Computational Photography - CSCI-GA.3033-012
Semester: Fall 2013. Time and Location: Thursday 5:00-6:50pm, Room 1221, 715 Broadway. TA: To be decided Instructor: Rob Fergus Office hours: Thursday 4-5pm, Room 1226, 12th floor, 715 Broadway. Overview Computational Photography is an exciting new area at the intersection of Computer Graphics and Computer Vision. Through the use of computation, its goal is to move beyond the limitations of conven
Locally Linear Embedding (LLE) Homepage
Jump to: A detailed tutorial description of the algorithm. References and links to LLE publications and (p)reprints. Gallery of example pictures and animations. LLE code page. Some notes and pointers to related work. Sam T. Roweis............ roweis at cs dot toronto dot edu ...... www.cs.toronto.edu/~roweis/ Lawrence K. Saul......... lsaul@research.att.com.......... www.research.att.com/~lsaul/
The Lasso Page
The Lasso Page L1-constrained fitting for statistics and data mining The Lasso is a shrinkage and selection method for linear regression. It minimizes the usual sum of squared errors, with a bound on the sum of the absolute values of the coefficients. It has connections to soft-thresholding of wavelet coefficients, forward stagewise regression, and boosting methods. A simple explanation of the las
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連立１次方程式ライブラリ
/* linear.c */ #include <stdio.h> #include <stdlib.h> #include "sslib.h" void gausei(double a[], int l, int m, int iter, double eps, double x[]) { int i, j, k; double ay, def, *p, *q, *r, sum, w, y; if(l < m || m < 2 || iter < 1 || eps <= 0.) { fprintf(stderr, "Error : Illegal parameter in gausei()\n"); return; } for(i = 0, p = x; i < m; i++) *p++ = 0.; k = 1; do { def = 0.; for(i = 0, p = x; i <
Scale-invariant feature transform - Wikipedia
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999.[1] Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first
直交ウェーブレット変換について
離散ウェーブレット変換について 信州大学工学部 井澤裕司 １．ウェーブレット変換とは 近年、画像信号の圧縮や解析の有力な手法としてウェーブレット変換が注目されている。従来のフーリエ変換が、三角関数を基底とした直交変換であるのに対し、ウェーブレット変換では、局所化された関数から作られる相似関数系を基底とする。これにより、周波数精度は若干低下するが、時間−周波数の同時分解が可能となる。[1] ウェーブレット変換には、連続ウェーブレット変換と離散ウェーブレット変換がある。前者は、データのパターンや相似性の解析に用いられるのに対し、後者は収束性のよい正規直交系となるため、データ圧縮やエネルギ解析等に用いられる。[2] ここでは、画像圧縮への応用が期待されている離散ウェーブレット変換（以下、単にウェーブレット変換という）について解説する。なお、ウェーブレット変換は、サブバンド
Compute 3D shape and scene from single image
Publication Feng Han, and Song-Chun Zhu, "Bayesian Reconstruction of 3D Shapes and Scenes From A Single Image", Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis, Nice, 2003. Abstract It's common experience for human vision to perceive full 3D shape and scene from a single 2D image with the occluded parts ``filled-in'' by prior visual knowledge. In this paper we repre
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http://www.ceremade.dauphine.fr/~peyre/cs-tv/
Bregman Iterative Regularization and Bregman Methods
GPSR
http://lts2www.epfl.ch/~jacques/index.php?n=LJ.Codes
http://www.ifp.uiuc.edu/~cao4/MeanShiftbib.htm
A Topological Approach to Hierarchical Segmentation using Mean Shift
CSDL | IEEE Computer Society
http://www.math.ucla.edu/~xbresson/subdir/gmac.html
http://www.tgisfw.com/2007/08/seam-carving-and-image-resizing.html
http://www.math.ucla.edu/~gilboa/PDE-filt/diffusions.html
Comparaison sur une image artificielle
http://dl.cybernet.co.jp/matlab/support/manual/r14/toolbox/wavelet/?/matlab/support/manual/r14/toolbox/wavelet/ref_ope2.shtml