19.01.2015 • Views

Share Embed Flag

Midterm Topics - Stanford Vision Lab

ePAPER READ

DOWNLOAD ePAPER

vision.stanford.edu

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

START NOW

∙ RANSAC, pros and cons

3. Segmentation and Clustering:

∙ Gestalt principles and cues

∙ k-means clustering

∙ mean-shift segmentation

∙ Mixture of Guassians, Expectation Maximization (EM) algorithm

– Be able to compare and contrast the above methods (theory and understand

when each should be used)

∙ Spectral clustering

∙ Normalized cut (theory and practical approximation, how to set up the

problem, and obtain eigensolutions)

3 Camera models

∙ Pinhole Camera Geometry

∙ Homogeneous Coordinates

∙ Different camera models: weak, affine, projective

– How are parallel lines mapped

∙ Vanishing points and lines

∙ Intrinsic and extrinsic parameters (definitions, properties)

∙ Affine camera model (properties)

∙ Camera calibration

∙ Epipolar geometry

– Essential matrix

– Fundamental matrix

– 8-pt algorithm

– Rectification: what is it why is it useful

∙ Stereo vision

– Correspondence

– Reconstruction

∙ Structure from motion

– Factorization algorithm

GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts

Dalal & Triggs, 2005 - Stanford Vision Lab

The Parahippocampal Place Area: A Cortical Representation of the ...

Lecture 1: What is CV? - Stanford Vision Lab

JIA LI - Stanford Vision Lab - Stanford University

Lecture 18: Human Motion Recognition - Stanford Vision Lab

Simultaneous Image Classification and Annotation - Princeton ...

Arbelaez et al, 2011 - Stanford Vision Lab - Stanford University

A scene-centered representation of gist - Stanford Vision Lab

Ultra-Rapid Scene Processing - Stanford Vision Lab

What Does Classifying More Than 10,000 Image Categories Tell Us?

Lecture 1 - Stanford Vision Lab; Prof. Fei-Fei Li

∙ RANSAC, pros and cons 3. Segmentation and Clustering: ∙ Gestalt principles and cues ∙ k-means clustering ∙ mean-shift segmentation ∙ Mixture of Guassians, Expectation Maximization (EM) algorithm – Be able to compare and contrast the above methods (theory and understand when each should be used) ∙ Spectral clustering ∙ Normalized cut (theory and practical approximation, how to set up the problem, and obtain eigensolutions) 3 Camera models ∙ Pinhole Camera Geometry ∙ Homogeneous Coordinates ∙ Different camera models: weak, affine, projective – How are parallel lines mapped ∙ Vanishing points and lines ∙ Intrinsic and extrinsic parameters (definitions, properties) ∙ Affine camera model (properties) ∙ Camera calibration ∙ Epipolar geometry – Essential matrix – Fundamental matrix – 8-pt algorithm – Rectification: what is it why is it useful ∙ Stereo vision – Correspondence – Reconstruction ∙ Structure from motion – Factorization algorithm 2

4 Technical tools ∙ Least square, langrangian, system of linear equations (Ax=b) ∙ Linear algebra ∙ Classification: boosting ∙ Unsupervised clustering – EM – Eigensolutions ∙ Correlation, convolution ∙ Image normalization 3

Page 1: CS 231A Computer Vision (Y2011-2012

Midterm Topics - Stanford Vision Lab

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?