'Image space' presentation slideshows

Segmentation & Fitting

Segmentation & Fitting. Dan Witzner Hansen. Today (and some of next week too). Review Data fitting Segmentation Clustering Semi-automatic methods Snakes. Review?. Extracting objects. How could this be done?. From images to objects. What Defines an Object?

1.08k views • 93 slides

Brightfield and Phase Contrast Microscopy

2.01k views • 63 slides

Edge Detection and Geometric Primitive Extraction

Edge Detection and Geometric Primitive Extraction. Jinxiang Chai. Outline. Edge detection Geometric primitive extraction. Edge Detection. What are edges in this image?. Edge Detection. What are edges in this image?. Origin of Edges. Edges are caused by a variety of factors.

800 views • 63 slides

From-Point Occlusion Culling

From-Point Occlusion Culling. Chapter 23. Talk Outline . Image space methods Hierarchical Z-Buffer Hierarchical occlusion maps Some other methods Object space methods General methods Shadow frusta, BSP trees, temporal coherent visibility Cells and portals.

647 views • 48 slides

From-Point Occlusion Culling

From-Point Occlusion Culling. Chapter 23. Talk Outline . Image space methods Hierarchical Z-Buffer Hierarchical occlusion maps Some other methods Object space methods General methods Shadow frusta, BSP trees, temporal coherent visibility Cells and portals.

485 views • 48 slides

Mixed-Resolution Patch-Matching (MRPM)

Mixed-Resolution Patch-Matching (MRPM). Harshit Sureka and P.J. Narayanan (ECCV 2012). Presentation by Yaniv Romano. Problem Definition. Patch Matching Nearest Neighbor Fields: F or every patch in image A, find the K most similar patches in image B. A. B. NNF.

714 views • 48 slides

Volume Rendering, Part 1

Volume Rendering, Part 1. Hank Childs, University of Oregon. November 13 th , 2013. Announcements. Final project Proposals due Fri No proposals for pre-defined project Volume rendering. Prompt out this weekend. OH: Weds 10-11:30 Thurs 10- 11. Upcoming Schedule.

604 views • 42 slides

Generic Object Detection using Feature Maps

Generic Object Detection using Feature Maps. Oscar Danielsson (osda02@kth.se) Stefan Carlsson ( stefanc@kth.se ). Outline. Detect all Instances of an Object Class. The classifier needs to be fast (on average). This is typically accomplished by:

636 views • 40 slides

Likelihood Models for Template Matching Using the PDF Projection Theorem

Likelihood Models for Template Matching Using the PDF Projection Theorem. Arasanathan Thayananthan Ramanan Navaratnam Dr. Phil Torr Prof. Roberto Cipolla. Problem. Overview. Problem Motivation PDF Projection Theorem Likelihood Modelling for Chamfer Matching Experiments Conclusion.

607 views • 39 slides

Sample CT Image

Sample CT Image. X-ray. 2 % attenuation change detectable in film. CT. 0.2 % change in attenuation coefficient. y. x. f(x,y). CT, like X-ray measure line integrals. ? ? ? ? ?. g(R) = CT detector array output. g(R). Direct Methods. Radon Transform 1917

530 views • 34 slides

Programmable Graphics Hardware CS 446: Real-Time Rendering & Game Technology

Programmable Graphics Hardware CS 446: Real-Time Rendering & Game Technology. David Luebke University of Virginia. Recap: Advanced Texturing. Billboards Screen-aligned, world-aligned Point sprites Imposters Trees, buildings, portal textures, billboard clouds

485 views • 33 slides

Non-Photo Realistic Rendering

515 views • 31 slides

Optical systems

Optical systems. Hecht 5.7 Wednesday October 2, 2002. Field Stop. The aperture that controls the field of view by limiting the solid angle formed by chief rays. As seen from the centre of the entrance pupil (E n P), the field stop (or its image) subtends the smallest angle.

629 views • 29 slides

3D Scanner

485 views • 21 slides

Orderless Tracking through Model-Averaged Posterior Estimation

Orderless Tracking through Model-Averaged Posterior Estimation. Seunghoon Hong* Suha Kwak Bohyung Han Computer Vision Lab. Dept. of Computer Science and Engineering POSTECH. Goal of Visual Tracking. Robust estimation of accurate target states through out an input video.

385 views • 21 slides

Orderless Tracking through Model-Averaged Posterior Estimation

Orderless Tracking through Model-Averaged Posterior Estimation. Seunghoon Hong* Suha Kwak Bohyung Han Computer Vision Lab. Dept. of Computer Science and Engineering POSTECH. Goal of Visual Tracking. Robust estimation of accurate target states through out an input video.

445 views • 21 slides

Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces)

Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces). Pradeep Buddharaju. COSC 6397. U of H. Principal Component Analysis. A N x N pixel image of a face, represented as a vector occupies a single point in N 2 -dimensional image space.

782 views • 19 slides

Hough Transform

Hough Transform. Finding lines in an image. Option 1: Search for the line at every possible position/orientation What is the cost of this operation? Option 2: Use a voting scheme: Hough transform. Finding lines in an image. Connection between image (x,y) and Hough (m,b) spaces

309 views • 11 slides

Ultrasound

307 views • 8 slides

Joomla Article Image Gallery Modal Box

210 views • 5 slides