVIRONMENTS
AN AUTONOMOUS SIMULATION BASED SYSTEM FOR ROBOTIC SERVICES IN PARTIALLY KNOWN ENVIRONMENTS Eva Cipi, PhD in Computer Engineering University of Vlora, Albania Abstract This paper is focused on presenting
CSE 473/573 Computer Vision and Image Processing (CVIP) Ifeoma Nwogu
CSE 473/573 Computer Vision and Image Processing (CVIP) Ifeoma Nwogu inwogu@buffalo.edu Today Logistics Schedule Introductions What is computer vision? Why is vision so hard? Prerequisites This course
School of Computer Science. Course Title: Introduction to Human-Computer Interaction Date: 8/16/11
Course Title: Introduction to Human-Computer Interaction Date: 8/16/11 Course Number: CEN-371 Number of Credits: 3 Subject Area: Computer Systems Subject Area Coordinator: Christine Lisetti email: lisetti@cis.fiu.edu
AI MAGAZINE AMER ASSOC ARTIFICIAL INTELL UNITED STATES English ANNALS OF MATHEMATICS AND ARTIFICIAL
Title Publisher ISSN Country Language ACM Transactions on Autonomous and Adaptive Systems ASSOC COMPUTING MACHINERY 1556-4665 UNITED STATES English ACM Transactions on Intelligent Systems and Technology
3D and Sequential Representations of Spatial Relationships among Photos
3D and Sequential Representations of Spatial Relationships among Photos Mahoro Anabuki Canon Development Americas, Inc. E15-349, 20 Ames Street Cambridge, MA 02139 USA mahoro@media.mit.edu Hiroshi Ishii
On the WEB. Digital Image Processing ECE 178. B. S. MANJUNATH RM 3157 ENGR I Tel:
Digital Image Processing ECE 178 B. S. MANJUNATH RM 3157 ENGR I Tel:893-7112 manj@ece.ucsb.edu http://vision.ece.ucsb.edu Introduction 1 On the WEB For course information: http://www.ece.ucsb.edu/~manj/ece178
Advances in Human!!!!! Computer Interaction
Advances in Human!!!!! Computer Interaction Seminar WS 07/08 - AI Group, Chair Prof. Wahlster Patrick Gebhard gebhard@dfki.de Michael Kipp kipp@dfki.de Martin Rumpler rumpler@dfki.de Michael Schmitz schmitz@cs.uni-sb.de
Norbert Kruger John Hallam. The Mærsk Mc-Kinney Møller Institute University of Southern Denmark
Norbert Kruger John Hallam The Mærsk Mc-Kinney Møller Institute University of Southern Denmark www.mmmi.sdu.dk 08-05-2010 The Maersk McKinney Moller Institute 1 1. Motivation: Biologically inspired design
Journal Title ISSN 5. MIS QUARTERLY BRIEFINGS IN BIOINFORMATICS
List of Journals with impact factors Date retrieved: 1 August 2009 Journal Title ISSN Impact Factor 5-Year Impact Factor 1. ACM SURVEYS 0360-0300 9.920 14.672 2. VLDB JOURNAL 1066-8888 6.800 9.164 3. IEEE
CS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 22: Computational photography photomatix.com Announcements Final project midterm reports due on Tuesday to CMS by 11:59pm BRDF s can be incredibly complicated
Introduction. BIL719 Computer Vision Pinar Duygulu Hacettepe University
Introduction BIL719 Computer Vision Pinar Duygulu Hacettepe University Basic Info Textbooks (suggested): Forsyth & Ponce, Computer Vision: A Modern Approach Richard Szeliski, Computer Vision: Algorithms
EARIN Jarosław Arabas Room #223, Electronics Bldg.
EARIN http://elektron.elka.pw.edu.pl/~jarabas/earin.html Jarosław Arabas jarabas@elka.pw.edu.pl Room #223, Electronics Bldg. Paweł Cichosz pcichosz@elka.pw.edu.pl Room #215, Electronics Bldg. EARIN Jarosław
Associated Emotion and its Expression in an Entertainment Robot QRIO
Associated Emotion and its Expression in an Entertainment Robot QRIO Fumihide Tanaka 1. Kuniaki Noda 1. Tsutomu Sawada 2. Masahiro Fujita 1.2. 1. Life Dynamics Laboratory Preparatory Office, Sony Corporation,
Rm 211, Department of Mathematics & Statistics Phone: (806) Texas Tech University, Lubbock, TX Fax: (806)
Jingyong Su Contact Information Research Interests Education Rm 211, Department of Mathematics & Statistics Phone: (806) 834-4740 Texas Tech University, Lubbock, TX 79409 Fax: (806) 472-1112 Personal Webpage:
RESEARCH PAPER FOR ARBITRARY ORIENTED TEAM TEXT DETECTION IN VIDEO IMAGES USING CONNECTED COMPONENT ANALYSIS
International Journal of Latest Trends in Engineering and Technology Vol.(7)Issue(4), pp.137-141 DOI: http://dx.doi.org/10.21172/1.74.018 e-issn:2278-621x RESEARCH PAPER FOR ARBITRARY ORIENTED TEAM TEXT
CS6550 Computer Vision
CS6550 Computer Vision Class Meeting: M7M8 (3:30pm 5:20pm), R6 (2:20pm 3:10pm). Rm 106 Delta Bldg., 台達館 106 室 Instructor: Prof. Shang-Hong Lai, Rm. 636 Delta Bldg., 賴尚宏, 台達館 636 室, Tel: ext. 42958, Email:
Content Based Image Retrieval Using Color Histogram
Content Based Image Retrieval Using Color Histogram Nitin Jain Assistant Professor, Lokmanya Tilak College of Engineering, Navi Mumbai, India. Dr. S. S. Salankar Professor, G.H. Raisoni College of Engineering,
Prospective Teleautonomy For EOD Operations
Perception and task guidance Perceived world model & intent Prospective Teleautonomy For EOD Operations Prof. Seth Teller Electrical Engineering and Computer Science Department Computer Science and Artificial
Computer Vision Lecture 1
Computer Vision Lecture 1 Introduction 19.10.2016 Bastian Leibe Visual Computing Institute RWTH Aachen University http://www.vision.rwth-aachen.de/ leibe@vision.rwth-aachen.de Organization Lecturer Prof.
Social Editing of Video Recordings of Lectures
Social Editing of Video Recordings of Lectures Margarita Esponda-Argüero esponda@inf.fu-berlin.de Benjamin Jankovic jankovic@inf.fu-berlin.de Institut für Informatik Freie Universität Berlin Takustr. 9
Introduction To Cognitive Robots
Introduction To Cognitive Robots Prof. Brian Williams Rm 33-418 Wednesday, February 2 nd, 2004 Outline Examples of Robots as Explorers Course Objectives Student Introductions and Goals Introduction to
Introduction to Vision & Robotics
Introduction to Vision & Robotics Vittorio Ferrari, 650-2697,IF 1.27 vferrari@staffmail.inf.ed.ac.uk Michael Herrmann, 651-7177, IF1.42 mherrman@inf.ed.ac.uk Lectures: Handouts will be on the web (but
Andy Zeng 35 Olden Street Princeton NJ cs.princeton.edu/~andyz
Andy Zeng 35 Olden Street Princeton NJ 08540 andyz@princeton.edu cs.princeton.edu/~andyz Education Princeton University, Princeton NJ PhD, Department of Computer Science Advisor: Thomas Funkhouser Princeton
Chapter 1: Introduction to Neuro-Fuzzy (NF) and Soft Computing (SC)
Chapter 1: Introduction to Neuro-Fuzzy (NF) and Soft Computing (SC) Introduction (1.1) SC Constituants and Conventional Artificial Intelligence (AI) (1.2) NF and SC Characteristics (1.3) Jyh-Shing Roger
A Very High Level Interface to Teleoperate a Robot via Web including Augmented Reality
A Very High Level Interface to Teleoperate a Robot via Web including Augmented Reality R. Marín, P. J. Sanz and J. S. Sánchez Abstract The system consists of a multirobot architecture that gives access
CIS 849: Autonomous Robot Vision
CIS 849: Autonomous Robot Vision Instructor: Christopher Rasmussen Course web page: www.cis.udel.edu/~cer/arv September 5, 2002 Purpose of this Course To provide an introduction to the uses of visual sensing
ENHANCED HUMAN-AGENT INTERACTION: AUGMENTING INTERACTION MODELS WITH EMBODIED AGENTS BY SERAFIN BENTO. MASTER OF SCIENCE in INFORMATION SYSTEMS
BY SERAFIN BENTO MASTER OF SCIENCE in INFORMATION SYSTEMS Edmonton, Alberta September, 2015 ABSTRACT The popularity of software agents demands for more comprehensive HAI design processes. The outcome of
FP7 ICT Call 6: Cognitive Systems and Robotics
FP7 ICT Call 6: Cognitive Systems and Robotics Information day Luxembourg, January 14, 2010 Libor Král, Head of Unit Unit E5 - Cognitive Systems, Interaction, Robotics DG Information Society and Media
Changyin Zhou. Ph.D, Computer Science, Columbia University Oct 2012
Changyin Zhou Software Engineer at Google X Google Inc. 1600 Amphitheater Parkway, Mountain View, CA 94043 E-mail: changyin@google.com URL: http://www.changyin.org Office: (917) 209-9110 Mobile: (646)
Computer Vision in Human-Computer Interaction
Invited talk in 2010 Autumn Seminar and Meeting of Pattern Recognition Society of Finland, M/S Baltic Princess, 26.11.2010 Computer Vision in Human-Computer Interaction Matti Pietikäinen Machine Vision
Course Descriptions / Graphic Design
Course Descriptions / Graphic Design ADE 1101 - History & Theory for Art & Design 1 The course teaches art, architecture, graphic and interior design, and how they develop from antiquity to the late nineteenth
Introduction to Vision & Robotics
Introduction to Vision & Robotics Lecturers: Tim Hospedales 50-4450, IF 1.10 t.hospedales@ed.ac.uk Michael Herrmann 51-7177, IF 1.42 michael.herrmann@ed.ac.uk Lectures (Mon and Thr 9:00 9:50) are available
The Future of AI A Robotics Perspective
The Future of AI A Robotics Perspective Wolfram Burgard Autonomous Intelligent Systems Department of Computer Science University of Freiburg Germany The Future of AI My Robotics Perspective Wolfram Burgard
Unit 1: Introduction to Autonomous Robotics
Unit 1: Introduction to Autonomous Robotics Computer Science 4766/6778 Department of Computer Science Memorial University of Newfoundland January 16, 2009 COMP 4766/6778 (MUN) Course Introduction January
INTERACTIVE SKETCHING OF THE URBAN-ARCHITECTURAL SPATIAL DRAFT Peter Kardoš Slovak University of Technology in Bratislava
INTERACTIVE SKETCHING OF THE URBAN-ARCHITECTURAL SPATIAL DRAFT Peter Kardoš Slovak University of Technology in Bratislava Abstract The recent innovative information technologies and the new possibilities
Data-Starved Artificial Intelligence
Data-Starved Artificial Intelligence Data-Starved Artificial Intelligence This material is based upon work supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract
Ninad Thakoor 3460 Florida St Riverside, California, Ph
Ninad Thakoor 3460 Florida St Riverside, California, 92507 Email: ninadt@ucr.edu Ph. 682-472-7685 Research Interests: Computer Vision, Pattern Recognition, Real time systems, Image Processing Education:
Background. Computer Vision & Digital Image Processing. Improved Bartlane transmitted image. Example Bartlane transmitted image
Background Computer Vision & Digital Image Processing Introduction to Digital Image Processing Interest comes from two primary backgrounds Improvement of pictorial information for human perception How
Computer Vision, Computer Graphics, Machine Learning
Curriculum Vitae NAME: Rei Kawakami BIRTH DATE: February, 15th, 1980 AFFILIATION: Assistant Professor, University of Tokyo SPECIALIZATION: Computer Vision, Computer Graphics, Machine Learning LANGUAGE:
Context-Aware Interaction in a Mobile Environment
Context-Aware Interaction in a Mobile Environment Daniela Fogli 1, Fabio Pittarello 2, Augusto Celentano 2, and Piero Mussio 1 1 Università degli Studi di Brescia, Dipartimento di Elettronica per l'automazione
COMP 776: Computer Vision
COMP 776: Computer Vision Basic Info Instructor: Svetlana Lazebnik (lazebnik@cs.unc.edu) Office hours: By appointment, FB 244 Textbook (recommended): Forsyth & Ponce, Computer Vision: A Modern Approach
Efficient Gesture Interpretation for Gesture-based Human-Service Robot Interaction
Efficient Gesture Interpretation for Gesture-based Human-Service Robot Interaction D. Guo, X. M. Yin, Y. Jin and M. Xie School of Mechanical and Production Engineering Nanyang Technological University
CS594, Section 30682:
CS594, Section 30682: Distributed Intelligence in Autonomous Robotics Spring 2003 Tuesday/Thursday 11:10 12:25 http://www.cs.utk.edu/~parker/courses/cs594-spring03 Instructor: Dr. Lynne E. Parker ½ TA:
Towards affordance based human-system interaction based on cyber-physical systems
Towards affordance based human-system interaction based on cyber-physical systems Zoltán Rusák 1, Imre Horváth 1, Yuemin Hou 2, Ji Lihong 2 1 Faculty of Industrial Design Engineering, Delft University
CSE 408 Multimedia Information System
CSE 408 Multimedia Information System Intro to Images & Vision Yezhou Yang Lots of slides from Tamara Berg and L. Feifei Intro to Computer Vision Source: L. Lazebnik The goal of computer vision To perceive
Lecture 19: Depth Cameras. Kayvon Fatahalian CMU : Graphics and Imaging Architectures (Fall 2011)
Lecture 19: Depth Cameras Kayvon Fatahalian CMU 15-869: Graphics and Imaging Architectures (Fall 2011) Continuing theme: computational photography Cheap cameras capture light, extensive processing produces
Digital Image Processing. By Rafael C. Gonzalez
Digital Image Processing. By Rafael C. Gonzalez Digital image processing medical applications Biomedical - Designed for advanced undergraduates and graduate students who will become endusers of digital
Robust Hand Gesture Recognition for Robotic Hand Control
Robust Hand Gesture Recognition for Robotic Hand Control Ankit Chaudhary Robust Hand Gesture Recognition for Robotic Hand Control 123 Ankit Chaudhary Department of Computer Science Northwest Missouri State