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When Signal Processing Meets Machine Learning

When Signal Processing Meets Machine Learning

When Signal Processing Meets Machine Learning. Yu-Chiang Wang ??? , PhD Candidate Electrical & Computer Engineering Carnegie Mellon University. March 2009. Pittsburgh, USA. Home of the Pittsburgh Pirates. And the Pittsburgh Steelers!. Who Are They?. Signal Processing

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922 views • 86 slides
Camera Calibration

Camera Calibration

CS 636 Computer Vision. Camera Calibration. Nathan Jacobs. overview. assignment 2 out review from last time review camera parameters intrinsic extrinsic several methods for camera calibration constraints vs. solution methods vs. error functions.

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1.01k views • 41 slides
Nonlinear High-Fidelity Solutions for Relative Orbital Motion

Nonlinear High-Fidelity Solutions for Relative Orbital Motion

Nonlinear High-Fidelity Solutions for Relative Orbital Motion. 20 May 2014. Dr. T . Alan Lovell AFRL/RVSV Space Vehicles Directorate. Outline. Goals, Motivation, Previous/Recent Work FY14 Effort Projected FY15 Effort. Goals.

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431 views • 32 slides
Camera Model & Camera Calibration

Camera Model & Camera Calibration

Camera Model & Camera Calibration. Slides are from Marc Pollefeys @ETH. Pinhole camera model. Pinhole camera model. Principal point offset. principal point. Principal point offset. calibration matrix. Camera rotation and translation. CCD camera. non-singular. Finite projective camera.

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908 views • 25 slides
Grouping Process for MU-MIMO

Grouping Process for MU-MIMO

Grouping Process for MU-MIMO. Authors:. Date: 2010-05-16. Outline. Typical problem definition MU solution details: Optimal linear solution [1] Single and multi-spatial stream users Grouping algorithm Real-time implementation issues Qualifying candidate groupings

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355 views • 18 slides
Random Walk with Restart (RWR) for Image Segmentation

Random Walk with Restart (RWR) for Image Segmentation

Random Walk with Restart (RWR) for Image Segmentation. Sungsu Lim AALAB, KAIST. Image Segmentation. Computer vision : make machine to see or to understand/ interpret the scenes (images & videos) like human do. Image segmentation is one of the most challenging issues in computer vision.

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441 views • 16 slides
Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 2 2 Linear discrimination - variants W?odzis?aw Duch Dept. of Informatics, UMK Google: W Duch. LDA Wine example. LDA with WEKA, using Wine data. Classification via regression is used, regression may also be used.

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307 views • 14 slides

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