Discrete Inference and Learning in Artificial Vision

Li Yin
Li Yin
Jul 27, 2017 · 2 min read

This article will be about the probability graphical models. At first, I will just gain resources link:

  1. A survey paper:

Markov Random Field modeling, inference & learning in computer vision

& image understanding: A survey

2. Nikos Paragios, Professor of Applied Mathematics & Computer Science

where the energy (or cost, objective) function E(x, D; w) can be regarded as a quality measure of a parameter configuration x in the solution space given the observed data D, and w denotes the model parameters

visual perception involves three main tasks: modeling, inference and learning. The modeling has to accomplish: (i) the choice of an appropriate representation of the solution using a tuple of variables x; and (ii) the design of the class of energy functions E(x, D; w) which can correctly mea- sure the connection between x and D. The inference has to search for the configuration of x leading to the optimum of the energy function, which corresponds to the solution of the original problem. The learning aims to select the optimal model parameters w based on the training data.

3. Conditional Random Fields

Fully-connected CRFs: this includes codes in python. This could be a good start to combine CRF to TensorFlow

Python Package

Deep Neural Network + CRF as post-processing in TensorFlow

Li’s Computer Vision Blogs

Research stories, shares, and learning on the topic of Computer Vision

    Li Yin

    Written by

    Li Yin

    Computer scientist. ✍️A book in progress@https://github.com/liyin2015/Algorithms-and-Coding-Interviews.

    Li’s Computer Vision Blogs

    Research stories, shares, and learning on the topic of Computer Vision

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