'L1 norm' presentation slideshows

Comparable Corpora

Comparable Corpora. Kashyap Popat (113050023) Rahul Sharnagat (11305R013). Outline. Motivation Introduction: Comparable Corpora Types of corpora Methods to extract information from comparable corpora Bilingual dictionary Parallel sentences Conclusion. Motivation.

673 views • 44 slides

Linear Solution to Scale and Rotation Invariant Object Matching

Linear Solution to Scale and Rotation Invariant Object Matching. Hao Jiang and Stella X. Yu Computer Science Department Boston College. Problem. Template image and mesh. Target image. Matching. Target Mesh. Challenges. Template Image. Target Images. Challenges. Template Image.

436 views • 26 slides

Prediction with Regression

445 views • 25 slides

Static Image Mosaicing

Static Image Mosaicing. Amin Charaniya (amin@cse.ucsc.edu). EE 264: Image Processing and Reconstruction. Presentation Overview. Problem definition Background Literature Survey Image transformations Image Registration Coarse Image registration Transformation Optimization Image Blending

402 views • 22 slides

Static Image Mosaicing

Static Image Mosaicing. Amin Charaniya (amin@cse.ucsc.edu). EE 264: Image Processing and Reconstruction. Presentation Overview. Problem definition Background Literature Survey Image transformations Image Registration Coarse Image registration Transformation Optimization Image Blending

397 views • 22 slides

Statistical NLP: Lecture 10

Statistical NLP: Lecture 10. Lexical Acquisition. Goal of Lexical Acquisition. Goal: To develop algorithms and statistical techniques for filling the holes in existing machine-readable dictionaries by looking at the occurrence patterns of words in large text corpora.

370 views • 18 slides

Compressive sensing

Compressive sensing. Aishwarya sreenivasan 15 December 2006. transform coding. *linear process *no information is lost *number of coefficients = number of pixels transformed *transform coefficients and their locations *imaging sensor is forced to acquire and process the entire signal.

209 views • 18 slides

Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization

Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization. Authors: Baiyang Liu, Lin Yang, Junzhou Huang , Peter Meer, Leiguang Gong and Casimir Kulikowski. Outline. Problem of Tracking State of the art algorithms The proposed algorithm Experiment result. The problem.

344 views • 16 slides

IE 302 Recitation 3

IE 302 Recitation 3. Distance Measures. Rectilinear distance (L 1 norm) d(X, P i ) = |x - a i | + |y - b i | Straight line or Euclidean distance (L 2 norm) d(X, P i ) = Tchebyshev distance (L ? norm) d(X, P i ) = max{|x - a i |, |y - b i |}. P i = (a i , b i ). X = (x, y).

212 views • 8 slides

Caltech October 2005 Compressive Sensing Tutorial PART 2 Svetlana Avramov-Zamurovic

187 views • 5 slides

Caltech October 2005 Compressive Sensing Tutorial PART 2 Svetlana Avramov-Zamurovic

57 views • 5 slides

Anthony Wong, MTech 1 , Senthil K. Nachimuthu, MD 1 , Peter J. Haug, MD 1,2

Predicting Sepsis in the ICU using Dynamic Bayesian Networks. Anthony Wong, MTech 1 , Senthil K. Nachimuthu, MD 1 , Peter J. Haug, MD 1,2. 1 Department of Biomedical Informatics, University of Utah, 2 Intermountain Healthcare, Salt Lake City, Utah. Introduction

150 views • 1 slides