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Attention Modules Improve image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study

Attention Modules Improve Image-Level Anomaly Detection for ...

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IEEE/CVF Winter conference on Applications of Computer vision (WACV)

作者： Vieira E Silva, Andre Luiz Simties, Francisco Kowerko, Danny Schlosser, Tobias Battisti, Felipe Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Tech Univ Chemnitz Jr Professorship Media Comp Chemnitz Germany Univ Fed Rural Pernambuco Visual Comp Lab DC Recife PE Brazil

ISBN: (纸本)9798350318920;9798350318937

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

关键词： Algorithms Algorithms and algorithms Applications formulations image recognition and understanding machine learning architectures Remote Sensing

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PVDN-URBAN - A DATASET FOR PROVIDENT VEHICLE DETECTION AT NIGHT IN URBAN SCENARIOS 31

PVDN-URBAN - A DATASET FOR PROVIDENT VEHICLE DETECTION AT NI...

引用

2024 International conference on image processing

作者： Ewecker, Lukas Schiffel, Florian Schwager, Robin Bruehl, Tim Sohn, Tin Stribor Villmann, Thomas Dr Ing Hc F Porsche AG Weissach Germany Saxony Inst Computat Intelligence & Machine Learn Mittweida Germany

ISBN: (纸本)9798350349405;9798350349399

For an autonomous vehicle to drive safely and efficiently, it is important to have a good understanding of the surrounding environment. Having early information about other road users can enable the vehicle to anticipate their behavior and take appropriate actions to avoid dangerous situations. Humans often use light reflections caused by oncoming vehicles at night to anticipate their appearance before they are directly visible. This problem of provident vehicle detection at night has already been studied in rural land road scenarios. However, urban scenarios are more complex, as the number of light sources is higher and the light reflections are more complex. In this paper, we therefore present the PVDN-urban dataset, which is the first dataset to study provident vehicle detection at night in urban scenarios. The dataset contains detailed annotations of light reflections caused by oncoming vehicles at night in urban scenarios. Also, it provides bounding box annotations for all vehicles, to make the dataset usable also for conventional vehicle detection tasks under low-light conditions. We provide an in-depth analysis of the dataset, an efficient annotation method for light reflections, as well as baseline results using state-of-the-art semantic segmentation models. With that, we provide the basis to further study provident vehicle detection at night also for complex urban scenarios. Furthermore, we provide a dataset for the development of algorithms for general vehicle detection at night in urban scenarios.

关键词： Provident Vehicle Detection Automated Driving Dataset Computer vision Object Detection

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An Optimization Design of System Layout Based on machine vision 4

An Optimization Design of System Layout Based on Machine Vis...

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4th International conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Ma, Chuanming Lyu, Lin Zhang, Mingming School of Creative Industry Jilin Animation Institute Jilin Changchun130012 China Department of Integrative Arts Silla University Busan46958 Korea Republic of School of Media & Communication Changchun Humanities and Sciences College Jilin Changchun130117 China The Ninth Middle School of Automotive Economic and Technological Development Zone Jilin Changchun130013 China

machine vision is based on computer, through the comprehensive utilization of a series of advanced theoretical knowledge such as image acquisition equipment, digital processing technology and related algorithms. It can quickly and accurately extract the required features from the input data and describe them, which plays an important role in modern industrial production. machine vision can intuitively express the image information that people are exposed to in a certain way, and process and process it accurately and quickly. This paper aims to use machine vision to quickly obtain spatial position, size and other data, combined with artificial intelligence genetic algorithm, particle swarm algorithm, intelligent plane optimization. This paper mainly uses the experimental simulation method and the comparison method to study the optimization method of the system layout, puts forward two improvement schemes of the storage system layout, and makes a comparative analysis of their space loss parameters. The experimental results show that in terms of total space loss, the value of improved scheme 1 is 0.741, which is higher than that of improved scheme 2. © 2024 The Authors.

关键词： machine vision

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An Improved Depth image Clustering Algorithm Based on Spatial Information 2

An Improved Depth Image Clustering Algorithm Based on Spatia...

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

作者： Yan, Li Su, Shan Hu, Xiao Zhou, Yuquan Chen, Changjun Wuhan University School of Geodesy and Geomatics Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798350331417

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed. © 2023 IEEE.

关键词： Depth image clustering K-Means RGB-D datasets Spatial information

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Computer vision and image Segmentation: LBW Automation Technique 3rd

Computer Vision and Image Segmentation: LBW Automation Techn...

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3rd International conference on machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Nayak, Jeebanjyoti Jena, Jyotsnarani Pradhan, Hrushikesh Das, Jyotiprakash Bhagat, Surendra N. Deparment of Computer Science and Engineering Balasore College of Engineering and Technology Sergarh Balasore India

ISBN: (纸本)9789819939312

Cricket is globally a famous game in which different technologies are being used to help the umpire in correct decision management. The bowl delivery is whether a fair one or LBW which is generally a matter of concern. Sometimes the decision of third umpire also goes wrong. This kind of error in making the decision can change the dimension of the game so, it is essential to make the decision precisely. This paper proposes computer vision and image subtraction technique. At first, it must figure out that whether a delivery is no-ball or the fair one, for that image processing method is used on popping crease by detecting the pixel size. If the delivery is a fair one then, it will track the movement of the ball by using the colour segmentation method, video processing on the pitch. Then by the help of ultra-edge detection technique it can identify whether the ball has touched the edge of the bat or not. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision

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Multifocus Camera Optics with 5^x Extending the Depth of Field 8

Multifocus Camera Optics with 5<SUP>x</SUP> Extending the De...

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conference on Optics, Photonics, and Digital Technologies for Imaging Applications VIII

作者： Laskin, Alexander Laskin, Vadim Ostrun, Aleksei AdlOpt GmbH Rudower Chaussee 29 D-12489 Berlin Germany St Petersburg Natl Res Univ Informat Technol Mech Kronverkskiy Pr 49 St Petersburg 197101 Russia

ISBN: (纸本)9781510673151;9781510673144

Extending the depth of field (DOF) of imaging optics is a longstanding challenge in machine vision, microscopy, photography and cinematography. This paper presents a method to extend DOF of camera lenses up to 5 times by using foto-foXXus - multi-focus quasi afocal optics. The foto-foXXus devices are implemented as achromatic aplanatic optical systems installed in front of camera lenses in such a way that the combined optical system has simultaneously several focuses separated along the optical axis. When applied for imaging a scene, such a combined optical system forms along the optical axis several images of each object of the extended DOF. The inevitable decrease in contrast of the common image, resulting from defocusing of some images from the plane of camera sensor (or film), can be enhanced using specific algorithms in the stage of image processing, which is nowadays an obligatory part of image capture in machine vision or microscopy. This method is very effective in capturing black-and-white objects, such as QR-codes, or in computer vision-based robotic arms for detecting the shape and size of objects. Direct measurements of the modulation transfer function (MTF) and through-focus MTF curves for a system consisting of a foto-foXXus and a state-of-the-art machine vision objective confirm the increase in depth of focus of the combined optical system and, consequently, depth of field in the Object space. The paper presents description of the foto-foXXus devices, measurements data of MTF and through-focus MTF-curves using the MTF test bench, as well as examples of imaging real objects demonstrating effective extending depth of field.

关键词： extended depth of field DOF imaging camera optics machine vision microscopy industrial inspection photography cinematography

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Hybrid Neural Network Based on Parameterized Quantum Circuits for image Classification

Hybrid Neural Network Based on Parameterized Quantum Circuit...

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2024 Cross Strait Radio Science and Wireless Technology conference

作者： Zhang, Jun Xue, XiLing Ruan, Yue Anhui Univ Technol Sch Comp Sci & Technol Maanshan Anhui Peoples R China

ISBN: (纸本)9798331507800;9798331507794

image classification is a core task in the field of computer vision, with significant implications for applications such as medical imaging and autonomous driving. Classical neural network architectures face numerous challenges when dealing with high-dimensional and complex data, particularly in terms of feature extraction and data processing capabilities, limiting their performance in certain applications. Quantum computing offers promising new approaches to processing high dimensional complex data, most notably through Parameterized Quantum Circuits (PQC) as quantum hybrid neural networks for image classification. In this paper, we propose a hybrid neural network featuring a specific PQC structure designed to generate entanglement in accordance with sample images. This PQC structure utilizes the adjacency matrix of the corresponding graph of the sample image, embedding adjacency relationships among nodes into the entanglement module, thereby generating entanglement aligned with the features of specific images. Numerical results validate the effectiveness of our PQC model across various image classification tasks, enhancing the efficiency of feature learning and representation. Our model demonstrates significant advantages in classification accuracy and learning efficiency on classical datasets. Our research not only advances the theoretical understanding of hybrid quantum networks but also paves the way for practical implementations in real-world scenarios.

关键词： image classification quantum machine learning parameterized quantum circuits

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Semantic Threads Enabling image-Text Retrieval via VQA Transformers 1

Semantic Threads Enabling Image-Text Retrieval via VQA Trans...

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1st International conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Noorbhasha, Junnubabu Guddeti, Rohitha Lingutla, Satvika Etikikota, Sujitha Kumkumkari, Santhosh Madanapalle Institute of Technology and Science Computer Science and Technology Madanapalle India

ISBN: (纸本)9798350365405

The integration of vision and language has propelled the advancement of artificial intelligence systems. Visual Question Answering (VQA) stands at the nexus of computer vision and natural language processing, enabling machines to comprehend and respond to image-related queries. This paper introduces a novel VQA approach harnessing the capabilities of the BLIP (Bootstrapping Language image Pretrained) model, a transformer-based architecture esteemed for its natural language understanding prowess. The methodology involves image preprocessing, and question translation into a standardized language for efficient processing by BLIP. Mainly, the study integrates multilingual support into the VQA framework, facilitating seamless interaction with users across diverse linguistic backgrounds. Through rigorous experimentation, this paper demonstrates the effectiveness of our approach in accurately answering questions in various languages These findings underscore the robustness and adaptability of the BLIP model in handling multilingual inputs, thereby enhancing accessibility and usability in real-world applications. This research contributes to advancing the state-of-the-art in VQA systems by addressing language barriers and promoting inclusivity in human-machine interaction. © 2024 IEEE.

关键词： Semantics

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Design of Robot vision Recognition System

Design of Robot Vision Recognition System

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International conference on image processing, Computer vision and machine Learning (ICICML)

作者： Wen, Binbin Wang, Yifan Xue, Yuming Tianjin Univ Technol Sch Integrated Circuit Sci & Engn Tianjin Peoples R China

In this paper, a robot vision recognition system is developed based on the Robot Operating System (ROS) and the Open Source Computer vision (Open CV), which mainly implements face recognition, object detection, motion... 详细信息

ISBN: (纸本)9781665464680

关键词： Face recognition Edge detection Motion analysis

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Generating Caption From images Using Flickr image Dataset 15

Generating Caption From Images Using Flickr Image Dataset

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Piyush Kaur, Kiranpreet Gandotra, Aaryan Patial, Pranay Radhika Gaba, Sehaj Chandigarh University Department of Cse Mohali India

ISBN: (纸本)9798350370249

In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of Artificial Intelligence. Enabling the machine to describe images with the same skillful accuracy as the human has important applications in various fields such as robotic vision, manufacturing, and beyond This project integrates recurrent neural networks with is a topic of contextual parallelism dedicated to extracting features from images Natural Language processing Computer vision and integrating them seamlessly, this research provides insights a it goes further on this interdisciplinary topic. Additionally, annotations for the sample images are created and performed a comparative analysis of different feature extraction and encoder patterns to determine which model provided the highest accuracy and delivered the desired results. © 2024 IEEE.

关键词： Caption from an image Caption of an image Encoder- Decoder outputs text from an image

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