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Sub-pixel dimensional and vision measurement method of eccentricity or annular parts

APPLIED OPTICS 2022年第6期61卷 1531-1538页

作者： Fang, Yuntao Wang, Xiaodong Xin, Yunpeng Luo, Yi Dalian Univ Technol Key Lab Micro Nano Technol & Syst Liaoning Prov Dalian Peoples R China Dalian Univ Technol Minist Educ Key Lab Precis & Nontradit Machining Dalian Peoples R China

Eccentricity measurement of annular parts with millimeter scale and micrometer precision requirements is widely used in mechanical engineering applications. To realize accurate eccentricity measurement for large-scaled annular parts, a vision-based and sub-pixel dimensional measurement method is proposed. First, to facilitate the eccentricity measurement, an improved auto focus algorithm is introduced to provide better focused images of the measured parts. Then the traditional Canny operator is modified in gradient direction calculation and a double threshold process to locate the pixel edge more accurately. Next, a model-based sub-pixel edge detection method is studied to extract the sub-pixel edge coordinates. Finally, the eccentricity is calculated according to these sub-pixel edge coordinates. To guarantee measurement accuracy, the pixel equivalent and manual installation error of three degree of freedom (DOF) stages are calibrated, and the verification experiments indicate that the measurement error of the proposed method is better than 1.0 mu m. (C) 2022 Optica Publishing Group

关键词： Autofocus Digital image processing Edge detection image processing Laser sensors machine vision

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A machine Learning Approach to image Registration Verification

A Machine Learning Approach to Image Registration Verificati...

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Multimodal image Exploitation and Learning 2023

作者： DelMarco, Stephen Webb, Helen Stolle, Frank Bortolami, Simone Tucker, Frank Hillyer, Travis BAE Systems 600 District Avenue BurlingtonMA01803 United States BAE Systems 600 District Avenue BurlingtonMA01803 United States CCDC Soldier Center STTC 12423 Research Pkwy OrlandoFL32826 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

image registration is an important pre-processing step for many image exploitation algorithms such as geo-location, object recognition, vision-aided navigation, and image fusion. The utility and effectiveness of downstream exploitation algorithms depends on reliable image registration. image registration failure can corrupt processing and performance of downstream algorithms by mis-associating image features. For example, feature mis-association leads to generation of incorrect target geo-coordinates in aerial surveillance applications, or erroneous vision-based measurements in vision aided navigation. Accurate and dependable registration failure detection mitigates deleterious effects of erroneous registration. However, in autonomous operation modes, with no human in-the-loop, and in the absence of registration solution groundtruth knowledge, verifying registration solutions is problematic. In this paper we present a machine learning based image registration verification system that operates autonomously, without ground-truth. We train a machine learning algorithm to identify correct registration solutions, even for difficult multi-modal image registration in which sensor phenomenology differences produce different feature manifestation. The verification approach includes techniques for mitigation of falsealarms that may arise due to feature ambiguity. We present examples of feature ambiguity for correlation-related registration techniques. We describe Radon transform processing, covariance estimation, and fusion techniques for feature ambiguity detection. We present numerical verification performance results from a small pilot study designed to investigate the feasibility of using machine learning for reliable registration verification. © 2023 SPIE.

关键词： image registration

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

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2nd International Conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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Frame-Adaptive Multi-Object Tracking for Discontinuous image Sequences 2

Frame-Adaptive Multi-Object Tracking for Discontinuous Image...

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

作者： Li, Xiaolei Xiao, Xingjie Yang, Siyuan Sha, Moquan Sha, Zongyao Tu, Jianguang School of Remote Sensing and Information Engineering Wuhan University Wuhan China Wuhan Luojia Zhongheng Remote Sensing Data Technology Co Wuhan China China Unicom Smart City Research Institute Beijing China

ISBN: (纸本)9798350390254

In the field of multi-object tracking, this study introduces an innovative framework designed to address the challenges posed by frame loss in image sequences, particularly within the contexts of video surveillance and traffic management. Despite the significant achievements of deep learning-based tracking methods in processing high frame rate videos, frame loss remains a prevalent issue in real-world deployments due to network and hardware constraints, which can result in erroneous target associations and interrupted trajectory tracking. Our research begins by detecting frame loss through a timestamp- based analysis, this is followed by the deployment of an adaptive algorithm that fuses spatial and appearance metrics to compensate for the positional discrepancies and visual inconsistencies caused by missing frames, thus improving the precision of data association. We have curated a dataset that accurately reflects real-world environments, and conducted validation on subsampled versions of the MOT17 and MOT20 datasets, in addition to our original dataset. Empirical results indicate that our multi-object tracking approach demonstrates significant adaptability and robustness in handling discontinuous image sequences, reducing the number of ID switches by 54.1 % on our original dataset compared to state-of-the-art methods. The proposed framework has the potential to significantly enhance the performance of multi-object tracking systems in real-world scenarios where frame loss is a common occurrence, thereby improving the reliability and accuracy of video surveillance and traffic management applications. © 2024 IEEE.

关键词： Adaptive algorithms

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Embedded Intelligence for Safety and Security machine vision applications 21st

Embedded Intelligence for Safety and Security Machine Vision...

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21st International Conference on image Analysis and processing (ICIAP)

作者： Lioupis, Panagiotis Dadoukis, Aris Maltezos, Evangelos Karagiannidis, Lazaros Amditis, Angelos Gonzalez, Maite Martin, Jon Cantero, David Larranaga, Mikel Inst Commun & Comp Syst ICCS Zografos 15773 Greece Fdn Tekniker Inaki Goenaga 5 Eibar 20600 Spain

ISBN: (纸本)9783031133244;9783031133237

Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as image processing for security applications. Deep learning, a subset of AI, is particularly useful for those image processing applications. Deep learning methods can achieve state-of-the-art results on computer vision for image classification, object detection, and face recognition applications. This allows to automate video surveillance reducing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Hardware manufacturers have developed powerful co-processors specifically designed to execute deep learning algorithms. But also, new lightweight open-source middleware for constrained resources devices such as EdgeX foundry have emerged to facilitate the collection and processing of data at sensor level, with communication capabilities to cloud enterprise applications. The aim of this work is to show and describe the development of Smart Camera Systems within S4AllCities H2020 project, following the edge approach.

关键词： Horizon2020 Edge EdgeX foundry machine vision Artificial intelligence Deep learning

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Efficient dual-wavelength metasurface for second-order differential edge detection in the ultraviolet

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APPLIED OPTICS 2023年第32期62卷 8552-8557页

作者： Chen, Hao Gui, Lili Guo, Jinyang Xu, Kun Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

Metasurfaces for edge detection through spatial analog calculations have attracted much attention due to advantages such as a flexible design and small footprint. Up until now, most studies have focused on single-wavelength operation in the near-infrared or visible regions, while little work has been done in the ultraviolet band. It is of significance to explore metasurfaces for edge detection in the ultraviolet band for their great potential in highresolution imaging and lithography. Here, we propose a dual-wavelength HfO2 metasurface for edge detection working at 273 nm and 293 nm, with 25% and 72% efficiency, respectively, controlled by the linear polarization of the incident light. The efficient dual-wavelength second-order differential calculation in the ultraviolet band of the metasurface has been confirmed by 1D signal and 2D image processing. It may find applications in the fields of computer vision and bioimaging. (c) 2023 Optica Publishing Group

关键词： Edge detection Imaging systems Lithography machine vision Optical transfer functions Polarization control

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machine Learning Algorithms for Signal and image processing 1

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2022年

作者： Deepika Ghai Suman Lata Tripathi Sobhit Saxena Manash Chanda Mamoun Alazab

ISBN: (数字)9781119861850

ISBN: (纸本)9781119861829

Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing machine Learning Algorithms for Signal and image processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech

关键词： speech signal processing image processing computer vision biomedical signal processing adaptive filtering text processing pre-processing feature extraction source separation data decompositions machine learning tasks Speech recognition image reconstruction object classification and detection Healthcare monitoring biomedical systems green energy sign language recognition fake news detection in social media structural damage prediction epileptic seizure detection

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A machine Learning-based Approach for Automatic Grading and Quality Inspection of Indian Mangoes 2

A Machine Learning-based Approach for Automatic Grading and ...

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2nd IEEE-Industrial-Electronics-Society Annual On-Line Conference (ONCON)

作者： Bagchi, Sourav Aditya, Janumpally Varun Kumari, Sneha Dhanraj, Malla Jenamani, Mamata Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India

ISBN: (纸本)9798350357974

Manual visual assessment of mangoes has been problematic for the agriculture sector because of its time-consuming nature and inconsistent evaluation and sorting methods. The advent of automated flaw identification using computer vision and machine learning offers a notable shift and improvement in the visual inspection process. A common issue with mangoes is the presence of dark patches, indicative of disease or rot, which negatively affect the appearance and quality of the fruit. This paper introduces a framework using computer vision which utilizes image analysis and machine learning methods to identify these dark spots, taking into account the mangoes' texture. The proposed framework has a simplified configuration and tuning process, enhancing its ease of deployment in real-world applications. This innovation aligns with the advancements in integrating cutting-edge technologies to optimize efficiency and consistency in agricultural practices, thereby contributing to the evolution of smart agriculture and addressing the challenges and opportunities presented by the next wave of industrial revolution.

关键词： Computer vision System Dark Patches detection image processing Local Binary Pattern (LBP) machine Learning Random Forest SVM Classifier Grading

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Bio-Inspired Electronic Eyes and Synaptic Photodetectors for Mobile Artificial vision

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2022年第2期1卷 76-87页

作者： Choi, Changsoon Seung, Hyojin Kim, Dae-Hyeong Seoul02792 Korea Republic of Seoul08826 Korea Republic of School of Chemical and Biological Engineering Institute of Chemical Processes Seoul National University Seoul08826 Korea Republic of

Conventional imaging and data processing devices are not ideal for mobile artificial vision applications, such as vision systems for drones and robots, because of the heavy and bulky multilens optics in the camera modules. Furthermore, the physically isolated image data processing units of conventional systems induce large power consumption and data latency. For mobile artificial vision applications, electronic eyes, including neuromorphic ones, have been developed inspired by biological eyes and neural networks. Here, we summarize the development of such bio-inspired electronic eyes and synaptic photodetectors (PDs). Bio-inspired electronic eyes, typically consisting of curved image sensor arrays, enable aberration-free imaging and module size miniaturization in addition to other advantageous optical features, such as wide field-of-view and deep depth-of-field. Furthermore, photodetecting devices with synaptic properties can efficiently enhance image contrast because of photon-triggered synaptic plasticity. Therefore, the signal-to-noise ratio of the acquired image can be enhanced, which facilitates efficient image recognition for machine vision. A brief summary of the remaining challenges and prospects concludes this review. © 2022 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Cameras

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Detection and diabetic retinopathy grading using digital retinal images

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND applications 2023年第2期7卷 426-458页

作者： Malhi, Avleen Grewal, Reaya Pannu, Husanbir Singh Bournemouth Univ Poole England Aalto Univ Dept Comp Sci Espoo Finland Thapar Inst Engn & Technol Patiala India

Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient's vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments;one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease.

关键词： machine learning image processing Diabetic retinopathy Exudates Microaneurysms

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