'Image features' diaporamas de présentation

Vision

Vision. University of Oulu Face Database. Yale Face Database. What are the requirements for a computer vision system capable of recognizing faces? What problems must be solved? What special techniques are required? What uses could be made of a working application?.

1.38k views • 105 slides

Joseph Gonzalez Joint work with

991 views • 87 slides

Bayesian Networks

Bayesian Networks. Tamara Berg CS 590-133 Artificial Intelligence. Many slides throughout the course adapted from Svetlana Lazebnik , Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke Zettlemoyer , Rob Pless , Killian Weinberger, Deva Ramanan. Announcements.

948 views • 84 slides

CS558 Computer Vision

922 views • 72 slides

A New Parallel Framework for Machine Learning

774 views • 68 slides

Interactive Segmentation

856 views • 63 slides

Face detection

707 views • 42 slides

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Image Retrieval: Current Techniques, Promising Directions, and Open Issues. Yong Rui, Thomas Huang and Shih-Fu Chang Published in the Journal of Visual Communication and Image Representation. Presented by: Deepak Bote. Presentation Outline. History of image retrieval – Issues faced

592 views • 41 slides

Learning Semantics of Words and Pictures

Learning Semantics of Words and Pictures. Tejaswi D evarapalli. C ontent. Introduction Modeling Image dataseT Statistics Hierarchical Model Testing and Using Basic Model Auto Illustration Auto Annotation Results Discussions. Semantics.

511 views • 40 slides

The Viola/Jones Face Detector

625 views • 34 slides

369 views • 29 slides

Tuesday Seminar Deformable Image Registration

07 th of January, 2014. Tuesday Seminar Deformable Image Registration. HyunSeok Lee leehseok @snu.ac.kr. Contents. What is Image Registration? Deformable Image Registration Basic concept Algorithm Products Open source Debates about dose deform TG132. What is image registration?.

885 views • 28 slides

Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues

Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. David R. Martin Charless C. Fowlkes Jitendra Malik. Paper Contribution. Proposed Boundary Classifier Uses local image features Detect & Localize Boundaries

436 views • 26 slides

Exploiting Ontologies for Automatic Image Annotation

M. Srikanth, J. Varner, M. Bowden, D. Moldovan Language Computer Corporation www.languagecomputer.com Richardson, Texas. Exploiting Ontologies for Automatic Image Annotation. Contents. Motivation Automatic Image Annotation Problem Ontologies for Defining Visual Vocabularies

391 views • 25 slides

Heather Dunlop 16-721: Advanced Perception January 25, 2006

Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues by David R. Martin, Charless C. Fowlkes, Jitendra Malik. Heather Dunlop 16-721: Advanced Perception January 25, 2006. Human. Canny. Martin, 2002. What is a Boundary?. Dataset.

310 views • 23 slides

Searching for Logo and Trademark Images on the Web

Searching for Logo and Trademark Images on the Web. Euripides G.M. Petrakis * Epimenidis Voutsakis * Evangelos Milios ** * Technical University of Crete, Chania, Greece ** Dalhousie University, Halifax, Canada. Retrieval of Logo & Trademarks.

278 views • 19 slides

LEARNING IMAGE FEATURES TO ENCODE VISUAL INFORMATION (LIFE-VISION)

LEARNING IMAGE FEATURES TO ENCODE VISUAL INFORMATION (LIFE-VISION). Kick-off meeting 28/01/2013. VISUALLY LOSSLESS CODING. Visibility thresholds. LIFE-VISION. VISUALLY LOSSLESS CODING. Visibility thresholds. LIFE-VISION. VISUALLY LOSSLESS CODING. Visibility thresholds. LIFE-VISION.

239 views • 18 slides

Browserbite: Accurate Cross-Browser Testing via Machine Learning Over Image Features

271 views • 17 slides

Incorporating Global Information into Active Contour Models

Incorporating Global Information into Active Contour Models. Anthony Yezzi Georgia Institute of Technology. Snakes: Active Contour Models. Initialization. Final Segmentation. Snakes or Active Contours pose the segmentation as an energy minimization problem. Kass, Witkins & Terzopoulos.

307 views • 15 slides

Machine Learning

121 views • 0 slides