CV in a Nutshell (I) Yi Li inNutshell.htm.

Slides:



Advertisements
Similar presentations
Computer Vision Group UC Berkeley How should we combine high level and low level knowledge? Jitendra Malik UC Berkeley Recognition using regions is joint.
Advertisements

Understanding Optical Illusions
Computer Vision Panel Board on Mathematical Sciences Workshop on The Interface Between Computer Science & Mathematical Sciences April 28-29, 2000, NAS,
Image formation. Image Formation Vision infers world properties form images. How do images depend on these properties? Two key elements –Geometry –Radiometry.
CV in a Nutshell (||) Yi Li inNutshell.htm.
Big Idea 2: Develop an understanding of and use formulas to determine surface areas and volumes of three-dimensional shapes.
Face Recognition. Introduction Why we are interested in face recognition? Why we are interested in face recognition? Passport control at terminals in.
Detecting Faces in Images: A Survey
Face Image Recognition Face recognition technology works well with most of the shelf PC cameras, generally requiring 320*240 resolution at 3~5 frames per.
Face Recognition By Sunny Tang.
A survey of Face Recognition Technology Wei-Yang Lin May 07, 2003.
Amir Hosein Omidvarnia Spring 2007 Principles of 3D Face Recognition.
CS 790Q Biometrics Face Recognition Using Dimensionality Reduction PCA and LDA M. Turk, A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive.
Recognising objects and faces. General problems Given that objects move on a surface, why do they not appear to change shape? How do we recognise objects.
Eigenfaces As we discussed last time, we can reduce the computation by dimension reduction using PCA –Suppose we have a set of N images and there are c.
Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces)
Face Recognition eScience Project of MIT Comp 6702 (18 units)
Segmentation and Perceptual Grouping Kaniza (Introduction to Computer Vision, )
Face Recognition: A Comparison of Appearance-Based Approaches
Motion Field and Optical Flow. Outline Motion Field and Optical Flow Definition, Example, Relation Optical Flow Constraint Equation Assumptions & Derivation,
The Terrapins Computer Vision Laboratory University of Maryland.
Object recognition under varying illumination. Lighting changes objects appearance.
Vision in Man and Machine. STATS 19 SEM Talk 2. Alan L. Yuille. UCLA. Dept. Statistics and Psychology.
Face Recognition: An Introduction
Three-Dimensional Face Recognition Using Surface Space Combinations Thomas Heseltine, Nick Pears, Jim Austin Advanced Computer Architecture Group Department.
A Brief Survey on Face Recognition Systems Amir Omidvarnia March 2007.
PhD Thesis. Biometrics Science studying measurements and statistics of biological data Most relevant application: id. recognition 2.
CS 485/685 Computer Vision Face Recognition Using Principal Components Analysis (PCA) M. Turk, A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive.
1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.
Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean Hall 5409 T-R 10:30am – 11:50am.
MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.
8/16/99 Computer Vision and Modeling. 8/16/99 Principal Components with SVD.
Themes in Computer Vision Carlo Tomasi. Applications autonomous cars, planes, missiles, robots,... space exploration aid to the blind, ASL recognition.
Recognition Part II Ali Farhadi CSE 455.
Face Recognition and Feature Subspaces
2D Shape Matching (and Object Recognition)
TECH 104 – Technical Graphics Communication Week 13: 3D Modeling Basics.
TTH 1:30-2:48 Winter DL266 CIS 788v04 Zhu Topic 5. Human Faces Human face is extensively studied.
1 Recognition by Appearance Appearance-based recognition is a competing paradigm to features and alignment. No features are extracted! Images are represented.
Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition IEEE Transaction on Information Forensics and Security Zhifeng Li,
Face Recognition: An Introduction
2/14/00 Computer Vision. 2/14/00 Computer Vision Lecturer: Ir. Resmana Lim, M.Eng. Text: 1) Computer Vision -- A Modern Approach.
Korea University Dept.of Industrial System & Information Engineering User Interface Lab Chapter 3 _ Object Recognition + 이병용.
Adaptive Rigid Multi-region Selection for 3D face recognition K. Chang, K. Bowyer, P. Flynn Paper presentation Kin-chung (Ryan) Wong 2006/7/27.
Real-Time Detection, Alignment and Recognition of Human Faces Rogerio Schmidt Feris Changbo Hu Matthew Turk Pattern Recognition Project June 12, 2003.
Paper Reading Dalong Du Nov.27, Papers Leon Gu and Takeo Kanade. A Generative Shape Regularization Model for Robust Face Alignment. ECCV08. Yan.
Facial Recognition Justin Kwong Megan Thompson Raymundo Vazquez-lugo.
Botany Image Retrieval Haibin Ling University of Maryland, College Park.
High-Level Vision Object Recognition.
Announcements Final is Thursday, March 18, 10:30-12:20 –MGH 287 Sample final out today.
TECH 104 – Technical Graphics Communication Week 12: 3D Modeling Basics.
Deeply learned face representations are sparse, selective, and robust
University of Ioannina
Lecture 25: Introduction to Recognition
Face Recognition and Feature Subspaces
Generalized Principal Component Analysis CVPR 2008
Image Primitives and Correspondence
Perceiving and Recognizing Objects
Lecture 25: Introduction to Recognition
Outline Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,”
Lecture 13: Cameras and geometry
Brief Review of Recognition + Context
Filtering Things to take away from this lecture An image as a function
Which of the equations below is an equation of a cone?
Announcements Final is Thursday, March 16, 10:30-12:20
CS4670: Intro to Computer Vision
Filtering An image as a function Digital vs. continuous images
High-Level Vision Object Recognition II.
Presentation transcript:

CV in a Nutshell (I) Yi Li inNutshell.htm

Outline Introduction Image Formation Early vision Cognition in psychology Face recognition (I)

Paper discussion (1) Recognition by Components: – Segmentation: blocks, cylinders, wedges, cones – Curvature, collinearity, symmetry, parallel – Contour, surface, texture – Occlusion – Top-down or bottom-up? – Relation: pairwise?

Paper discussion (2) Face Recognition – extremely low-resolution images (Yang and Ma) – Occlusion (sparse) – Component vs hollistic – Context! – Facial expression (Cohn) – Illumination (Basri and Jacobs) – Specialized (Martinez)

Image Formation Geometry – image plane – projection Physics of light – Brightness / Illumination – Light source – Lambertian scattered such that the apparent brightness of the surface to an observer is the same regardless of the observer's angle of view (wiki) nikonweb.com

Lighting

Early Vision Early year of computer vision – David Marr (Psychologist, MIT) – Azriel Rosenfeld (Mathematician, Maryland) – Takeo Kanade (Engineer, CMU) Visual path – How early? – Recognition? – Saliency / gist scienceblogs.com

Illusion Aperture problem ment2.html ment2.html

Skeletonization

Template Matching Find transformation to align two images cost function Example: Chamfer Matching Example 2: Hough transform Example 3: Probabilistic voting

Deformable Shape matching – Shape Context Snake – Active Contour, Active Appearance Articulated objects – Scissor – Human body

Face Recognition Linear subspaces – PCA and LDA – Eigenfaces vs Fisherfaces

Q/A Introduction Image Formation Early vision Cognition in psychology Face recognition (I)