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No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis Research Article

Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao,hyao@usst.edu.cn,xudong@zjcc.org.cn,yaojc@zjcc.org.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12, Pages 1551-1684 doi: 10.1631/FITEE.2000716

Abstract: Noise is the most common type of image distortion affecting human visual perception. In this paper, we propose a no-reference image quality assessment (IQA) method for noisy images incorporating the features of entropy, gradient, and . Specifically, image is conducted in the discrete cosine transform domain based on skewness invariance. In the principal component analysis domain, feature is obtained by statistically counting the significant differences between images with and without noise. In addition, both the consistency between the entropy and features and the subjective scores are improved by combining them with the gradient coefficient. is applied to map all extracted features into an integrated scoring system. The proposed method is evaluated in three mainstream databases (i.e., LIVE, TID2013, and CSIQ), and the results demonstrate the superiority of the proposed method according to the Pearson linear correlation coefficient which is the most significant indicator in IQA.

Keywords：噪声图像质量评价；噪声估计；峰度；人类视觉系统；支持向量回归

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1000× Faster Camera and Machine Vision with Ordinary Devices Article

Tiejun Huang, Yajing Zheng, Zhaofei Yu, Rui Chen, Yuan Li, Ruiqin Xiong, Lei Ma, Junwei Zhao, Siwei Dong, Lin Zhu, Jianing Li, Shanshan Jia, Yihua Fu, Boxin Shi, Si Wu, Yonghong Tian

Engineering 2023, Volume 25, Issue 6, Pages 110-119 doi: 10.1016/j.eng.2022.01.012

Abstract:

In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we present vidar, a bit sequence array where each bit represents whether the accumulation of photons has reached a threshold, to record and reconstruct the scene radiance at any moment. By employing only consumer-level complementary metal-oxide-semiconductor (CMOS) sensors and integrated circuits, we have developed a vidar camera that is 1000× faster than conventional cameras. By treating vidar as spike trains in biological vision, we have further developed a spiking neural network (SNN)-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1000× faster than human vision. We demonstrate the utility of the vidar camera and the super vision system in an assistant referee and target pointing system. Our study is expected to fundamentally revolutionize the image and video concepts and related industries, including photography, movies, and visual media, and to unseal a new SNN-enabled speed-free machine vision era.

Keywords： Vidar camera Spiking neural networks Super vision system Full-time imaging

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Visual Inspection Technology and its Application

Ye Shenghua,Zhu Jigui,Wang Zhong,Yang Xueyou

Strategic Study of CAE 1999, Volume 1, Issue 1, Pages 49-52

Abstract:

Visual inspection, especially, the active visual inspection and passive visual inspection based on triangulation method has advantages of non-contact, rapid speed, flexibility, etc. Visual inspection is a advanced inspection technology, satisfies modern manufacturing demands. This paper discusses the principle of visual inspection, studies several developed applied visual inspection systems, these systems demostrate wide application foreground of visual inspection from different points of view.

Keywords： active visual inspection passive visual inspection inspection system modern manufacturing

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Performance analysis of visualmarkers for indoor navigation systems Article

Gaetano C. LA DELFA,Salvatore MONTELEONE,Vincenzo CATANIA,Juan F. DE PAZ,Javier BAJO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8, Pages 730-740 doi: 10.1631/FITEE.1500324

Abstract: The massive diffusion of smartphones, the growing interest in wearable devices and the Internet of Things, and the exponential rise of location based services (LBSs) have made the problem of localization and navigation inside buildings one of the most important technological challenges of recent years. Indoor positioning systems have a huge market in the retail sector and contextual advertising; in addition, they can be fundamental to increasing the quality of life for citizens if deployed inside public buildings such as hospitals, airports, and museums. Sometimes, in emergency situations, they can make the difference between life and death. Various approaches have been proposed in the literature. Recently, thanks to the high performance of smartphones’ cameras, marker-less and marker-based computer vision approaches have been investigated. In a previous paper, we proposed a technique for indoor localization and navigation using both Bluetooth low energy (BLE) and a 2D visual marker system deployed into the floor. In this paper, we presented a qualitative performance evaluation of three 2D visual markers, Vuforia, ArUco marker, and AprilTag, which are suitable for real-time applications. Our analysis focused on specific case study of visual markers placed onto the tiles, to improve the efficiency of our indoor localization and navigation approach by choosing the best visual marker system.

Keywords： Indoor localization Visual markers Computer vision

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Miniaturized five fundamental issues about visual knowledge Perspectives

Yun-he Pan,panyh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2040000

Abstract: 认知心理学早已指出，人类知识记忆中的重要部分是视觉知识，被用来进行形象思维。因此，基于视觉的人工智能（AI）是AI绕不开的课题，且具有重要意义。本文继《论视觉知识》一文，讨论与之相关的5个基本问题：（1）视觉知识表达；（2）视觉识别；（3）视觉形象思维模拟；（4）视觉知识的学习；（5）多重知识表达。视觉知识的独特优点是具有形象的综合生成能力，时空演化能力和形象显示能力。这些正是字符知识和深度神经网络所缺乏的。AI与计算机辅助设计/图形学/视觉的技术联合将在创造、预测和人机融合等方面对AI新发展提供重要的基础动力。视觉知识和多重知识表达的研究是发展新的视觉智能的关键，也是促进AI 2.0取得重要突破的关键理论与技术。这是一块荒芜、寒湿而肥沃的“北大荒”，也是一块充满希望值得多学科合作勇探的“无人区”。

Keywords：视觉知识表达；视觉识别；视觉形象思维模拟；视觉知识学习；多重知识表达

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Three-dimensional shape space learning for visual concept construction: challenges and research progress Perspective

Xin TONG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9, Pages 1290-1297 doi: 10.1631/FITEE.2200318

Abstract: Human beings can easily categorize three-dimensional (3D) objects with similar shapes and functions into a set of “visual concepts” and learn “visual knowledge” of the surrounding 3D real world (). Developing efficient methods to learn the computational representation of the visual concept and the visual knowledge is a critical task in artificial intelligence (). A crucial step to this end is to learn the shape space spanned by all 3D objects that belong to one visual concept. In this paper, we present the key technical challenges and recent research progress in 3D shape space learning and discuss the open problems and research opportunities in this area.

Keywords：视觉概念；视觉知识；三维几何学习；三维形状空间；三维结构

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A quantitative attribute-based benchmark methodology for single-target visual tracking Article

Wen-jing KANG, Chang LIU, Gong-liang LIU

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3, Pages 405-421 doi: 10.1631/FITEE.1900245

Abstract: In the past several years, various visual object tracking benchmarks have been proposed, and some of them have been used widely in numerous recently proposed trackers. However, most of the discussions focus on the overall performance, and cannot describe the strengths and weaknesses of the trackers in detail. Meanwhile, several benchmark measures that are often used in tests lack convincing interpretation. In this paper, 12 frame-wise visual attributes that reflect different aspects of the characteristics of image sequences are collated, and a normalized quantitative formulaic definition has been given to each of them for the first time. Based on these definitions, we propose two novel test methodologies, a correlation-based test and a weight-based test, which can provide a more intuitive and easier demonstration of the trackers’ performance for each aspect. Then these methods have been applied to the raw results from one of the most famous tracking challenges, the Video Object Tracking (VOT) Challenge 2017. From the tests, most trackers did not perform well when the size of the target changed rapidly or intensely, and even the advanced deep learning based trackers did not perfectly solve the problem. The scale of the targets was not considered in the calculation of the center location error; however, in a practical test, the center location error is still sensitive to the targets’ changes in size.

Keywords： Visual tracking Performance evaluation Visual attributes Computer vision

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Visual commonsense reasoning with directional visual connections Research Articles

Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000722

Abstract: To boost research into cognition-level visual understanding, i.e., making an accurate inference based on a thorough understanding of visual details, (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers, VCR requires models to select not only the correct answers, but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity, which is helpful in solving specific cognition tasks. Inspired by this idea, we propose a to achieve VCR by dynamically reorganizing the that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically, we first develop a GraphVLAD module to capture to fully model visual content correlations. Then, a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally, based on the output of , we propose to infer answers and rationales, which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.

Keywords：视觉常识推理；有向连接网络；视觉神经元连接；情景化连接；有向连接

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Visual knowledge: an attempt to explore machine creativity Perspectives

Yueting Zhuang, Siliang Tang,yzhuang@zju.edu.cn,siliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2100116

Abstract: 长期以来困扰人工智能领域的一个问题是：人工智能是否具有创造力，或者说，算法的推理过程是否可以具有创造性。本文从思维科学的角度探讨人工智能创造力的问题。首先，列举形象思维推理的相关研究；然后，重点介绍一种特殊的视觉知识表示形式，即视觉场景图；最后，详细介绍视觉场景图构造问题与潜在应用。所有证据表明，视觉知识和视觉思维不仅可以改善当前人工智能任务的性能，而且可以用于机器创造力的实践。

Keywords：思维科学；形象思维推理；视觉知识表达；视觉场景图

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On the Future: A Keynote Address

Martin John Rees

Engineering 2020, Volume 6, Issue 2, Pages 110-114 doi: 10.1016/j.eng.2019.12.009

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The Entropy Perspective on Human Illness and Aging

Zhiguo Wang

Engineering 2022, Volume 9, Issue 2, Pages 22-26 doi: 10.1016/j.eng.2021.08.014

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Science and Technology in Human's Living and Survival in the 21st Century

Jiang Zhenghua

Strategic Study of CAE 2002, Volume 4, Issue 4, Pages 1-11

Abstract:

This article, from the angle of management, reviews the world history over the past century, studies the prospective progress in the new century, and discusses ways for promoting world peace and cooperation and realizing common development.

Keywords： 21st century progress in science and technology human's living and survival

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Engineering for Human Security and Well-Being

Hideaki Koizumi

Engineering 2015, Volume 1, Issue 3, Pages 282-287 doi: 10.15302/J-ENG-2015066

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Machine Learning Produces Superhuman Chip Designs

Robert Pollie,

Engineering 2022, Volume 10, Issue 3, Pages 7-9 doi: 10.1016/j.eng.2022.01.006

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On visual knowledge Perspective

Yun-he PAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8, Pages 1021-1025 doi: 10.1631/FITEE.1910001

Abstract: 提出“视觉知识”概念。视觉知识是知识表达的一种新形式. 它与迄今为止人工智能（AI）所用知识表达方法不同. 其中视觉概念具有典型（prototype）与范畴结构、层次结构与动作结构等要素. 视觉概念能构成视觉命题，包括场景结构与动态结构，视觉命题能构成视觉叙事。指出重构计算机图形学成果可实现视觉知识表达及其推理与操作，重构计算机视觉成果可实现视觉知识学习。实现视觉知识表达、推理、学习和应用技术将是AI 2.0取得突破的重要方向之一。

Keywords： None

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Title Author Date Type Operation

No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis

Heng Yao, Ben Ma, Mian Zou, Dong Xu, Jincao Yao,hyao@usst.edu.cn,xudong@zjcc.org.cn,yaojc@zjcc.org.cn

Journal Article

1000× Faster Camera and Machine Vision with Ordinary Devices

Tiejun Huang, Yajing Zheng, Zhaofei Yu, Rui Chen, Yuan Li, Ruiqin Xiong, Lei Ma, Junwei Zhao, Siwei Dong, Lin Zhu, Jianing Li, Shanshan Jia, Yihua Fu, Boxin Shi, Si Wu, Yonghong Tian

Journal Article

Visual Inspection Technology and its Application

Ye Shenghua,Zhu Jigui,Wang Zhong,Yang Xueyou

Journal Article

Performance analysis of visualmarkers for indoor navigation systems

Gaetano C. LA DELFA,Salvatore MONTELEONE,Vincenzo CATANIA,Juan F. DE PAZ,Javier BAJO

Journal Article

Miniaturized five fundamental issues about visual knowledge

Yun-he Pan,panyh@zju.edu.cn

Journal Article

Three-dimensional shape space learning for visual concept construction: challenges and research progress

Xin TONG