'Image set' diaporamas de présentation

Lecture Two for teens

Lecture Two for teens. Introduction to Morphological Operators. About this lecture. In this lecture we introduce INFORMALLY the most important operations based on morphology, just to give you the intuitive feeling. In next lectures we will introduce more formalism and more examples.

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Kernel Discriminant Analysis Based on Canonical Difference for Face Recognition in Image Sets

Kernel Discriminant Analysis Based on Canonical Difference for Face Recognition in Image Sets. Wen-Sheng Chu ( ??? ) Ju-Chin Chen ( ??? ) Jenn-Jier James Lien ( ??? ) Robotics Lab, CSIE NCKU http://robotics.csie.ncku.edu.tw CVGIP 2007. pose. facial expression. illumination.

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Face Recognition and Retrieval in Video

Face Recognition and Retrieval in Video. Basic concept of Face Recog . & retrieval And their basic methods. C.S.E. Kwon Min Hyuk. True? False?. Q1 : in recently, face recognition researches focus on video-based rather than still image-based (O / X) Q2 : There is three approaches; (O/ X)

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Image Search

Image Search. SMX West 2009 Case Study. Image Search Facts. Image Search Volume. comScore numbers say that 7+% of all searches take place in image search engines. Other industry figures have suggested the total image search volume is about 15% of all search

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Plenoptic Modeling: An Image-Based Rendering System

Plenoptic Modeling: An Image-Based Rendering System. Leonard McMillan & Gary Bishop SIGGRAPH 1995 presented by Dave Edwards 10/12/2000. Introduction. Two Kinds of Rendering Geometric Requires increased complexity to achieve realism Solid foundation & physical models Image-based

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COLOR ME ORTHOED

COLOR ME ORTHOED. Gary Lobdell Photogrammetric Account Manger Certified Photogrammetrist The Sidwell Company. COLOR ME ORTHOED. ORTHOS, NOW WHAT? Review of the Image 2005 Orthophoto Products DEM vs. DTM How Will I View Them? What Features Will I Be Able to Identify?

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Intro

Intro. CALTECH 256. Greg Griffin, Alex Holub and Pietro Perona. Overview. 256 Object Categories + Clutter At least 80 images per category 30608 images instead of 9144. Caltech-101: Drawbacks. Smallest category size is 31 images: Too easy? left-right aligned Rotation artifacts

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