In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available.
Automatic annotation of visual deep neural networks
College of Mathematics and Computer Science, Fuzhou University（福州大学数学与计算机科学学院）；Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing（福建省网络计算与智能信息处理重点实验室）
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans
Volume Graphics GmbH（Volume Graphics有限公司）；University Medical Center Mannheim, Heidelberg University（海德堡大学曼海姆大学医学中心）；Heidelberg University（海德堡大学）；University of Padova（帕多瓦大学）
Automatic Identification and Description of Jewelry Through Computer Vision and Neural Networks for Translators and Interpreters
Department of Computer Science, University of Córdoba（计算机科学系，科尔多瓦大学）；Economics, Engineering, Society and Business Organization, University of Tuscia（经济学、工程学、社会与商业组织，图斯卡大学）；Department of Social Sciences, Translation and Interpreting, University of Córdoba（社会科学系，翻译与口译，科尔多瓦大学）
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory
University of Naples Federico II（那不勒斯费德里科二世大学）；University of Maryland（马里兰大学）；University of Salzburg（萨尔茨堡大学）
欧洲航天局（ESA）将地球观测（EO）2级产品定义为针对几何、大气、邻接和地形效应进行校正的多光谱（MS）图像，并与场景分类图（SCM）叠加，其图例包括云和云阴影等质量层。欧空局的EO 2级产品从来没有在地面部分系统地产生过。为了填补从EO大数据到ESA EO 2级产品的信息空白，通过独立方式对年度网络陆地卫星数据（WELD）图像合成时间进行了卫星图像自动制图器（SIAM）轻量级计算机程序的原始第4阶段验证（Val）-SIAM的核心是一个基于先验知识的决策树，用于MS反射空间超多面体化为静态颜色名称，近年来在文献中提出。为了便于阅读，本文分为两部分。本第一部分理论提供了认知科学中先验颜色命名的多学科背景，从语言学到计算机视觉。为了应付字典MS颜色名称和土地覆盖类名称不一致，必须协调，提出了一个原始的混合指南，以确定一个分类变量对关系。一个原始的定量措施的分类变量对关联也提出了。随后的第2部分验证讨论了第4阶段验证结果，这些结果是通过原始方案收集的，用于墙到墙专题地图质量评估，而不进行抽样，其中测试和参考地图图例可能不同。结论是，SIAM-WELD地图实例化的2级SCM产品，其图例是粮农组织土地覆盖分类系统的4级分类法，处于二分阶段1级植被/非植被和2级陆生/水生。
Patch-Based Encoder-Decoder Architecture for Automatic Transmitted Light to Fluorescence Imaging Transition: Contribution to the LightMyCells Challenge
AGH University of Krakow（克拉科夫AGH科技大学）；University of Applied Sciences Western Switzerland（瑞士西应用科学大学）；University of Geneva（日内瓦大学）
