Computer Vision

Computational Photography TOC {:toc} Basically, enhance image by computation! Intersection of 3 fields Optics Vision Graphics Majorly two kinds of work Co-design camera and image processing (optics + vision) Use Vision to help Graphics to help generate better image faster! CG2REAL CG rendering is very computational intensive!

Feb 27, 2020

Motivation This is a brief analytical note about how physical self movement of eye / camera will induce optic flow in a static environment. And then discuss how a system can separate these two components instantaneously.

Feb 17, 2020

Stereo Basic Stereo algorithm can be formulated as Markov Random Field. Thus Methods in MRF inference could all be used. Prior Planar Prior Natural scene is usually piece-wise! How to impose this idea to depth map?

Feb 4, 2020

Semantics Vision Task Note semantics and geometric reasoning is conceptually similar to each other Stereo and Optical flow is about finding correspondence / matches. Object recognition in some sense is finding correspondence w.r.t. a template, and make the template match the observation. Semantic Vision before CNN So ancient semantic detector works like this

Feb 4, 2020

TOC {:toc} Continuing Image Prior and Generative Model . Probabilistic Graphic Model comes into scene, when we want to model and deal with some complex distribution over many variables. When we start to add structure into the model, not everything depend on everything, then the dependency relationship among variables emerges as a graph structure.

Jan 28, 2020

Note on Advanced Computer Vision This is the course note for Advanced Computer Vision Class (CS 659a) These are links to notes for individual modules and specific domain notes. Basic Computer Vision

Jan 20, 2020

Image Prior: Modeling Spatial Relaionship Materials: https://www.cse.wustl.edu/~ayan/courses/cse659a/lec1.html#/ TOC {:toc} This is the basis for most further applications We need Regularizer for a spatial configuration $$\hat X=\arg\min_X \phi(X)+R(X)\\$$This could be interpreted in a Bayesian way,

Jan 14, 2020

Understanding of Adversarial Attack Why we should care adversarial examples? Why they are surprising? Adversarial example and feature visualization are related intrinsically. Basic algorithm underlying both is to generate image that activate a unit most. For Adversarial attack, the point is to change the image in an in-perceptible way but change the network output dramatically. On the other hand the feature visualization is to generate a perceptually meaningful pattern that represent a natural image that the unit like.

Jan 7, 2020

Computer Graphics environment in Matlab @(515.1 Computer Graphics)[matlab] Recently, we are using matlab to do computer vision experiments. Thus this note introduces some function controlling the elementary graphics environment. For Computer Graphics, the basic 3 components are

Nov 14, 2019

Note on Local Feature Descriptors Before the advent of convolutional neural network, many techniques to represent and detect local features has been invented. As lower level feature detector, many of them are strongly mathematically motivated. Some are still used in some Computer Vision tasks as preprocessing step.

Nov 4, 2019