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Key technology for digital twins in the architecture, engineering, and construction industry: new advances in point cloud semantic segmentation algorithms for buildings

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JOURNAL OF ELECTRONIC IMAGING 2024年第5期33卷

作者： Ai, Da Qin, Siyu Gao, Shansong Yuan, Hui Li, Ying Xian Key Lab Image Proc Technol & Applicat Publ S Xian Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China Int Joint Res Ctr Wireless Commun & Informat Proc Xian Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

In the architecture, engineering, and construction (AEC) industry, point cloud semantic segmentation provides comprehensive and accurate data support for building information modeling (BIM) and is one of the key technologies for building digital twins. However, the complexity and diversity of building semantic categories and the incompleteness of the current building point cloud datasets for training make deep learning-based semantic segmentation of building point clouds still a challenging task. We systematically summarize the existing classical point cloud semantic segmentation algorithms and further compare and analyze the state-of-the-art point cloud semantic segmentation algorithms of buildings according to two application scenarios: outdoor and indoor. Second, we summarize the point cloud datasets applicable to the AEC field and quantitatively analyze and compare the semantic segmentation performance of various algorithms according to different application scenarios. Finally, we explore the research directions and application prospects of point cloud semantic segmentation algorithms in the field of AEC, encompassing data acquisition and processing. scene detection and reconstruction, digital twin, etc. (c) 2024 SPIE and IS&T

关键词： building point clouds semantic segmentation deep learning digital twin

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Optimizing image Feature Extraction with Convolutional Neural Networks for Chicken Meat Detection Applications

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APPLIED SCIENCES-BASEL 2025年第2期15卷 733-733页

作者： Mjahad, Azeddine Polo-Aguado, Antonio Llorens-Serrano, Luis Rosado-Munoz, Alfredo Univ Valencia Sch Engn GDDP Dept Elect Engn Burjassot 46100 Valencia Spain PYGSA Sistema & Aplicac Dept Desarrollo Software Poligono Ind Fuente Jarro Calle Villa BilbaoSL 10 Paterna 46988 Valencia Spain

The food industry continuously prioritizes methods and technologies to ensure product quality and safety. Traditional approaches, which rely on conventional algorithms that utilize predefined features, have exhibited limitations in representing the intricate characteristics of food items. Recently, a significant shift has emerged with the introduction of convolutional neural networks (CNNs). These networks have emerged as powerful and versatile tools for feature extraction, standing out as a preferred choice in the field of deep learning. The main objective of this study is to evaluate the effectiveness of convolutional neural networks (CNNs) when applied to the classification of chicken meat products by comparing different image preprocessing.approaches. This study was carried out in three phases. In the first phase, the original images were used without applying traditional filters or color modifications, processing.them solely with a CNN. In the second phase, color filters were applied to help separate the images based on their chromatic characteristics, while still using a CNN for processing. Finally, in the third phase, additional filters, such as Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and saliency, were incorporated to extract complementary features from the images, without discontinuing the use of a CNN for processing. Experimental images, sourced from the Pygsa Group databases, underwent preprocessing.using these filters before being input into a CNN-based classification architecture. The results show that the developed models outperformed conventional methods, significantly improving the ability to differentiate between chicken meat types, such as yellow wing, white wing, yellow thigh, and white thigh, with the training accuracy reaching 100%. This highlights the potential of CNNs, especially when combined with advanced architectures, for efficient detection and analysis of complex food matrices. In conclusion, these techniques c

关键词： chicken meat food quality control deep learning in food analysis convolutional neural network feature extraction image preprocessing data augmentation localization image filters

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Deep Learning-Assisted Droplet digital PCR for Quantitative Detection of Human Coronavirus

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BIOCHIP JOURNAL 2023年第1期17卷 112-119页

作者： Lee, Young Suh Choi, Ji Wook Kang, Taewook Chung, Bong Geun Sogang Univ Dept Mech Engn Seoul 04107 South Korea Sogang Univ Dept Chem & Biomol Engn Seoul 04107 South Korea Sogang Univ Inst Integrated Biotechnol Seoul 04107 South Korea

Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing.images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology.

关键词： ddPCR image processing Deep learning Mask R-CNN GMM clustering

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Towards Robust and Generalizable Lensless Imaging With Modular Learned Reconstruction

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2025年 11卷 213-227页

作者： Bezzam, Eric Perron, Yohann Vetterli, Martin Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1015 Lausanne Switzerland Ecole Ponts ParisTech ENPC IMAGINE Res Grp F-77420 Champs Sur Marne France

Lensless cameras disregard the conventional design that imaging should mimic the human eye. This is done by replacing the lens with a thin mask, and moving image formation to the digital post-processing. State-of-the-art lensless imaging techniques use learned approaches that combine physical modeling and neural networks. However, these approaches make simplifying modeling assumptions for ease of calibration and computation. Moreover, the generalizability of learned approaches to lensless measurements of new masks has not been studied. To this end, we utilize a modular learned reconstruction in which a key component is a pre-processor prior to image recovery. We theoretically demonstrate the pre-processor's necessity for standard image recovery techniques (Wiener filtering and iterative algorithms), and through extensive experiments show its effectiveness for multiple lensless imaging approaches and across datasets of different mask types (amplitude and phase). We also perform the first generalization benchmark across mask types to evaluate how well reconstructions trained with one system generalize to others. Our modular reconstruction enables us to use pre-trained components and transfer learning on new systems to cut down weeks of tedious measurements and training. As part of our work, we open-source four datasets, and software for measuring datasets and for training our modular reconstruction.

关键词： Training Wiener filters Computational modeling Transfer learning Computer architecture Cameras Transformers Software Software measurement image reconstruction Lensless imaging modularity robustness generalizability programmable mask transfer learning

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Adaptive Filtering Method Based on Deviation Compensation in Signal processing

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Journal of The Institution of Engineers (India): Series B 2025年 1-12页

作者： Liu, Xia Wang, Kangyi Liu, Chongguang School of Innovation and Entrepreneurship North University of China Taiyuan030051 China Taiyuan Municipal Construction Group Co. LTD. Taiyuan030000 China

With the widespread application of computer processors, information processing.technologies are rich and diverse in the digital information age. As an important research hotspot in signal processing. adaptive filtering algorithms have multiple application branches in signal processing. This study uses a deviation compensation term to infer and improve traditional adaptive filtering algorithms, thereby automatically updating filter parameters. Combined with different pulse noise and input signal noise environments, the adaptive filtering algorithms and input signal noise are analyzed and verified. By comparing the mean square deviation and performance of different algorithms, the cross-correlation adaptive filtering algorithm with a step factor of 0.4 and a kernel width of 4 for deviation compensation had the best convergence performance. At the same time, the mean square deviation of its algorithm was below − 22, indicating the superiority of the cross-correlation adaptive filtering algorithm for deviation compensation. Finally, comparing the mean squared deviation of different algorithms, the normalized minimum mean squared algorithm based on the Sigmoid architecture had the best mean squared deviation of − 33.9. The minimum mean squared algorithm based on affine projection-Wu-Manber least mean square had the best mean squared deviation of − 20.04. This indicates the superiority of the minimum mean square adaptive filtering algorithm based on deviation compensation. This is to suppress input signal noise interference and provide theoretical basis and technical reference for signal processing.technology. © The Institution of Engineers (India) 2025.

关键词： Adaptive filtering

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Randomized quaternion tensor UTV decompositions for color image and color video processing

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PATTERN RECOGNITION 2025年 165卷

作者： Yang, Liqiao Miao, Jifei Jiang, Tai-Xiang Zhang, Yanlin Kou, Kit Ian Southwestern Univ Finance & Econ Res Inst Digital Econ & Interdisciplinary Sci Sch Comp & Artificial Intelligence Chengdu 611130 Peoples R China Univ Macau Fac Sci & Technol Dept Math Macau 999078 Peoples R China

In this paper, we propose novel quaternion matrix UTV (QUTV) and quaternion tensor UTV (QTUTV) decomposition methods, specifically designed for color image and video processing. We begin by defining both QUTV and QTUTV decompositions and provide detailed algorithmic descriptions. To enhance computational efficiency, we introduce randomized versions of these decompositions using random sampling from the quaternion normal distribution, which results in cost-effective and interpretable solutions. Extensive numerical experiments demonstrate that the proposed algorithms significantly improve computational efficiency while maintaining relative errors comparable to existing decomposition methods. These results underscore the strong potential of quaternion-based decompositions for real-world color image and video processing.applications. Theoretical findings further support the robustness of the proposed methods, providing a solid foundation for their widespread use in practice.

关键词： Quaternion UTV Randomized algorithm Quaternion tensor Color image processing Low-rank approximation

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Research and System Development of Ink Droplet Measurement Analysis Algorithm for High-Speed Inkjet System

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JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY 2025年第1期69卷

作者： Zhu, Yan Chen, Yajun Guo, Qiyang Mu, Chenyan Xian Univ Technol Fac Printing Packaging Engn & Digital Media Techno Xian 710054 Peoples R China

In response to the challenge of monitoring the quality of ink droplet injection in the field of digital inkjet printing, this study designs and implements a visual measurement system for ink droplets based on high-definition video image processing.technology. The aim is to provide a convenient and accurate method to alert users on time to the quality of ink droplet injection in inkjets. The system can capture and analyze the image of a sprayed ink droplet by an inkjet in real time, effectively monitoring and evaluating the quality of ink droplet injection. This study uses high-definition camera equipment to capture real-time images of ink droplets sprayed by an inkjet head. By using image processing.algorithms, the system can accurately extract key parameters such as the number, position, volume, and flight speed of ink droplets. Through detailed experimental verification, the algorithm and system developed by our research institute have demonstrated excellent performance in detecting ink droplet spray anomalies, achieving precise detection and evaluation of ink droplets. The ink droplet visual detection system can not only capture high-definition images of ink droplets in real time but also extract crucial information for quality evaluation, providing users with an accurate and reliable tool for evaluating the quality of ink droplets. Experimental results demonstrate that the proposed droplet visual inspection system significantly outperforms other systems, validating its effectiveness in droplet detection applications. The results of this study not only provide strong technical support for quality control of inkjet printing technology but also significantly improve traditional ink droplet detection methods through real-time monitoring and automated processing. This improves the efficiency and accuracy of inkjet printing and also greatly promotes the application of inkjet printing technology in various fields through innovative system applications, especially in hi

关键词： inkjet printing ink droplet image processing measurement system

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Enhanced three-dimensional imaging with large depth of field using pre-processing.in optical scanning holography

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Optics Express 2025年第3期33卷 5810-5822页

作者： Yao, Yongwei Zhang, Yaping Duan, Jilu Poon, Ting-Chung Yunnan Kunming650500 China Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

Optical scanning holography (OSH) emerges as a groundbreaking single-pixel real-time holographic recording technique, charting new territory beyond conventional digital holography. A notable challenge in OSH, as with all three-dimensional (3D) imaging methods, is the susceptibility of reconstructed images corrupted by defocus noise. The noise is an unintended result of diffraction on the focused plane from out-of-focus planes of the three-dimensional object, which can degrade image quality upon holographic reconstruction. Current methods, however, all use some algorithms to post-process holographic data to achieve the purpose of eliminating out-of-focus noise, which is difficult to meet the requirements of real-time image processing. We propose an innovative pre-processing.approach in optical scanning holography that not only eliminates defocus noise but also significantly enhances image quality. Preprocessing.is accomplished by the utilization of a focus-tunable lens (FTL) to refocus the optical scanning beam according to the depth of the occlusion-free object. Complex holograms are then captured without the artifacts associated with defocus through real-time holographic data pre-processing. Numerical simulations and experimental results demonstrate that our proposed method can directly obtain high-quality reconstructed images with all the 3D information being in focus simultaneously. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Holograms

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Architectural design visual information mining system based on image processing.technology

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JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期34卷

作者： Pan, Junyu Univ New South Wales Art Design & Architecture Sydney 2000 Australia

With the rapid development of modern society and economy, various types of science and technology have also achieved relatively rapid development, which has led to significant changes in all walks of life in modern society. This change not only makes modern society transition toward an information society but also makes people in modern society obtain more convenience. The construction industry has also achieved higher quality development with the development of science and technology, especially through the informatization reform of existing technical means in multiple processes such as building design and construction, which has saved a lot of manpower and material resources. The most important aspect in the field of architecture is the design work before construction, and the degree of refinement in this process also determines the merits of the building to a certain extent. Therefore, the field of architectural design has also received more attention from relevant researchers. At the same time, the further development of social economy in the new era also puts forward more requirements for architectural design in the construction industry, which urges researchers to conduct in-depth research on existing architectural design. At the same time, combining some emerging information technologies, a new architectural design mode with better structure and performance is proposed. image processing.technology mainly uses computer algorithms to collect images, thereby converting these images into digital signals that can be recognized by a computer, and then displaying them on a computer display. This image processing.technology can also identify and extract information from images, thereby displaying the key information therein. This article mainly analyzes image processing.techniques and some data analysis algorithms to obtain the feasibility of their application in visual information mining systems for architectural design. The contribution of this study is to propose

关键词： architectural design visualization technology information mining image processing.technology wavelet transform denoising threshold segmentation algorithm data analysis algorithm

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Modified bernoulli map-based scramble and s-box supported colour image encryption

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SIGNAL image AND VIDEO processing 2025年第1期19卷 1-10页

作者： Etem, Taha Kaya, Turgay Cankiri Karatekin Univ Dept Comp Engn TR-23250 Merkez Turkiye Firat Univ Dept Elect & Elect Engn TR-23250 Merkez Turkiye

The exponential growth of digital image sharing has amplified concerns regarding data privacy and security, especially for colour images of varying sizes and resolutions. Traditional encryption algorithms often fall short in balancing speed, scalability, and robust security for such diverse image datasets. Addressing this gap, we introduce a novel colour image encryption scheme that synergizes modified Bernoulli map-based random number generation for pixel scrambling with an S-Box-supported diffusion process. Our approach first employs a chaotic random number generator to effectively reorder pixel positions, enhancing confusion. This is followed by a diffusion phase utilizing a robust Khan S-Box to introduce nonlinearity and further obfuscate pixel values. To evaluate the security and efficiency of our method, we conducted extensive tests including differential cryptanalysis using NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) metrics. The results demonstrate that our encryption system exhibits high resistance to differential attacks and achieves superior performance compared to existing methods. By combining fast random number generation with strong S-Box diffusion, our scheme offers a scalable and secure solution for real-time colour image encryption, contributing significant advancements to the field of cryptographic image processing.

关键词： image Encryption Scrambling S-Box Bernoulli Map Random Number Generator

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