A Flexible Camera Calibration Tool for 3D Capture. Lei Wang Media and Machine Lab Advisor: Cindy Grimm. 3D Capture. Data Acquisition Camera Calibration Shape Integration Texture Synthesis Shape Texture Integration. Camera Calibration. Camera Model Global Approach Requirement

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ByCamera calibration and single view metrology Class 4. Read Zhang’s paper on calibration http://www.vision.caltech.edu/bouguetj/calib_doc/papers/zhan99.pdf Read Criminisi’s paper on single view metrology http://www.unc.edu/courses/2004fall/comp/290/089/papers/Criminisi99.pdf. Camera model.

ByImage Warping. http://www.jeffrey-martin.com. 15-463: Computational Photography Alexei Efros, CMU, Fall 2007. Some slides from Steve Seitz. f. f. f. T. T. x. x. x. f. x. Image Warping. image filtering: change range of image g(x) = T(f(x)).

ByThree-Dimensional Viewing. CVGLab. Introduction. We want to create and control a camera that produces perspective projections. We also need ways to take more control of the camera’s position and orientation. We also need to achieve precise control over the camera’s view volume.

ByBLOOD SPATTER ANALYSIS. CP Forensics Alvarado. How A SOURCE OF FORENSIC EVIDENCE?. 1) Origin(s) of bloodstain 2) Distance of bloodstain from target 3) Direction from which blood impacted. 4) Speed with which blood left its source 5) Position of victim &

By09/27/11. Image Warping. Computational Photography Derek Hoiem, University of Illinois. Many slides from Alyosha Efros + Steve Seitz. Photo by Sean Carroll. Administrative stuff. Vote for class favorites for project 2 Next Tues: take photos – can I get a volunteer for photographer?.

ByCollision recognition from a video part A . Students: Adi Vainiger , Eyal Yaacoby Supervisor: Netanel Ratner Laboratory of Computer Graphics & Multimedia Electrical Engineering faculty, Technion Semester: Winter 2012. Objective. Design a system with two main roles:

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BySean’s Research and Stuff. By Sean Hoyt – October 10, 2001 DMS Laboratory EE1-159. Main Areas of Research. Radio Frequency Identification (RFID) Understanding Optimization – Cypris’ PSoC Extending into other sensing areas Portable Chemical Sensor “Electronic Nose”

ByWhat is Computer Vision?. Finding “meaning” in images Where’s Waldo? How many cells are on this slide? Is there a brain tumor here? Find me some pictures of horses. Where is the road? Is there a safe path to the refrigerator? Where is the “widget” on the conveyor belt?

ByGames Development 1 Camera Projection / Picking. CO3301 Week 8. Today’s Lecture. World / View Matrices Recap Projection Maths Pixel from World-Space Vertex World Space Ray from Pixel

ByProjective geometry. ECE 847: Digital Image Processing. Stan Birchfield Clemson University. Lines. almost. A line in 2D is described by two parameters: But vertical lines? Only two parameters are sufficient, but requires nonlinear formulation:. ^. slope. y-intercept. Lines.

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