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Factorization-Based Self-Calibration and 3D Shape Recovery on a Novel SEM image Dataset of Copper Foil Surfaces

Factorization-Based Self-Calibration and 3D Shape Recovery o...

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2023 International conference on machine vision, image processing and Imaging Technology, MVIPIT 2023

作者： Deng, Xuejiao Hascoet, Tristan Takiguchi, Tetsuya Kobe University Graduate School of System Informatics Hangzhou China

ISBN: (纸本)9798350306545

In 3D reconstruction within computer vision, deriving precise 3D points from 2D images remains challenging. This paper contributes to two primary areas: First, we introduce a novel dataset from Scanning Electron Microscopy(SEM) images of etched copper foil, showcasing varying magnifications and angles to delve into surface patterns and multi-view processing nuances. Subsequently, we leverage an optimization technique distinguished by the incorporation of tailored constraints, ensuring view and magnification consistency. Our method thus focuses on enhancing self-calibration through factorization and refining transformations to yield precise camera projection matrices. © 2023 IEEE.

关键词： Scanning electron microscopy

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LFRNet: Low-Light Face Super-Resolution with Light Frequency Representation 2

LFRNet: Low-Light Face Super-Resolution with Light Frequency...

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2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Zha, Bingxin Zhu, Huijie Zhang, Haolin Yang, Shengying School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Huzhou Zhongke Fanzai Electric Power Technology Development Co. Ltd Huzhou313000 China Co. Ltd Shanghai200233 China

ISBN: (纸本)9798350390254

In the field of computer vision, the task of facial super-resolution (FSR) is crucial for applications such as surveillance and photo restoration. However, factors such as noise and artifacts in real-world scenarios severely degrade image quality. Although existing methods use geometric priors and facial heatmap localization to improve FSR performance, these priors are often inaccurate under low-light conditions, affecting the results. This paper proposes an innovative low-light facial enhancement network aimed at integrating FSR and low-light enhancement tasks. We designed a Light Frequency Inference Block (LFIB) to capture and refine brightness and texture features at different frequencies, combining it with Transformer modules in an Encoder-Decoder architecture. The LFIB module separates degraded images into low-frequency and high-frequency brightness features and employs spatial cross-attention to capture facial texture details, effectively addressing the degradation issues in facial images. Experimental results demonstrate that our method excels in the low-light facial super-resolution task, outperforming existing methods on various metrics. Additionally, it shows good generalization capabilities in real-world scenarios, confirming its potential for practical applications. © 2024 IEEE.

关键词： image reconstruction

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Enhancing Multi-view Mammography image Classification: By using Breast Region Extraction Method and Statistical Features

Enhancing Multi-view Mammography Image Classification: By us...

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iranian machine vision and image processing (MVIP)

作者： Neda Shirany Bidabadi Elham Mahmoudzadeh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Breast cancer is one of the most dangerous diseases among women. Different methods are used to diagnose this cancer that among these, imaging and computer-aided systems are more common. In these systems, one of the most important step is preprocessing and removing unnecessary areas of the images, as well as extracting the chest area. In this paper, we present a method that consists of preprocessing, feature extraction, and using a machine learning classifier. In the preprocessing step, we propose a method to extract the region of interest in both angles of mammography images. The proposed novel method includes applying gamma correction thresholding to the images and obtaining two binary images based on the proposed threshold using the Otsu method. Results show the proposed method successfully removes the chest muscle with 98% accuracy. In the next, for feature extraction phase, we utilize three different methods for extracting features. Finally, by employing an Extra tree model classifier, we classify mammography images into normal and abnormal. By incorporating the block-based feature extraction method, we achieve 98% accuracy in classification. Overall, our approach demonstrates the effectiveness of preprocessing and feature extraction for diagnosing breast cancer using mammography images.

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LED Color Quality Detection Technology Based on image processing 2

LED Color Quality Detection Technology Based on Image Proces...

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Xin, Weijie Shanghai Institute of Technology Department of Electrical and Electronic Engineering Shanghai201418 China

ISBN: (纸本)9798350331417

Modern LED color quality detection systems (color calibration systems) often involve numerous color sensors and motors. Although this method has high detection accuracy, improper control of the motor can easily cause high-precision probes to collide with each other, causing unnecessary losses. In addition, if we want to further improve the utilization rate of calibration equipment, so that a machine can calibrate more light panels at the same time, relying solely on the increase in the number of motors and probes will lead to problems such as increased costs and reduced stability. If enough color information of LED can be collected through images and its quality can be successfully detected, it can get rid of the complex motor and color detection equipment, and even achieve the simultaneous detection of more lamp beads by one machine, thereby reducing detection costs. Therefore, this article combines image processing technology to first collect LED aperture color information, and then convert the color information into the HSL color space model for color quality analysis to determine whether the color quality is maintained within the allowable color difference range. This proves the possibility of using image processing technology to achieve LED color quality evaluation and provides theoretical support for further optimizing the color calibration system. © 2023 IEEE.

关键词： Color calibration system HSL color space image processing LED color quality inspection

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Att-CapsViT: Attention-based CapsNet and vision Transformer Hybride Network for Handwriting Chinese Characters Recognition 2

Att-CapsViT: Attention-based CapsNet and Vision Transformer ...

引用

2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Zheng, Xiangling Zhou, Nanxiang Geng, Zibin School of Information Technology & Management University of International Business and Economics Beijing China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350390254

The research on text recognition has long been under the close scrutiny of scholars, especially the recognition of complex Chinese characters, which is highly favored by the academic community. In recent years, handwriting recognition has achieved remarkable results within the traditional framework of deep learning. However, this approach still falls short in capturing the subtle features and spatial hierarchical relationships within Chinese characters. Furthermore, the complexity of Chinese handwritten characters and the diversity of writing styles pose higher challenges to models. To address this, we propose an attention-based CapsN et and vision transformer hybride network for handwriting Chinese characters recognition (Att-CapsViT), which combines the attention mechanism with capsule networks and visual transformer. The Att-CapsViT model integrates the attention mechanism, combining the local detail feature capturing capability of capsule networks (CapsNets) with the global contextual awareness of visual transformer (ViT) to achieve multi-level feature extraction from local to global. This experiment was validated using two open-source datasets, successfully confirming the robust performance of the Att-CapsViT model in the recognition of Chinese handwritten characters, providing a new perspective for the field of complex text recognition. © 2024 IEEE.

关键词： Deep learning

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Research on machine vision-based method for head-tail detection of corn ears

Research on machine vision-based method for head-tail detect...

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2023 International conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2023

作者： Chang, Hongjie Wang, Ping Niu, Huli Yan, Haipeng Wei, Bin Zhang, Gaoshuai College of Mechanical Engineering Hebei University of Science and Technology Shijiazhuang China Sinopec Shijiazhuang Refining and Chemical branch Shijiazhuang China

ISBN: (纸本)9781510674608

To address the issues of manual feeding and placement in the processing of fresh corn ears, this study develops a fresh corn ear detection system by using machine vision and deep learning techniques. The hardware setup and acquisition of clear image data were completed. The G channel image in the RGB channel was selected for subsequent image processing. Contrast enhancement and image sharpening techniques were employed to improve the clarity of the ear region. Additionally, a median filtering method was used to eliminate noise caused by lighting conditions. Morphological processing and Otsu threshold segmentation techniques were applied to extract complete corn ear regions. Based on the contour feature, three kinds of feature extraction of ear shape, gray value, and ear texture are selected. By comparison, the ear texture feature recognition algorithm with higher recognition accuracy and shorter detection time was selected. The algorithm was used to detect the ear handle region, and the cutting position was predicted in this region. This paper studied the method of predicting the cutting position by using the slope change feature of the spikelet region contour and used C# and Halcon mixed programming to realize the automatic detection of the ear. Experimental results show that the detection accuracy of the spikelet region can reach 98.23% by using the texture feature detection algorithm, and the detection time of a single ear is 23.90 ms. © 2024 SPIE.

关键词： Computer vision

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The research on machine-vision-based EMI source technology for DCDC converter circuit boards 6

The research on machine-vision-based EMI source technology f...

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6th International conference on Information Science, Electrical, and Automation Engineering, ISEAE 2024

作者： Feng, Hongzhi Vivo Mobile Communication Co. Ltd. Guangdong Province Dongguan City China

ISBN: (纸本)9781510682924

This paper proposes a method for EMI source localization on DCDC converter circuit boards based on machine vision. By analyzing the operating principle of DCDC converters and the mechanism of EMI generation, the application of vision technology in EMI source localization is discussed. A complete system including hardware platform construction, image processing, and EMI source localization algorithms is designed. Simulations and experiments show that this method can effectively identify and locate the EMI source with high precision and real-time performance. There is a good correspondence between the magnetic field distribution images and the EMI source locations. This paper provides new ideas and methods for the diagnosis and resolution of EMI issues. © 2024 SPIE.

关键词： machine vision

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Intelligent Traffic Monitoring and Management Based on Computer vision Technology 2

Intelligent Traffic Monitoring and Management Based on Compu...

引用

2nd International conference on Data Science and Information System, ICDSIS 2024

作者： Chen, Chunmei Shandong Transport Vocational College Department Of Vehicle Engineering Shandong Weifang China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

Traditional traffic monitoring systems are limited by the number of devices and the efficiency of location data processing and analysis, making it difficult to achieve timely perception and response to traffic conditions, and cannot adapt to dynamic changes in traffic conditions. By utilizing machine vision technology, it can compensate for the shortcomings in traditional traffic monitoring systems and improve the efficiency of intelligent traffic monitoring and management. By using cameras and other devices to collect image data from roads, preprocessing operations such as image denoising are performed on the collected image data to improve image quality and extract traffic information. This article uses the YOLO (You Only Look Once) object detection algorithm to detect and recognize objects in the collected images, and uses a Kalman filter for vehicle motion analysis. It can predict its direction and speed of travel, display the results of traffic flow and road condition analysis to traffic workers, facilitate relevant personnel to understand the traffic situation, and adjust and modify traffic management strategies. After using computer vision technology to achieve intelligent traffic monitoring and management, the average traffic diversion time was reduced by 48 minutes and the average accident rate was reduced by 0.3%. Intelligent traffic monitoring and management based on computer vision technology can provide functions such as illegal behavior monitoring and traffic flow optimization by processing and analyzing image data in traffic roads, improving the level of traffic management and road traffic safety. © 2024 IEEE.

关键词： Efficiency

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Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained image Classification 2

Abnormal Clustering and Cross Slicing Transformer for Insect...

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2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Mei, Aokun Huo, Hua Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Big Data Analysis Laboratory of Henan Medical Luoyang471023 China

ISBN: (纸本)9798350390254

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images. © 2024 IEEE.

关键词： image segmentation

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Redefining Dental Radiology Using Deep Learning 2

Redefining Dental Radiology Using Deep Learning

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2nd IEEE World conference on Communication and Computing (WCONF)

作者： Saha, Kshitij G. Dheeraj, R. P. Kulkarni, Pratham Agarwal, Animesh Prasad, V. R. Badri Saha, Mainak Kanti Saha, Suparna Ganguly PES Univ Dept Comp Sci Engn Bengaluru India Modern Dent Coll Dept Prosthodont Indore India Index Inst Dent Sci Dept Conservat Dent Indore India

ISBN: (纸本)9798350395334;9798350395327

The recent times have witnessed a rise in the use of image processing, computer vision, and machine learning in the field of medical imaging, thus offering more accurate diagnoses with a reduction of the cost of labor while at the same time, minimizing the scope for human error. Dental X-ray images are often challenging and time-consuming to study consequently making diagnosis more arduous. Furthermore, only an experienced clinician can endeavor to provide an accurate diagnosis from a two-dimensional X-ray image. Manual investigation of dental diseases and abnormalities is still the most prevalent method in the field of dentistry. This article aims to introduce a novel method to automate the process of obtaining an initial diagnosis from orthopantamogram(OPG) X-rays by using state-of-the-art object detection models which are currently proving to be effective in medical image diagnosis. By providing an effective comparison between popular object detection frameworks, we aim to determine the computer vision model that provides the most promising results by accurately diagnosing dental abnormalities and identifying treatments from a dental X-ray image in an error-free and efficient manner.

关键词： X-ray Dental Radiology OPG Computer vision Object Detection image processing medical imaging teeth deep learning YOLO

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