'Camera matrix' diaporamas de présentation

Skin

974 views • 83 slides

The Image Formation Pipeline

The Image Formation Pipeline. Computer Graphics. Output. Image. Model. Synthetic Camera. (slides courtesy of Michael Cohen). Computer Vision. Output. Model. Real Scene. Real Cameras. (slides courtesy of Michael Cohen). Combined. Output. Image. Real Scene. Model. Synthetic

928 views • 75 slides

Global Alignment and Structure from Motion

758 views • 63 slides

CS 395/495-25: Spring 2004

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Onto 3D

Onto 3D. Coordinate systems 3-D homogeneous transformations Translation, scaling, rotation Changes of coordinates Rigid transformations. Vector Projection. The projection of vector a onto u is that component of a in the direction of u. Vector Cross Product.

765 views • 61 slides

A dvanced C omputer V ision Feature-based Alignment

A dvanced C omputer V ision Feature-based Alignment. Lecturer: Lu Yi & Prof. Fuh l ynn. luyi@gmail.com CSIE NTU. Content. Pre-requisites: mainly about camera model Alignment Algorithm RANdom Sample Consensus (RANSAC ) Pose Estimation. Sources.

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CV: 3D sensing and calibration

CV: 3D sensing and calibration. Coordinate system changes; perspective transformation; Stereo and structured light. roadmap. using multiple cameras using structured light projector 3D transformations general perspective transformation justification of 3x4 camera model.

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Camera Calibration

Camera Calibration. Course web page: vision.cis.udel.edu/cv. March 24, 2003 ? Lecture 17. Announcements . No class Wednesday Homework 3 due Wednesday Midterm on Friday

641 views • 27 slides

CS 395/495-25: Spring 2003

CS 395/495-25: Spring 2003. IBMR: Week 8A Chapter 8... Epipolar Geometry: Two Cameras: Jack Tumblin jet@cs.northwestern.edu. IBMR-Related Seminars. Light Scattering Models For Rendering Human Hair Steve Marschner, Cornell University Friday May 23 3:00pm, Rm 381, CS Dept. Reminders.

376 views • 27 slides

N -Linearities and Multiple View Tensors Class 19

N -Linearities and Multiple View Tensors Class 19. Multiple View Geometry Comp 290-089 Marc Pollefeys. Multiple View Geometry course schedule (subject to change). Multi-view geometry. Contraction: (once above, once below). Index rule:. Tensor notation. Transformations:. (covariant).

394 views • 27 slides

CS 395/495-25: Spring 2003

CS 395/495-25: Spring 2003. IBMR: Week 8A Chapter 8... Epipolar Geometry: Two Cameras: Jack Tumblin jet@cs.northwestern.edu. IBMR-Related Seminars. Light Scattering Models For Rendering Human Hair Steve Marschner, Cornell University Friday May 23 3:00pm, Rm 381, CS Dept. Reminders.

275 views • 27 slides

Cameras

First photograph due to Niepce First on record shown - 1822 Basic abstraction is the pinhole camera lenses required to ensure image is not too dark various other abstractions can be applied. Cameras. Abstract camera model - box with a small hole in it. Pinhole cameras work in practice.

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Structuring Visual Words in 3D for Arbitrary-View Object Localization

Structuring Visual Words in 3D for Arbitrary-View Object Localization. Jianxiong Xiao, Jingni Chen, Dit-Yan Yeung, and Long Quan Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong

372 views • 25 slides

Camera Calibration

Camera Calibration. Camera Calibration. Issues: what are intrinsic parameters of the camera? what is the camera matrix? (intrinsic+extrinsic) General strategy: view calibration object identify image points obtain camera matrix by minimizing error

810 views • 24 slides

Photogrammetry (Camera Models)

Photogrammetry (Camera Models). The basic pinhole model. Euclidean 3-space R3 to Euclidean 2-space R2. Central projection using homogeneous coordinates. Homogeneous 4-vector. Homogeneous Camera Projection Matrix. Principal point offset. Principal point offset.

617 views • 24 slides

Camera Calibration

367 views • 24 slides

Camera Calibration

Camera Calibration. Camera Calibration. Issues: what are intrinsic parameters of the camera? what is the camera matrix? (intrinsic+extrinsic) General strategy: view calibration object identify image points obtain camera matrix by minimizing error

333 views • 24 slides

CS 395/495-25: Spring 2003

CS 395/495-25: Spring 2003. IBMR: Week 7A The Camera Matrix Continued... Jack Tumblin jet@cs.northwestern.edu. IBMR-Related Seminars. 3D Scanning for Cultural Heritage Applications Holly Rushmeier, IBM TJ Watson Friday May 16 3:00pm, Rm 381, CS Dept.

343 views • 23 slides

254 views • 12 slides

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics:

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography Estimating homography from point correspondence The single perspective camera An overview of single camera calibration

215 views • 11 slides