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Printed circuit board inspection using computer vision

MULTIMEDIA TOOLS AND applications 2024年第6期83卷 16363-16375页

作者： Rajesh, A. Jiji, G. Wiselin Indian Space Res Org Vikram Sarabhai Space Ctr Thiruvananthapuram India Dr Sivanthi Aditanar Coll Engn Dept Comp Sci & Engn Tiruchendur India

The inspection of electronic components, especially printed circuit boards (PCBs), has greatly benefited from the advancements in computer vision technology. With the miniaturization of electronic components, defects on PCBs are now often found in smaller or micro-sized forms. This poses a significant challenge for automated optical inspection methods to effectively detect and identify such small objects. The primary objective of this study is to address the issue of fault detection in printed circuit boards (PCBs). To achieve this, the study employs various image processing techniques to carry out the inspection process. These image processing operations play a crucial role in preparing the images for defect analysis. Once the image processing operations are completed, the study proceeds to classify the identified defects in the segmented regions using a support vector machine (SVM) classifier. The SVM classifier is trained to categorize the defects based on the extracted features and their respective class labels. This classification step plays a critical role in accurately identifying and characterizing the detected defects. To evaluate the effectiveness of this study, a comparison is made with earlier works in the field. This allows for a comprehensive assessment of the proposed methodology and its performance in comparison to existing approaches. By benchmarking against previous works, the study provides valuable insights into the advancements and improvements achieved in PCB defect detection.

关键词： Feature extraction Classification PCB inspection

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Congruent Feature Selection Method to Improve the Efficacy of machine Learning-Based Classification in Medical image processing

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

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A novel opto-tactile sensing approach to enhance the handling of soft fruit

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 235卷

作者： Ameur, Mohamed Adlan Ait El-Sayed, Amr M. Yan, Xiu T. Mehnen, Jorn Maier, Anja M. Univ Strathclyde Design Mfg & Engn Management Glasgow Scotland Assiut Univ Fac Engn Mechatron Engn Dept Assiut 71516 Egypt Glasgow Caledonian Univ Dept Mech Engn Cowcaddens Rd Glasgow G4 0BA Scotland

In agricultural settings, handling of soft fruit is critical to ensuring quality and safety. This study introduces a novel opto-tactile sensing approach designed to enhance the handling and assessment of soft fruit, with a case example of strawberries. Our approach utilises a Robotiq 2F-85 gripper equipped with the DIGIT vision-Based Tactile Sensor (VBTS) and attached to a Universal Robot UR10e. In contrast to force-based approaches, we introduce a novel purely image-based processing software pipeline for quantifying localised surface deformations in soft fruit. The system integrates fast and explainable image processing techniques applying image differencing, denoising, K-means clustering for unsupervised classification, morphological operations, and connected components analysis (CCA) to quantify surface deformations accurately. A calibration of the image processing pipeline using a rubber ball showed that the system effectively captured and analysed the rubber ball's surface deformations, benefiting from its uniform elasticity and predictable response to compression. As a soft fruit case example, the image processing pipeline was subsequently applied to strawberries, blueberries, and raspberries, demonstrating that the calibration parameters derived from the rubber ball could effectively assess surface deformations in soft fruits. Despite the complex, nonlinear deformation characteristics inherent to strawberries, blueberries, and raspberries, the pipeline exhibited robust performance, capturing and quantifying subtle surface changes. These findings underscore the system's capacity for precise deformation analysis in delicate materials, offering major potential for further refinement and adaptation. This novel approach of proposing and testing an image processing pipeline lays the groundwork for enhancing the handling and assessment of materials with intricate mechanical properties, paving the way for broader applications in sensitive agricultural and industrial

关键词： Robotic gripper vision-Based Tactile Sensor (VBTS) image processing machine Learning Soft Fruit Handling Robotic manipulation

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A review on multimodal medical image fusion towards future research

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MULTIMEDIA TOOLS AND applications 2023年第5期82卷 7361-7382页

作者： Venkatesan, B. Ragupathy, U. S. Natarajan, Indhu Kongu Engn Coll Dept Elect & Instrumentat Engn Erode Tamil Nadu India Sri Shakthi Inst Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India

image fusion is a technique used to merge two or more source images into a single image that incorporates more details than the originals and still offering an accurate depiction about the captured information. Resultant fused images are more accurate and provide comprehensive information for both the human and machine vision perception for further processing of the image. image fusion provides better performance in the areas like pattern recognition, image processing, computer vision, machine learning and artificial intelligence. In the recent years image fusion has moved out of the laboratories and used in the real time applications. This paper provides the insight of various techniques for image fusion like primitive fusion (Simple averaging, Maxima and Minima, etc.), Discrete Wavelet Transform (DWT) based fusion, Principal Component Analysis (PCA) based fusion, Curvelet transform based fusion etc. On-going through various literatures, it is found that image fusion in spatial domain provides high resolution images, although the fusion algorithms are dependent on the nature of image and also depends on the application for which the image is to be fused. Hence, spectral domain fusion and hybrid fusion techniques are introduced and it is proven to be better than the spatial domain fusion. Comparison of all the techniques along with recent approaches are done to find the best approach towards future research to provide new direction to the researchers in medical sector.

关键词： image fusion Imaging modalities Medical imaging Spatial domain Transform domain

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MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification 13

MaskChanger: A Transformer-Based Model Tailoring Change Dete...

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13th Iranian/3rd International machine vision and image processing Conference (MVIP)

作者： Ebrahimzadeh, Mohammad Manzuri, Mohammad Taghi Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications. However, complex factors like illumination variance and occlusion make robust automated change interpretation challenging. We propose MaskChanger - a novel deep learning paradigm tailored for satellite image change detection. Our method adapts the segmentation-specialized Mask2Former architecture by incorporating Siamese networks to extract features separately from bi-temporal images, while retaining the original mask transformer decoder. To our knowledge, this is the first study in which change detection is converted from the existing per-pixel classification approach into a mask classification approach. Evaluated on the LEVIR-CD benchmark of over 600 very high-resolution image pairs exhibiting real-world rural and urban changes, MaskChanger achieves F1-Score of 91.96%, outperforming prior transformer-based change detection approaches.

关键词： Change Detection Remote Sensing image Transformer

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Exploring photosensitive nanomaterials and optoelectronic synapses for neuromorphic artificial vision

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CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE 2025年 35卷

作者： Lee, Hyun-Haeng Ro, Jun-Seok Kim, Kwan-Nyeong Park, Hea-Lim Lee, Tae-Woo Seoul Natl Univ Dept Mat Sci & Engn Seoul 08826 South Korea Seoul Natl Univ Sci & Technol Dept Mat Sci & Engn Seoul 01811 South Korea Seoul Natl Univ Inst Engn Res Res Inst Adv Mat Dept Chem & Biol EngnInterdisciplinary Program Bi 1 Gwanak Ro Seoul 08826 South Korea SN Display Co Ltd Seoul 08826 South Korea

Artificial vision systems will be essential in intelligent machine-vision applications such as autonomous vehicles, bionic eyes, and humanoid robot eyes. However, conventional digital electronics in these systems face limitations in system complexity, processing speed, and energy consumption. These challenges have been addressed by biomimetic approaches utilizing optoelectronic synapses inspired by the biological synapses in the eye. Nano- materials can confine photogenerated charge carriers within nano-sized regions, and thus offer significant potential for optoelectronic synapses to perform in-sensor image-processing tasks, such as classifying static multicolor images and detecting dynamic object movements. We introduce recent developments in optoelectronic synapses, focusing on use of photosensitive nanomaterials. We also explore applications of these synapses in recognizing static and dynamic optical information. Finally, we suggest future directions for research on optoelectronic synapses to implement neuromorphic artificial vision.

关键词： Optoelectronic synapses Nanomaterials Artificial vision systems Artificial synapses Neuromorphic bioelectronics

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An image-Based Transfer Learning Approach for Using In Situ processing Data to Predict Laser Powder Bed Fusion Additively Manufactured Ti-6Al-4V Mechanical Properties

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3D PRINTING AND ADDITIVE MANUFACTURING 2025年第1期12卷 48-60页

作者： Luo, Qixiang Shimanek, John D. Simpson, Timothy W. Beese, Allison M. Penn State Univ Dept Mat Sci & Engn University Pk PA USA Penn State Univ Dept Ind & Mfg Engn University Pk PA USA Penn State Univ Dept Mech Engn University Pk PA USA Penn State Univ Dept Mat Sci & Engn University Pk PA 16802 USA

The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical properties for Ti-6Al-4V fabricated through laser powder bed fusion (PBF-LB) AM using in situ photodiode processing signals. Samples were fabricated under different processing parameters, varying laser powers and scan speeds for the purpose of probing a wide range of microstructure and property variations. Photodiode data were collected during fabrication, later to be arranged in image format and extracted to information-dense vectors by the transferal of deep convolutional neural network (DCNN) structures and weights pretrained on a large computer vision benchmark image database. The extracted features were then used to train and test a newly designed regression model for mechanical properties. Average cross-validation accuracies were found to be 98.7% (r(2) value of 0.89) for the prediction of ultimate tensile strength, which ranged from 900 to 1150 MPa in the samples studied, and 93.1% (r(2 )value of 0.96) for the prediction of elongation to fracture, which ranged from 0 to 17%. Thus, with high accuracy and hardware-accelerated inference speeds, we demonstrate that a transfer learning framework can be used to predict strength and ductility of metal AM components based on processing signals in PBF-LB, illustrating a potential route toward real-time closed-loop control and process optimization of PBF-LB in industrial applications.

关键词： machine learning computer vision deep convolution neural network laser powder bed fusion in situ processing monitoring

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Task-Switchable Pre-Processor for image Compression for Multiple machine vision Tasks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 6416-6429页

作者： Yang, Mingyi Yang, Fei Murn, Luka Blanch, Marc Gorriz Sock, Juil Wan, Shuai Yang, Fuzheng Herranz, Luis Xidian Univ Sch Telecommun Engn Xian Peoples R China Nankai Univ Coll Comp Sci Tianjin 300350 Peoples R China BBC Res & Dev London EC4Y 0DS England Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia Univ Autonoma Barcelona Comp Vis Ctr Barcelona 08193 Spain

Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications.

关键词： Task analysis Codecs machine vision image coding Semantics Bit rate Feature extraction image compression for machine vision Pre-processor Multiple tasks

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Illumination Consistency processing Based on Illumination Domain Signal-Guided Unsupervised Generative Adversarial Network for Flotation Froth images

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Xiaoli Zhang, Yinan Kong, Lingshuang Zhou, Jiayi Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China Changsha Univ Sch Elect Informat & Elect Engn Changsha 410083 Peoples R China

In the machine vision-based online monitoring of the flotation process, froth images acquired in real-time are subject to color distortion and excessive bright spots caused by inconsistent illumination, which hinders the effectiveness of image analysis and further online measurement for operating performance indicators. Current image processing methods struggle to correct color distortion and remove excess bright spots in froth images simultaneously. Therefore, in this article, an illumination domain signal-guided unsupervised generative adversarial network (IDS-GUGAN) is proposed for illumination consistency processing of flotation froth images. First, considering the varying effects of inconsistent illumination on froth images, the illumination domain signal-guided image generation (IDS-GIG) mechanism based on the theory of unsupervised disentangled representation learning is designed to achieve adaptive correction of froth images with varying degrees of distortion. Moreover, a novel lightweight double-closed-loop network architecture is introduced to support unsupervised learning utilizing unpaired froth images and improve computational efficiency, which makes the proposed approach highly suitable for industrial applications. Comprehensive experiments on a real tungsten cleaner flotation process dataset and two public benchmark datasets related to image illumination processing tasks consistently endorse the superiority of IDS-GUGAN.

关键词： Flotation froth image generative adversarial network (GAN) illumination consistency processing unsupervised disentangled representation learning Flotation froth image generative adversarial network (GAN) illumination consistency processing unsupervised disentangled representation learning

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Revolutionizing machine vision: Advanced Convolutional Strategies for Rapid image processing

Revolutionizing Machine Vision: Advanced Convolutional Strat...

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Information and Communication Technology (ICTech), International Conference of

作者： Hanlei Wu Pittsburgh Institute Sichuan University Chengdu China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

This paper presents a comprehensive examination of innovative strategies aimed at enhancing machine vision technology, particularly in the context of energy efficiency and processing speed, critical factors for applications like facial recognition. The study focuses on three distinct approaches: an optimized two-dimensional convolution algorithm, a novel Field-Programmable Gate Array (FPGA) implementation, and advancements in multichannel meta-imagers. Firstly, the paper discusses an optimized algorithm for two-dimensional convolutions, a fundamental operation in machine vision. This advanced algorithm significantly reduces computational complexity. For instance, in executing a two-dimensional 3×3 cyclic convolution, the proposed method reduces the number of necessary multiplications from 81 to merely 13, offering a substantial improvement in efficiency. Secondly, the paper explores an innovative FPGA implementation of the two-dimensional convolution algorithm. This implementation is designed to minimize the use of shift registers, multipliers, and adders. As a result, it utilizes fewer Look-Up Tables (LUTs), leading to energy and time savings in executing the convolution process. The paper details the architecture of this FPGA-based approach and its implications for energy consumption and processing speed in machine vision applications. Finally, the paper introduces a novel technique called the Avg-Topk method, addressing a critical challenge in the pooling layer of convolutional neural networks. This method combines the benefits of average pooling with the advantages of max pooling, aiming to enhance the accuracy of the pooling layer without compromising on efficiency. The Avg-Topk method represents a significant step forward in optimizing the pooling process within machine vision systems. In summary, this paper delves into groundbreaking methods to improve the speed and energy efficiency of machine vision systems, offering valuable insights and potential solution

关键词： image resolution Accuracy Convolution machine vision Shift registers Energy efficiency Table lookup Classification algorithms Usability Field programmable gate arrays

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