Computer Science

Note on Photometric Reasoning Shape $\hat n$, lighting $l$, reflectance $\rho$ affect image appearance $I$. Can we infer them back? $$ I=\rho<\hat n,l> $$ How much does shading and photometric effects tell us about shape, in natural settings.

Mar 17, 2020

Note on Hardware Based Computational Photography Now we have far more computational power than before! Besides, many images will go through complex algorithms as postprocessing. But we can also optimize camera measurement, so that results look even better.

Feb 27, 2020

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

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

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

Note on Patch Based Shape Interpretation These are 2 related papers both employ a patch based approach to tackle shape from shading problem. Typically patches have simpler appearance, thus they are easier to collect the statistics on or fit a model on. The spirit is to find a local explanation for patches in an image. However, as there will be ambiguity in local patches, the algorithm should not over-commiting to any one of the explanation and keep the distribution of possible shapes. And then take these local shape proposals and see which can stitch together and make sense globally.

Nov 2, 2019