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
Notes on Visual Imagery Definition: Recreate the sensory world in mind in absense of physical stimuli. Usage in daily cognition Closely related to memory. We solve some cognitive task by recreating the visual scene in mind and examine the mind picture! Some tasks are memory about spatial some are feature memory! Usage in creative work Provides another way of thinking, other than verbal and logical induction. Intuition Characteristics of Imagery Is the representation spatial or propositional?
Nov 5, 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
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
Note on Categorization and Concepts From lecture notes from Science of Behavior Configuration The relative configuration of a single elements Example: Face What defines a face? Components Essential feature Configural property Relative Invariance to many change in Stimuli
Oct 24, 2019
Note on CNN Interpretability 2 major way of interpreting CNN Feature visualization: See what a hidden neuron is interested in Attribution: See what part of image activate a filter or detector Activation Atlas These works try to find a tool kits for visualizing DeepNN and building up a human-computer interface of DeepNN.
Oct 4, 2019