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Combining Creative Adversarial Networks with Art Design Models and machine vision Feedback Optimization

Computer-Aided Design and applications 2024年第S15期21卷 103-116页

作者： Tian, Qiang Li, Qisong Art School Anhui Jianzhu University Anhui Hefei230601 China Department of Architecture National Taiwan University of Science and Technology Taipei106335 Taiwan

In the field of art and design, different artistic styles endow works with unique charm and expressive power. Computer-aided design (CAD) model processing in art and design refers to the stage of using computer technology to process CAD models in art and design works. The traditional CAD model processing methods mainly include rule-based optimization, parametric design, etc. However, these methods often struggle to achieve ideal results when faced with complex and diverse art and design works. This article proposes an art and design CAD model processing and machine vision feedback optimization method based on Generative Adversarial Networks (GAN). This method combines an image sparse encoding algorithm to repair and optimize art images, guiding the optimization direction of the model. Through experimental verification and subjective assessment by observers, the results show that this method is significantly superior to traditional design methods in terms of image restoration accuracy, quantity of erroneous pixels, and user satisfaction. This method improves the quality of restoration and creation and provides new impetus for the progress of art and design. © 2024 U-turn Press LLC.

关键词： Generative adversarial networks

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Artificial intelligence in pediatric surgery

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SEMINARS IN PEDIATRIC SURGERY 2024年第1期33卷 151390-151390页

作者： Tsai, Anthony Y. Carter, Stewart R. Greene, Alicia C. Penn State Hlth Childrens Hosp Div Pediat Surg 500 Univ Dr Hershey PA 17033 USA Univ Louisville Sch Med Div Pediat Surg Louisville KY 40292 USA

Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.

关键词： Artificial intelligence machine learning Natural language processing Computer vision Pediatric surgery Predictive analysis

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Comprehensive empirical evaluation of feature extractors in computer vision

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2415-e2415页

作者： Isik, Murat Kirsehir Ahi Evran Univ Fac Engn & Architecture Comp Engn Kirsehir Turkiye

Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications. This study offers a comprehensive evaluation of traditional feature detections and descriptors, analyzing methods such Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Accelerated KAZE (AKAZE), Fast Retina Keypoint (FREAK), Dense and Accurate Invariant Scalable descriptor for Yale (DAISY), Features from Accelerated Segment Test (FAST), and STAR. Each feature extractor was assessed based on its architectural design and complexity, focusing on how these factors influence computational efficiency and robustness under various transformations. Utilizing the image Matching Challenge Photo Tourism 2020 dataset, which includes over 1.5 million images, the study identifies the FAST algorithm as the most efficient detector when paired with the ORB descriptor and Brute-Force (BF) matcher, offering the fastest feature extraction and matching process. ORB is notably effective on affinetransformed and brightened images, while AKAZE excels in conditions involving blurring, fisheye distortion, image rotation, and perspective distortions. Through more than 2 million comparisons, the study highlights the feature extractors that demonstrate superior resilience across various conditions, including rotation, scaling, blurring, brightening, affine transformations, perspective distortions, fisheye distortion, and salt-and-pepper noise.

关键词： machine vision image processing Feature extractors Computer vision Robustness in feature detection

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image Registration Algorithm for Stamping Process Monitoring Based on Improved Unsupervised Homography Estimation

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7721页

作者： Zhang, Yujie Du, Yinuo Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Peoples R China

Featured Application This paper is applied in the industrial monitoring of the stamping process, where vibrations are common, necessitating the use of image registration and homography estimation methods to align template images with test images. By employing machine-vision and image-processing techniques, the process is monitored in real-time to detect any anomalies, ultimately aiming to protect the stamping *** Homography estimation is a crucial task in aligning template images with target images in stamping monitoring systems. To enhance the robustness and accuracy of homography estimation against random vibrations and lighting variations in stamping environments, this paper proposes an improved unsupervised homography estimation model. The model takes as input the channel-stacked template and target images and outputs the estimated homography matrix. First, a specialized deformable convolution module and Group Normalization (GN) layer are introduced to expand the receptive field and enhance the model's ability to learn rotational invariance when processing large, high-resolution images. Next, a multi-scale, multi-stage unsupervised homography estimation network structure is constructed to improve the accuracy of homography estimation by refining the estimation through multiple stages, thereby enhancing the model's resistance to scale variations. Finally, stamping monitoring image data is incorporated into the training through data fusion, with data augmentation techniques applied to randomly introduce various levels of perturbation, brightness, contrast, and filtering to improve the model's robustness to complex changes in the stamping environment, making it more suitable for monitoring applications in this specific industrial context. Compared to traditional methods, this approach provides better homography matrix estimation when handling images with low texture, significant lighting variations, or large viewpoint changes. Compared to other deep-lear

关键词： machine vision stamping mold protection image alignment single-stress estimation unsupervised learning data enhancement

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Dual-Adaptive Heterojunction Synaptic Transistors for Efficient machine vision in Harsh Lighting Conditions

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ADVANCED MATERIALS 2024年第32期36卷 2404160-2404160页

作者： Wang, Yiru Nie, Shimiao Liu, Shanshuo Hu, Yunfei Fu, Jingwei Ming, Jianyu Liu, Jing Li, Yueqing He, Xiang Wang, Le Li, Wen Yi, Mingdong Ling, Haifeng Xie, Linghai Huang, Wei Nanjing Univ Posts & Telecommun NJUPT State Key Lab Organ Elect & Informat Displays Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun NJUPT Inst Adv Mat IAM Nanjing 210023 Peoples R China Northwestern Polytech Univ Frontiers Sci Ctr Flexible Elect FSCFE MIIT Key Lab Flexible Elect KLoFE Xian 710072 Peoples R China

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes. Organic heterojunction transistors are designed to integrate light intensity-adaptive threshold sliding and second-order adaptive metaplasticity. The unique dual adaptability enables the highlighting of 0.4% low-contrast images, and the efficient recognition can be achieved benefiting from the learning rate changes in the backpropagation process. image

关键词： adaptation machine vision metaplasticity organic heterojunction visuomorphic computing

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Smartphone based app development with machine learning using Hibiscus sabdariffa L. extract for pH estimation

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 257卷

作者： Aydin, Omer Faruk Aydin, Merve Demir, Melisa Caliskan Kahraman, Sibel Istanbul Aydin Univ Dept Comp Programming Istanbul Turkiye Marmara Univ Dept Control & Automat Technol Istanbul Turkiye Istanbul Aydin Univ Dept Ind Engn Istanbul Turkiye Istanbul Aydin Univ Dept Food Engn Istanbul Turkiye

This study presents a novel approach for pH estimation in buffer solutions using images of solutions prepared with Hibiscus sabdariffa L. as a natural pH indicator. The images of the solutions, each displaying distinctive colours indicative of their pH levels, were transformed into standardized 200x200-pixel images through the application of image processing techniques. Following this, a pH prediction model was constructed using the Adaptive Boosting regressor algorithm. The pH values of the training data used when training the model were distributed irregularly between 0-14. The models were trained with 94 pictures and 1880 experimental values. In addition, a reliable pre-processing part has been placed into the model using image processing techniques, allowing test data to be obtained in any desired environment. The obtained training and test data were separated from noise parameters, affecting the prediction results negatively. A smartphone application based on the model has been developed and made available to everyone. This innovative methodology bridges the gap between traditional pH measurement techniques and computer vision, offering amore accessible and eco-friendly means of pH assessment. The practical applications of this research extend to various fields, including environmental monitoring, agriculture, and educational settings.

关键词： machine learning image processing pH estimation Hibiscus sabdariffa L. Smartphone

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Gaussian mixture model clustering allows accurate semantic image segmentation of wheat kernels from near-infrared hyperspectral images

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 259卷

作者： Kartakoullis, Andreas Caporaso, Nicola Whitworth, Martin B. Fisk, Ian D. Univ Nottingham Sch Biosci Div Div Food Nutr & Dietet Sutton Bonington Campus Nottingham LE12 5RD England Campden BRI Chipping Campden GL55 6LD Glos England Buhler UK Ltd London E16 2LD England

In this study, an ad-hoc image processing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machine vision, particularly for food product quality assessment and real-time inspection.

关键词： NIR hyperspectral imaging image segmentation Real time image processing Grain computer vision

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Diversified image style transfer-approaches, new methods and directed variability control

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machine vision AND applications 2025年第2期36卷 1-12页

作者： Ustyuzhanin, Alexander Kitov, Victor Kitov, Vladimir Yandex Technol LLC Artificial Intelligence & Res Dept 16 Lva Tolstogo St Moscow 119021 Russia Plekhanov Russian Univ Econ Artificial Intelligence Lab 36 Stremyanny Per Moscow 117997 Russia Lomonosov Moscow State Univ Fac Computat Math & Cybernet GSP-12nd Acad Bldg Moscow 119991 Russia

The task of image style transfer is to automatically redraw an input image in the style of another image, such as an artist's painting. The disadvantage of conventional stylization algorithms is the uniqueness of result. If the user is not satisfied with the way the style was transferred, he has no option to remake the stylization. The paper provides an overview of existing style transfer methods that generate diverse results after each run and proposes two new methods. The first method enables diversity by concatenating a random vector into inner image representation inside the neural network and by reweighting image features accordingly in the loss function. The second method allows diverse stylizations by passing the stylized image through orthogonal transformations, which impact the way the target style is transferred. These blocks are trained to replicate patterns from additional pattern images, which serve as additional input and provide an interpretable way to control stylization variability for the end user. Qualitative and quantitative comparisons demonstrate that both methods are capable to generate different stylizations with higher variability achieved by the second method. The code of both methods is available on github.

关键词： Artistic rendering Diversity image processing image generation Neural networks

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Dimensional Accuracy Evaluation of Single-Layer Prints in Direct Ink Writing Based on machine vision

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SENSORS 2025年第8期25卷 2543-2543页

作者： Tu, Yongqiang Zhang, Haoran Chen, Hu Bao, Baohua Fang, Canmi Wu, Hao Chen, Xinkai Hassan, Alaa Boudaoud, Hakim Jimei Univ Coll Marine Equipment & Mech Engn Xiamen 361021 Peoples R China Univ Lorraine Innovat Proc Res Inst F-54000 Nancy France

The absence of standardized evaluation methodologies for single-layer dimensional accuracy significantly hinders the broader implementation of direct ink writing (DIW) technology. Addressing the critical need for precision non-contact assessment in DIW fabrication, this study develops a novel machine vision-based framework for dimensional accuracy evaluation. The methodology encompasses three key phases: (1) establishment of an optimized hardware configuration with integrated image processing algorithms;(2) comprehensive investigation of camera calibration protocols, advanced image preprocessing techniques, and high-precision contour extraction methods;and (3) development of an iterative closest point (ICP) algorithm-enhanced evaluation system. The experimental results demonstrate that our machine vision system achieves 0.04 mm x 0.04 mm spatial resolution with the ICP convergence threshold optimized to 0.001 mm. The proposed method shows an 80% improvement in measurement accuracy (0.001 mm) compared to conventional approaches. Process parameter optimization experiments validated the system's effectiveness, showing at least 76.3% enhancement in printed layer dimensional accuracy. This non-contact evaluation solution establishes a robust framework for quantitative quality control in DIW applications, providing critical insights for process optimization and standardization efforts in additive manufacturing.

关键词： machine vision dimensional accuracy evaluation single layer direct ink writing

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VQA and Visual Reasoning: An overview of approaches, datasets, and future direction

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NEUROCOMPUTING 2025年 622卷

作者： Zakari, Rufai Yusuf Owusu, Jim Wilson Qin, Ke Wang, Hailin Lawal, Zaharaddeen Karami He, Tao Univ Elect Sci & Technol China Xiyuan Ave Chengdu 611731 Peoples R China Univ Brunei Darussalam Jalan Tungku Link BE-1410 Bandar Seri Begawan Brunei

Visual question answering (VQA) is a problem that researchers in both computer vision and natural language processing are interested in studying. In VQA, a system is given an image and a question in natural language about that image. The VQA system is then expected to answer in natural language. To find the right answer, a VQA algorithm may need to use common sense to make sense of the information in the image and external knowledge. In this paper, we discuss some of the main ideas behind VQA systems and provide a comprehensive literature survey of the current state of the art in VQA and visual reasoning from four perspectives: problem definition and challenges, approaches, existing datasets, and evaluation matrices. We conclude our survey with a discussion and some potential future research directions in this area to generate new ideas and creative approaches to solving current problems and developing new applications.

关键词： machine learning Deep learning VQA Natural language processing Reasoning Computer vision

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