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A Flexible Camera Calibration Tool for 3D Capture

A Flexible Camera Calibration Tool for 3D Capture

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

By niveditha
(516 views)

Image Warping

Image Warping

Image Warping. http://www.jeffrey-martin.com. 15-463: Computational Photography Alexei Efros, CMU, Fall 2005. 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)).

By kalliyan
(133 views)

Camera calibration and single view metrology Class 4

Camera calibration and single view metrology Class 4

Camera 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.

By shiloh
(662 views)

Image Warping

Image Warping

Image 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)).

By yelena
(113 views)

Three-Dimensional Viewing

Three-Dimensional Viewing

Three-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.

By ganya
(276 views)

BLOOD SPATTER ANALYSIS

BLOOD SPATTER ANALYSIS

BLOOD 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 &

By elvis
(753 views)

Image Warping

Image Warping

09/27/11. 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?.

By serge
(90 views)

Collision recognition from a video part A

Collision recognition from a video part A

Collision 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:

By moe
(87 views)

Mini-voids in the Local Volume

Mini-voids in the Local Volume

Mini-voids in the Local Volume. A.Tikhonov , St. Petersburg Univ. I.Karachentsev, SAO RAS RUSSIA. Abstract.

By stuart
(93 views)

Sean’s Research and Stuff

Sean’s Research and Stuff

Sean’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”

By taline
(84 views)

What is Computer Vision?

What is Computer Vision?

What 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?

By cain
(61 views)

Games Development 1 Camera Projection / Picking

Games Development 1 Camera Projection / Picking

Games 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

By michi
(118 views)

Projective geometry

Projective geometry

Projective 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.

By devined
(0 views)


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