Differently from computer vision systems which require explicit supervision,
humans can learn facial expressions by observing people in their environment.
In this paper, we look at how similar capabilities could be developed in
machine vision. As a starting point, we consider the problem of relating facial
expressions to objectively measurable events occurring in videos. In
particular, we consider a gameshow in which contestants play to win significant
sums of money. We extract events affecting the game and corresponding facial
expressions objectively and automatically from the videos, obtaining large
quantities of labelled data for our study. We also develop, using benchmarks
such as FER and SFEW 2.0, state-of-the-art deep neural networks for facial
expression recognition, showing that pre-training on face verification data can
be highly beneficial for this task. Then, we extend these models to use facial
expressions to predict events in videos and learn nameable expressions from
them. The dataset and emotion recognition models are available at
this http URL

本文讨论如何使用机器视觉学习人脸表情，使用深度神经网络识别人脸表情，并将其应用于视频中推断事件和学习表情，达到了最新的效果。

通过观看电视学习面部表情

Learning Grimaces by Watching TV

This is a preliminary version of visual interpretation integrating multiple
sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a
thorough integration of hierarchical Bayesian inference with comprehensive
physical representation of objects and their relations in a system for
reasoning with geometry, surface materials and sensor models in machine vision.
Bayesian inference provides a framework for accruing_ probabilities to rank
order hypotheses.

一篇关于基于贝叶斯推断以及多种传感器协同作用的机器视觉系统中，对物体和其属性进行全面分析和理解的研究论文。

基于模型的机器视觉中的贝叶斯推断

Bayesian Inference in Model-Based Machine Vision

This paper deals with projective shape analysis, which is a study of finite
configurations of points modulo projective transformations. The topic has
various applications in machine vision. We introduce a convenient projective
shape space, as well as an appropriate coordinate system for this shape space.
For generic configurations of k points in m dimensions, the resulting
projective shape space is identified as a product of k-m-2 copies of axial
spaces RP^m. This identification leads to the need for developing multivariate
directional and multivariate axial analysis and we propose parametric models,
as well as nonparametric methods, for these areas. In particular, we
investigate the Frechet extrinsic mean for the multivariate axial case.
Asymptotic distributions of the appropriate parametric and nonparametric tests
are derived. We illustrate our methodology with examples from machine vision.

本文介绍了一个便捷的投影形状空间及其适当的坐标系，用于解决有限点配置的项目形状分析问题。我们开发了多元方向和多元轴线分析的参数模型和非参数方法，且导出了相关参数和非参数检验的渐近分布，然后以机器视觉为例说明了我们的方法。