'Camera parameters' diaporamas de présentation

Cameras, lenses and sensors

Image Processing. Introduction to Image Processing. Cameras, lenses and sensors. Cosimo Distante Cosimo.distante@cnr.it Cosimo.distante@unisalento.it. Cameras, lenses and sensors. Camera Models Pinhole Perspective Projection Camera with Lenses Sensing The Human Eye.

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CSCE 641 Computer Graphics: Image-based Modeling

CSCE 641 Computer Graphics: Image-based Modeling. Jinxiang Chai. Image-based modeling. Estimating 3D structure Estimating motion, e.g., camera motion Estimating lighting Estimating surface model. Traditional modeling and rendering. Geometry Reflectance Light source Camera model.

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Self-calibration Class 13

Self-calibration Class 13. Read Chapter 6. Assignment 3. Collect potential matches from all algorithms for all pairs Matlab ASCII format, exchange data Implement RANSAC that uses combined match dataset Compute consistent set of matches and epipolar geometry

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Photo Navigator

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Mixed Reality Systems

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Lecture 12: Structure from motion

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Presenter : Shiu , Jia-Hau Advisor : Wang, Sheng-Jyh

Simultaneous surveillance camera calibration and foot-head homology estimation from human detection 1 Author : Micusic & Pajdla. Presenter : Shiu , Jia-Hau Advisor : Wang, Sheng-Jyh. 1. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Outline.

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3D Reconstruction Using Aerial Images

3D Reconstruction Using Aerial Images. A Dense Structure from Motion pipeline. Ramakrishna Vedantam CTT IN, Bangalore. Project Goal. 3D capture of ground structures using aerial imagery. Volume Estimation of mine dumps Infrastructure development monitoring Augmented Reality.

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Camera parameters

Camera parameters. Extrinisic parameters define location and orientation of camera reference frame with respect to world frame Intrinsic parameters define pixel coordinates of image point with respect to coordinates in camera reference frame. Homogenous coordinates.

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Single-view geometry

Single-view geometry. Odilon Redon, Cyclops, 1914. X?. X?. X?. Our goal: Recovery of 3D structure. Recovery of structure from one image is inherently ambiguous. x. Our goal: Recovery of 3D structure. Recovery of structure from one image is inherently ambiguous.

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Accurate Camera Calibration from Multi-view Stereo and Bundle Adjustment

Accurate Camera Calibration from Multi-view Stereo and Bundle Adjustment. Yasutaka Furukawa, University of Illinois at Urbana-Champaign, USA Jean Ponce, Ecole Normale Superieure , Paris, France Reporter ? Jheng-You Lin. Outline. Introduction Imaging Model Algorithm Experimental Results.

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Announcements

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Electrogenerated Chemiluminescence Detection in Paper-Based Microfluidic Sensors

Electrogenerated Chemiluminescence Detection in Paper-Based Microfluidic Sensors. Advisor: Dr. James Rusling Boya Song Feb. 24, 2011. Outline. What is Microfluidic sensor ? Why paper -based ? How to make the Microfluidic sensor ? How does the Microfluidic sensor work ?

1.18k views • 22 slides

Electrogenerated Chemiluminescence Detection in Paper-Based Microfluidic Sensors

Electrogenerated Chemiluminescence Detection in Paper-Based Microfluidic Sensors. Advisor: Dr. James Rusling Boya Song Feb. 24, 2011. Outline. What is Microfluidic sensor ? Why paper -based ? How to make the Microfluidic sensor ? How does the Microfluidic sensor work ?

1.35k views • 22 slides

Single-view geometry

Single-view geometry. Odilon Redon, Cyclops , 1914. X?. X?. X?. Our goal: Recovery of 3D structure. Recovery of structure from one image is inherently ambiguous. x. Our goal: Recovery of 3D structure. Recovery of structure from one image is inherently ambiguous.

372 views • 22 slides

Screen-Strategy Analysis in Broadcast Basketball Video using Player Tracking

450 views • 20 slides

Silhouette-based Object Phenotype Recognition using 3D Shape Priors

Silhouette-based Object Phenotype Recognition using 3D Shape Priors. Yu Chen 1 Tae- Kyun Kim 2 Roberto Cipolla 1 University of Cambridge, Cambridge, UK 1 Imperial College, London, UK 2. Problem Description. Task: To identify the phenotype class of deformable objects.

266 views • 19 slides

Silhouette-based Object Phenotype Recognition using 3D Shape Priors

Silhouette-based Object Phenotype Recognition using 3D Shape Priors. Yu Chen 1 Tae- Kyun Kim 2 Roberto Cipolla 1 University of Cambridge, Cambridge, UK 1 Imperial College, London, UK 2. Problem Description. Task: To identify the phenotype class of deformable objects.

272 views • 19 slides

Notes

Notes. Movie today (compositing etc.). Example. Camera Control. Text: 3.4, 4.1 Three situations to think about: CG film Everything is synthetic, so control the camera however you want (artistic control or algorithms + artistic tweaking) Video games

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Research Group Human Factors (HF)

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