基于视觉暗示的战争纪念建筑设计手法探究

当代建筑中，视觉暗示的设计手法对纪念建筑的营造有着至关重要的作用。现试图从建筑学的视角出发，以当代战争纪念建筑实际案例为研究对象，对战争纪念建筑的设计方法展开讨论。首先着重分析视觉暗示与格式塔心理学的关系，其次阐述视觉暗示给参观者心理带来的微妙变化，之后探讨视觉暗示对空间纪念效果造成的影响，最终归纳为可操作的设计策略：图形暗示、内容暗示、秩序暗示，并明确其如何相互影响。立足于格式塔心理学及心理暗示的理论基础，是对纪念建筑设计方法的补充，亦可为今后的战争纪念馆设计提供启示。     

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.     

平面设计中视觉与触觉的审美构建

目的探索视觉与触觉在平面设计中的多元化呈现及应用路径。方法立足于平面设计中的审美与功能的双向视角,从视觉与触觉对平面设计的艺术潜力与价值进行挖掘,总结两者在平面设计中必须遵循的秩序性、冲击感和功能性三大原则,并在此基础上深度探寻应用领域,总结两者共同作用下的平面设计在合理构图、色彩搭配、材质升级、强化交互等方面的创新与突破,展示其中的典型案例,总结更高层面的审美价值的表达与创作。结论良好的触觉感知及视觉冲击在平面设计的发展壮大过程中发挥着重要作用,在有效提升平面设计水平的基础上,深度挖掘出平面设计本身的情趣,最大限度地满足了人们的审美需求,也找到了彼此融合与发展的共促路径。     

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.     

Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one. This process can take up a large amount of resource real estate, and as we have laid out in this paper, by using a Model consisting of various Neural Networks trained on a custom dataset. It can be automated into assisting designers, allowing them to focus on the other more complicated parts of the system they are designing by quickly generating what would rather be straightforward busywork. Over the past 20 years, the boom in how much the internet is used and the sheer volume of pages on it demands a high level of work and time to create them. For the efficiency of the process, we proposed a multi-model-based architecture on image captioning, consisting of Convolutional neural network (CNN) and Long short-term memory (LSTM) models. Our proposed approach trained on our custom-made database can be automated into assisting designers, allowing them to focus on the other more complicated part of the system. We trained our model in several batches over a custom-made dataset consisting of over 6300 files and were finally able to achieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50 hand-drawn images at 87.86%     

基于GL0816传感器的高速线阵CMOS相机系统设计

为了满足印刷等高端工业检测中物体快速运动,需要大幅面、高行频、高分辨率图像采集等要求,研发了一款微米级高分辨率、高速线阵工业相机。首先,介绍了高行频、高分辨率国产CMOS图像传感器GL0816的功能与特点。然后,基于该芯片设计了一套高速大幅面高分辨率线阵工业相机系统,该系统采用FPGA作为整个系统的控制核心,以DDR3LSDRAM作为图像缓存器,以GigE vision2.0协议为输出标准,以SFP+作为高速图像输出接口。最后,搭建相机系统测试环境,对所设计的相机进行系统测试。结果表明:该相机系统行分辨率为8 192,可连续采集2 000行作为一帧图像输出,行频为50kHz,动态范围为57.32dB,信噪比为40.95dB,具有实时图像采集功能。该相机系统具有大幅面、高帧频、高分辨率、高信噪比、宽动态范围等优点,适用于印刷检测行业快速运动目标捕获成像及图像实时显示。     

基于机器视觉的玻璃缺陷检测

随着科学技术的进步,高端显示屏产品对平板玻璃的质量要求越来越高,玻璃的表面缺陷检测技术也因此备受关注。传统的人眼检测方法工作量大且准确率低,已经无法满足生产实际要求。研究了一种基于机器视觉的玻璃质量检测系统,采用先进的CCD成像技术和背光式照明获取图像,用MATLAB图像处理工具箱对采集到的图像进行灰度值化、滤波降噪和阈值分割处理,实现对缺陷区域的特征提取和识别。最后用BP神经网络对玻璃表面的三种缺陷进行分类,该神经网络识别的平均误差率为9.84%,表明此检测方法具有一定的应用价值。     

A tutorial on the segmentation of metallographic images: Taxonomy,new MetalDAM dataset,deep learning-based ensemble model,experimental analysis and challenges

Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.     

Visual aesthetic perception of handwoven cotton fabrics

We revealed the factors that govern the visual aesthetic perception of handwoven fabrics by performing two psychological experiments to determine the effects of cotton hand-spun yarn on the perceptual and cognitive responses to woven fabrics. First, a free sorting task followed by multidimensional scaling and hierarchical cluster analysis explored the effect of yarn type (hand-spun, machine-spun, and slub-yarn) on the woven fabric. Visual categorization of the fabrics depended on yarn type. Second, functional and aesthetic perceptions that produced the differences among yarn types were investigated by semantic differential scaling method that collected subjects’ ratings for the fabrics from 17 bipolar adjective pairs. Generalized linear mixed-effects modeling revealed the association between the yarn type and perception expressed by functional and aesthetic words. Visual aesthetic perception of fabrics was also affected by the consumers’ background. Our method and findings can be applied to examine the visual aesthetic of other textile products.