1 Image Recognition has a wide variety of cool apps Robotic control Robotic control Quality control of manufactured parts Quality control of manufactured.

Slides:



Advertisements
Similar presentations
Hand Gesture for Taking Self Portrait Shaowei Chu and Jiro Tanaka University of Tsukuba Japan 12th July 15 minutes talk.
Advertisements

CSSE463: Image Recognition Day 20 Announcements: Announcements: Sunset detector due Weds. 11:59 Sunset detector due Weds. 11:59 Literature reviews due.
Visual Event Detection & Recognition Filiz Bunyak Ersoy, Ph.D. student Smart Engineering Systems Lab.
A Mobile-Cloud Pedestrian Crossing Guide for the Blind
CPSC 425: Computer Vision (Jan-April 2007) David Lowe Prerequisites: 4 th year ability in CPSC Math 200 (Calculus III) Math 221 (Matrix Algebra: linear.
Video Enhancement & RFID Ecosystem. Video Enhancement  Most video cameras = low quality  Most digital cameras = high quality  The video enhancement:
1 Introduction What IS computer vision? Where do images come from? the analysis of digital images by a computer.
Group-1 Group members- Sadbodh sharma-y07uc101 Kapil Phatnani-y08uc065.
Sachin Chopra Trevor Garson Madhuri Rapaka Introduction Tool to Tag all your Photographs within minutes 2 – Pass Processing Face Detection – Run the.
Computer Vision. DARPA Challenge Seeks Robots To Drive Into Disasters. DARPA's Robotics Challenge offers a $2 million prize if you can build a robot capable.
Lecture #32 WWW Search. Review: Data Organization Kinds of things to organize –Menu items –Text –Images –Sound –Videos –Records (I.e. a person ’ s name,
Capture your favorite image Done by: ms.Hanan Albarigi.
Alternative Input Devices Part B There will be a test on this information (both part a & b).
Mi-Gyeong Gwak, Christian Vargas, Jonathan Vinson
Machine Learning An Introduction. What is Learning?  Herbert Simon: “Learning is any process by which a system improves performance from experience.”
Edit the text with your own short phrases. The animation is already done for you; just copy and paste the slide into your existing presentation. Face Detection,
CSCE 5013 Computer Vision Fall 2011 Prof. John Gauch
Compiled By: Raj G Tiwari.  A pattern is an object, process or event that can be given a name.  A pattern class (or category) is a set of patterns sharing.
Digital Image Processing Lecture notes – fall 2008 Lecturer: Conf. dr. ing. Mihaela GORDAN Communications Department
1 Image Recognition has a wide variety of cool apps Robotic control Robotic control Quality control of manufactured parts Quality control of manufactured.
Computer Science Department Pacific University Artificial Intelligence -- Computer Vision.
DIEGO AGUIRRE COMPUTER VISION INTRODUCTION 1. QUESTION What is Computer Vision? 2.
MACHINE VISION Machine Vision System Components ENT 273 Ms. HEMA C.R. Lecture 1.
WEB 2.0 Uses in a classroom. Web 2.0 second stage of development of the World Wide Web characterized especially by the change from static web pages to.
Beyond the PC Kiosks & Handhelds Albert Huang Larry Rudolph Oxygen Research Group MIT CSAIL.
Efficient Visual Object Tracking with Online Nearest Neighbor Classifier Many slides adapt from Steve Gu.
Transforming video & photo collections into valuable resources John Waugaman President - Tygart Technology, Inc.
Team Members Ming-Chun Chang Lungisa Matshoba Steven Preston Supervisors Dr James Gain Dr Patrick Marais.
Final Year Project Vision based biometric authentication system By Padraic ó hIarnain.
CSSE463: Image Recognition Matt Boutell Myers240C x8534
Mapping Technology and Topographic Maps Chapter 1, Lesson 3 and 4.
Introduction to Biometrics Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #6 Guest Lecture + Some Topics in Biometrics September 12,
Computer Applications I I dentify alternative input devices and techniques.
1 Image Recognition has a wide variety of cool apps Robotic control Robotic control Quality control of manufactured parts Quality control of manufactured.
  Computer vision is a field that includes methods for acquiring,prcessing, analyzing, and understanding images and, in general, high-dimensional data.
Making Research Tools Accessible for All AI Students Zach Dodds, Christine Alvarado, and Sara Sood Though a compelling area of research with many applications,
A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from.
Shital ghule..  INTRODUCTION: This paper proposes an ATM security model that would combine a physical access card,a pin and electronic facial recognition.
1 Image Recognition has a wide variety of cool apps Robotic control Robotic control Quality control of manufactured parts Quality control of manufactured.
MULTIMEDIA SYSTEMS CBIR & CBVR. Schedule Image Annotation (CBIR) Image Annotation (CBIR) Video Annotation (CBVR) Video Annotation (CBVR) Few Project Ideas.
Seethru Range of Home Surveillance Systems
Technology: Mobile, Wifi, and Facial Recognition
Face Detection and Notification System Conclusion and Reference
Technology: Mobile, Wifi, and Facial Recognition
Gait Analysis for Human Identification (GAHI)
ABSTRACT FACE RECOGNITION RESULTS
CSSE463: Image Recognition
CSE/EE 576 Computer Vision Spring 2007
CSE/EE 576 Computer Vision Spring 2011
CSE 455 Computer Vision Autumn 2014
EE 596 Machine Vision Autumn 2015
CSEP 576 Computer Vision Winter 2008
Seminar Presentation on Biometrics
Introduction Computer vision is the analysis of digital images
Introduction What IS computer vision?
CSE/EE 576 Computer Vision Spring 2012
吴心筱 计算感知 吴心筱
CSSE463: Image Recognition Day 17
CSE/EE 576 Computer Vision Spring 2010
Geography & Technology Global Positioning System “GPS”
CSSE463: Image Recognition Day 16
Tricks to design gate pass for visitors with ID card maker tool
Image Recognition has a wide variety of cool apps
Introduction Computer vision is the analysis of digital images
Lab # 5 Detecting and reading barcodes
CS 332 Visual Processing in Computer and Biological Vision Systems
CSSE463: Image Recognition Day 16
Image Recognition has a wide variety of cool apps
Jiwon Kim Steve Seitz Maneesh Agrawala
Presentation transcript:

1 Image Recognition has a wide variety of cool apps Robotic control Robotic control Quality control of manufactured parts Quality control of manufactured parts Medical imaging Medical imaging Images from Dr. Walter Images from Dr. Walter Biometrics Biometrics Photograph analysis Photograph analysis Surveillance Surveillance Face detection Face detection Color matching by cell- phone Color matching by cell- phone 3D photography/mapping (Google’s street view) 3D photography/mapping (Google’s street view)street viewstreet view

Term Projects Requirements Requirements Interesting to you Interesting to you Anywhere on theoretical to real-world spectrum Anywhere on theoretical to real-world spectrum Could lead to publishable work Could lead to publishable work Teams of 4 Teams of 4 Each person me a topic (1 annotated slide, with >= 1 reference) by Monday 11:59 pm Each person me a topic (1 annotated slide, with >= 1 reference) by Monday 11:59 pm If you have already formed a team, you need only submit 1 proposal per team. If you have already formed a team, you need only submit 1 proposal per team. Will give a 2-minute, 1-slide “lightning talk” on Tuesday and projects will be chosen by the end of the day. Will give a 2-minute, 1-slide “lightning talk” on Tuesday and projects will be chosen by the end of the day.

How to start What interests you, either in the list or on your own? What interests you, either in the list or on your own? Be creative! Be creative! Dig some Dig some Talk to me Talk to me Consult the literature (at least 1 reference initially) Consult the literature (at least 1 reference initially)

4 Past CSSE463 projects Misapplied coating Misapplied coating Counting Fly-eye cells Counting Fly-eye cells Gesture recognition Gesture recognition Aerial airplane detection Aerial airplane detection Iris detection Iris detection Image tagging Image tagging Shopping cart surveillance for Baesler’s Shopping cart surveillance for Baesler’s Where’s Waldo? Where’s Waldo? Connect the Dots Connect the Dots

Past projects, Road-sign detection Road-sign detection License-plate detection License-plate detection Recognizing Constellations Recognizing Constellations IGVC Obstacle and Line Detection IGVC Obstacle and Line Detection Connect the Dots Connect the Dots Geographic Feature Extraction from Satellite Images Geographic Feature Extraction from Satellite Images

Past projects, Tennis-ball tracker in video (in OpenCV, not MATLAB) Tennis-ball tracker in video (in OpenCV, not MATLAB) Projectile Hole Recognition Projectile Hole Recognition For senior project For senior project High-capacity color barcodes High-capacity color barcodes Road location from frontal view Road location from frontal view Towards automatic generation of Miis from photos Towards automatic generation of Miis from photos

Past projects, Handwritten digit recognition for Tablet PC Handwritten digit recognition for Tablet PC ID’ing the goal and ball in RoboCup ID’ing the goal and ball in RoboCup Playing Card ID system Playing Card ID system Salamander recognition (for biologists) Salamander recognition (for biologists) Talk show recognition from video stills Talk show recognition from video stills NEAT Face Detection NEAT Face Detection Towards Object Recognition, using OpenCV and real-time videos Towards Object Recognition, using OpenCV and real-time videos Painting genre classifier Painting genre classifier

Past projects, Automated image region tagging Automated image region tagging Spot the differences Spot the differences Connect the dots (digit recognition) Connect the dots (digit recognition) Counting cells in fly retinas Counting cells in fly retinas Gesture recognition Gesture recognition Conformal coating misapplication detection Conformal coating misapplication detection

Past projects, Where’s Waldo? Where’s Waldo? Face recognition in video Face recognition in video Iris detection Iris detection Sport classification Sport classification Eigenfaces Eigenfaces Aircraft detection in aerial images Aircraft detection in aerial images Baesler’s shopping cart detection Baesler’s shopping cart detection

Senior Thesis Topics My research was on using context to help classify whole scenes I have done… We could do… Camera settings (flash, exposure time) Geospatial (GPS) Data General image collections Specific image collections (learning stats specific to a single user’s habits) Fully-labeled data Partially-labeled data General image collections Events (parties, weddings) Outdoor scenes Indoor scenes (done already)