'Image representation' diaporamas de présentation

Image Retrieval

Image Retrieval. John Tait University of Sunderland, UK. Outline of Afternoon. Introduction Why image retrieval is hard How images are represented Current approaches Indexing and Retrieving Images Navigational approaches Relevance Feedback Automatic Keywording

1.14k views • 95 slides

CPSC 641: Computer Graphics Image Formation

CPSC 641: Computer Graphics Image Formation. Jinxiang Chai. Are They Images?. Outline. Color representation Image representation Pin-hole Camera Projection matrix Plenoptic function. Outline. Color representation Image representation Pin-hole Camera Projection matrix

1.12k views • 91 slides

PLSA ??????

PLSA ??????. ???. Contents. ?????. LSA ????. PLSA ????. PLSA ??????????. PLSA ?????. ?????. ???????? ???? ???? ???? ???? ????? ????????????????????? ????? ???????????????. ????????. ????????. ??. ?? ?? ?? ?? ??? ? ??. ????????. ????????. ??.

871 views • 45 slides

Image Management

Image Management. Dr. Hayit Greenspan Dept of BioMedical Engineering Faculty of Engineering hayit@eng.tau.ac.il 640-7398. Roles for Imaging in Health Care:. Diagnosis Assessment and Planning Guidance of Procedures Communication Education and Training Research.

553 views • 42 slides

Binary Image Compression

Binary Image Compression. The art of modeling image source Image pixels are NOT independent events Run length coding of binary and graphic images Applications: BMP, TIF/TIFF Lempel-Ziv coding* How does the idea of building a dictionary can achieve data compression?. From Theory to Practice.

754 views • 40 slides

Image Processing for Microarray Data Analysis

Image Processing for Microarray Data Analysis. Chaur-Chin Chen Department of Computer Science National Tsing Hua University Hsinchu 300, Taiwan Tel/Fax +886 3 573 1078 / +886 3 572 3694 cchen@cs.nthu.edu.tw. Outline. A Digital Image Processing System Image Representation and Formats

536 views • 34 slides

Combining efficient object localization and image classi?cation

392 views • 31 slides

Learning to Detect Objects in Images via a Sparse, Part-Based Representation

434 views • 28 slides

The EM algorithm, and Fisher vector image representation

The EM algorithm, and Fisher vector image representation. Jakob Verbeek December 17, 2010 Course website: http://lear.inrialpes.fr/~verbeek/MLCR.10.11.php. Plan for the course. Session 1, October 1 2010 Cordelia Schmid: Introduction Jakob Verbeek: Introduction Machine Learning

546 views • 27 slides

Object Bank

Object Bank. Presenter ? Liu Changyu Advisor ? Prof. Alex Hauptmann Interest ? Multimedia Analysis. April 4 th , 2013. Contents. Introduction Model Algorithm Experiment Conclusion. Introduction. 1. Research Question

410 views • 24 slides

Discriminative and generative methods for bags of features

Discriminative and generative methods for bags of features. Zebra. Non-zebra. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. Image classification.

304 views • 22 slides

Object Detection Using the Statistics of Parts

334 views • 20 slides

OPTIMOL: automatic Object Picture collecTion via Incremental MOdel Learning

310 views • 18 slides

Simple Ray Tracing Framework

Simple Ray Tracing Framework. Interactive Control. Interactive Preview. Depth or Mask Image. Ray Trace Image. Technologies. C# Just another programming language WinForm : GUI Application Development not covered in this class Interactive Viewing: XNA (can be OGL or D3D)

289 views • 17 slides

Deformable Part Model

Deformable Part Model. Presenter ? Liu Changyu Advisor ? Prof. Alex Hauptmann Interest ? Multimedia Analysis. April 11 st , 2013. Contents. Introduction Model Learning Experiment Conclusion. Introduction. 1. Research Question

358 views • 17 slides

Object- oriented Model for GIS Compressed Images

OBJECT -ORIENTED MODEL FOR GIS COMPRESSED IMAGES Boris Rachev, Mariana Stoeva Technical University of Varna, Department of Computer Science 1, Studentska str., 9010, Varna, Bulgaria. Object- oriented Model for GIS Compressed Images. Abstract 1. Introduction 2. Definition of the Problem

304 views • 12 slides

Introduction

Introduction. Computer graphics (CG) is the use of a computer to define, store, manipulate, interrogate, and present pictorial output

185 views • 11 slides

COMPUTER GRAPHICS

237 views • 8 slides

CT333/CT433 Image Processing and Computer Vision

CT333/CT433 Image Processing and Computer Vision. Contact Details. Dr Tim Morris Room MSS/G19 tim.morris@manchester.ac.uk www.informatics.manchester.ac.uk/~dtm. Timetable. Week 1 2,3 4 5 6 7 8 9 10. Topic Introduction, Image Representation

188 views • 7 slides

3D Vision Research in CUHK

3D Vision Research in CUHK.

68 views • 1 slides