13 items tagged
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.
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.
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!
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?
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
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.
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
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,
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.
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.
Deep Unsupervised Learning Lecture notes from Berkeley-cs294 https://sites.google.com/view/berkeley-cs294-158-sp19/home Lec 1 Category Generative Model Non-generative representation learning Motivation for Unsupervised Learning Application Generate/predict fancy samples Detect Anomaly / deviation from distribution Which human can do quite well without training Data Compression (because of predictability) Use the inner representation to do other tasks! Pre-training Type of Question Core Question: Modeling a Distribution
Note on Computer Vision Lecture Notes from CS559. TOC {:toc} Lec01: Image Formation In principle, digital images are formed by measuring energy (counting photons) over an array. But several pre-processing steps makes it interesting and relevant to processing.
Note on Network Commnunication TOC {:toc} General Introduction Network connects devices to transfer data / information. LAN and WAN LAN: Localized network, connected machines in the same area. WAN: Wide area, Internet is the largest WAN! The 2 types are less distinct now, they are blurred because of cellular tech and wireless network.