论文标题
在视频游戏中自动化伪影检测
Automating Artifact Detection in Video Games
论文作者
论文摘要
尽管游戏硬件和软件取得了进步，但游戏玩法通常会被图形错误，故障和屏幕伪像所污染。这项概念验证研究提出了一种机器学习方法，用于自动检测视频游戏中图形损坏。基于代表性屏幕损坏示例的样本，该模型能够以合理的精度识别10个最常见的屏幕伪像。数据的特征表示包括离散的傅立叶变换，定向梯度的直方图和图形拉普拉斯人。这些功能的各种组合用于训练机器学习模型，以识别单个图形损坏的单个类别，然后将其组装成单个混合专家“ Ensemble”分类器。合奏分类器在Holdout测试集上进行了测试，并且在以前见过的比赛中的精度为84％，在从未见过的比赛中获得了69％的精度。
In spite of advances in gaming hardware and software, gameplay is often tainted with graphics errors, glitches, and screen artifacts. This proof of concept study presents a machine learning approach for automated detection of graphics corruptions in video games. Based on a sample of representative screen corruption examples, the model was able to identify 10 of the most commonly occurring screen artifacts with reasonable accuracy. Feature representation of the data included discrete Fourier transforms, histograms of oriented gradients, and graph Laplacians. Various combinations of these features were used to train machine learning models that identify individual classes of graphics corruptions and that later were assembled into a single mixed experts "ensemble" classifier. The ensemble classifier was tested on heldout test sets, and produced an accuracy of 84% on the games it had seen before, and 69% on games it had never seen before.
