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Non-Linear Least Squares and Sparse Matrix Techniques: Fundamentals

Non-Linear Least Squares and Sparse Matrix Techniques: Fundamentals

Non-Linear Least Squares and Sparse Matrix Techniques: Fundamentals. Richard Szeliski Microsoft Research UW-MSR Course on Vision Algorithms CSE/EE 577, 590CV, Spring 2004. Readings. Press et al. , Numerical Recipes, Chapter 15 (Modeling of Data)

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791 views • 55 slides
Noise Estimation from a Single Image

Noise Estimation from a Single Image

Noise Estimation from a Single Image. Ce Liu William T. Freeman. Richard Szeliski Sing Bing Kang. Parameter Tweaking in Computer Vision. Computer vision algorithms suffer from hand tuning parameters for particular images or image sequences

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474 views • 34 slides
ETISEO Project

ETISEO Project

ETISEO Project. V ideo U nderstanding Evaluation. David CHER R&D department SILOGIC S.A. , Toulouse, France. Summary. General introduction Specific topics : V ideo Providers, Corpus Data Ground Truth & Video annotation Tasks & Metrics Evaluation Cycles Results & Dissemination.

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251 views • 12 slides
Hack, Math and Stat --- call for a reconciliation between extreme views

Hack, Math and Stat --- call for a reconciliation between extreme views

Hack, Math and Stat --- call for a reconciliation between extreme views. Song-Chun Zhu. The Frontiers of Vision Workshop, August 20-23, 2011. Hack, Math and Stat: co-existing ingredients. Hack : something, somehow, works better somewhere.

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244 views • 11 slides
Create ing Low-cost Spatial Awareness

Create ing Low-cost Spatial Awareness

Create ing Low-cost Spatial Awareness. Sabreen Lakhani '11, Anatole Paine '11, Zeke Koziol '10, Zach Dodds (advisor). Education : they're seven-year-old-proof!. Next-gen Toys. Accessibility. Applications.

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119 views • 1 slides

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