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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Shivam Kumar Swapnadeep Poddar Zhenghao Long Zhiyong Fan Dept. of ECE The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798331504168

ISBN: (纸本)9798331504175

The human visual system serves as an inspiration for an efficient image sensor for applications in robotics, sensing and computer vision. Inspired by the retina, we present a perovskite nanowire based artificial vision system for integrated sensing and data preprocessing. The sensor shows stable response to different learning and forgetting visual stimuli. We demonstrate the capabilities of the artificial vision system with a crossbar array and integration with perovskite-based memory for different shape recognition.

关键词： image sensors Visualization Shape Array signal processing machine vision Visual systems vision sensors Robot sensing systems Retina Perovskites

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A vision transformer-based automated human identification using ear biometrics

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JOURNAL OF INFORMATION SECURITY AND applications 2023年 78卷

作者： Mehta, Ravishankar Shukla, Sindhuja Pradhan, Jitesh Singh, Koushlendra Kumar Kumar, Abhinav Natl Inst Technol Jamshedpur Dept CSE Machine Vis & Intelligence Lab Jamshedpur 831014 Jharkhand India Motilal Nehru Natl Inst Technol Allahabad Allahabad 211001 Uttar Pradesh India

Recent years vision Transformers (ViTs) have gained significant attention in the field of computer vision for their impressive performance in various tasks, including image recognition and machine translation tasks, question answering, text classification, image captioning. ViTs performs better on several benchmark image datasets such as imageNet with fewer parameters and computation compared to CNN-based models. The self-attention part performs the feature extraction component of the convolutional neural network (CNN). The proposed model provides a framework on vision transformer-based model for 2D ear recognition. The self-attention part is jointly applied with Convolutional Neural Network (CNNs) in the proposed model. Adjustments and fine-tuning has been done based on the specific characteristics of the ear dataset and the desired performance requirements. In the field of deep learning, the application areas of the CNNs have been proven to be de-facto mainly due to its learning capability of spatially local representations based on their inductive biases, learning the global representation further enhances the recognition accuracy through self-attention mechanism of vision transformers (ViT's). This has been made possible by direct applications of transformer on to the sequence of image patches for better performance in classifying the images. The proposed work utilizes various patch size of images during the model training. From the experimental analysis, it has been observed that with patch size 16 x 16 it achieves highest accuracy of 99.36%. The proposed model has been validated with the Kaggle and iiTD-ii data set. The efficiency of the proposed model over the existing models has been also reported in the present work.

关键词： vision transformer Patch Embedding Attention network Data augmentation

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Optimizing image-based deep learning for energy geoscience via an effortless end-to-end approach

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartB期215卷

作者： Koeshidayatullah, Ardiansyah King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Dept Geosci Dhahran 31261 Saudi Arabia

The rapid growth of artificial intelligence (AI) technology and its applications in recent years has transformed the process of data analytics in many scientific fields, including geoscience. Geoscience has traditionally been a descriptive science and fundamentally relies upon visual recognition and identification of different geological features, from satellite images to subsurface seismic, to study Earth's history. Geological image data provides immense potential to apply advanced AI methods, such as deep learning to improve and optimize different geological and geophysical characterization workflows. Despite the increasing efforts and interest toward using AI in geosciences, its actual potential remains untapped, and further exploration is required. The prospect of AI application in geosciences is primarily hindered by the following: (i) limited availability of high-quality labeled datasets and (ii) inherited imbalance dataset distribution. These limitations are compounded by overexploitation of the transfer learning method to mitigate such issues, discarding the interpretability of the AI black-box problems. In this study, a robust and effortless strategy is proposed to overcome the limitations and simultaneously reduce our dependency on to the transfer learning method. Among the various methods available to mitigate these issues, only traditional data augmentation is heavily used in geosciences. This study, therefore, explored and developed a workflow by combining three readily available methods to maximize the performance of machine learning algorithms when dealing with a limited and imbalanced geoscience dataset. Here, the proposed method follows three robust and straightforward end-to-end steps: (i) combining traditional and advanced data augmentation (e.g., CutOut and CutMix) techniques to enhance localization and generalization performance;(ii) employing an algorithm-level class weight method to minimize detrimental impact and performance bias due to class

关键词： Artificial Intelligence Deep learning Geosciences Interpretability Energy Computer vision

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Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Automated post-earthquake damage assessment of stone masonry...

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作者： Pantoja Rosero, Bryan German Swiss Federal Institute of Technology in Lausanne

学位级别：博士

Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task automatically, efficiently, and objectively. Our vision for an automated post-earthquake evaluation begins with image data, such as that obtained by an Unmanned Aerial Vehicle, which is then processed to detect damage and generate a Finite Element Method (FEM) model. This thesis aims to realize this vision for free-standing stone masonry buildings. The main objective of the current research is to propose robust and computationally efficient methodologies to automatically generate 3D models for free-standing stone masonry buildings and provide information on damage detected in RGB images. This allows for an effective and more objective post-earthquake damage assessment with straightforward documentation, allowing future correlation of damage information with the mechanical properties of the model. RGB images were used for two purposes, i. e., 3D model generation and damage detection. Related to 3D models, an image-based pipeline was developed to automatically create level of detail (LOD) models, specifically LOD3, using structure-from-motion and semantic segmentation, in order to produce a geometrical representation of a building. In contrast to the existing works, the method does not rely on post-processing of extremely precise 3D models, does not use predefined templates, does not require human manipulation, and provides semantic understanding of the final model's components. Cracks were detected using state-of-the-art deep learning approaches, which were complemented with a TOPO-Loss function that does not require pixel-precise labels and emphasizes the continuity of the crack topology. When assessing the mechanical effect of a crack, not only the crack geometry but also the crack opening in Mode I and ii are impo

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Quality control automation of metallic surface using machine vision

Quality control automation of metallic surface using machine...

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Conference on Photonics applications in Astronomy, Communications, Industry, and High Energy Physics Experiments

作者： Lenty, Bartosz Kwiek, Pawel J. AGH Univ Sci & Technol Al Mickiewicza 30 Krakow Poland

ISBN: (数字)9781510649569

ISBN: (纸本)9781510649569;9781510649552

The article presents the usage of machine vision to automate quality control (QC) of metallic surfaces. QC include detection of selected defects of metallic surface, i.e. scratches, cracks. Imaging using the scatter method has been proposed, resulting in greater contrast. The article provides a detailed description of the measurement stand, image acquisition method and image analysis algorithm. The project's principal aim is to construct an automatic system that controls the state of the surface with a frequency of 6 Hz.

关键词： machine vision vision system 2D image image processing quality control scattering scratches detection

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Artificial Intelligence applications in Intrusion Detection Systems for Unmanned Aerial Vehicles

Artificial Intelligence Applications in Intrusion Detection ...

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作者： Hamadi, Raby King Abdullah University of Science and Technology

学位级别：硕士

This master thesis focuses on the cutting-edge application of AI in developing intrusion detection systems (IDS) for unmanned aerial vehicles (UAVs) in smart cities. The objective is to address the escalating problem of UAV intrusions, which pose a significant risk to the safety and security of citizens and critical infrastructure. The thesis explores the current state of the art and provides a comprehensive understanding of recent advancements in the field, encompassing both physical and network attacks. The literature review examines various techniques and approaches employed in the development of AI-based IDS. This includes the utilization of machine learning algorithms, computer vision technologies, and edge computing. A proposed solution leveraging computer vision technologies is presented to detect and identify intruding UAVs in the sky effectively. The system employs machine learning algorithms to analyze video feeds from city-installed cameras, enabling real-time identification of potential intrusions. The proposed approach encompasses the detection of unauthorized drones, dangerous UAVs, and UAVs carrying suspicious payloads. Moreover, the thesis introduces a Cycle GAN network for image denoising that can translate noisy images to clean images without the need for paired training data. This approach employs two generators and two discriminators, incorporating a cycle consistency loss that ensures the generated images align with their corresponding input images. Furthermore, a distributed architecture is proposed for processing collected images using an edge-offloading approach within the UAV network. This architecture allows flying and ground cameras to leverage the computational capabilities of their IoT peers to process captured images. A hybrid neural network is developed to predict, based on input tasks, the potential edge computers capable of real-time processing. The edge-offloading approach reduces the computational burden on the centralized system a

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Noncontact Clearance Measurement Research Based on machine vision 11th

Noncontact Clearance Measurement Research Based on Machine V...

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11th International Workshop of Advanced Manufacturing and Automation, IWAMA 2021

作者： Che, Kai Lu, Dongli Guo, Jun Chen, Yufeng Peng, Guosheng Xu, Lianbing School of Electrical and Information Engineering Hubei University of Automotive Technology Hubei Shiyan442002 China Zhejiang Hong Cheng Computer Systems Co. Ltd. Hangzhou311100 China

ISBN: (纸本)9789811905711

Parts assembly clearance measurement is facing a trend towards high-precision and noncontact. This work aims to measure clearance by image processing based on machine vision. The machine vision system is to highlight the assembly clearance region. Hence, clearance regions are segmenting, and rotating to vertical, then get the geometric center of the region and the inclination relative to the horizontal direction, the two points intersecting the boundary of the region can be obtained through the linear relationship, the clearance width is the pixel distance between two points mapped to the actual width in the world coordinate system. Results of the measurement results show that the system works effectively and meets the requirements, which makes it suitable for industrial applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Real Time Object Detection and Clasification Using Small and Similar Figures in image processing 3

Real Time Object Detection and Clasification Using Small and...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Thakur, Kanika Banerjee, Pallab Naaz, Farheen Banerjee, Probal Amity School of Engg. and Technology Amity University Jharkhand Ranchi India Govt. Polytechnic Jharkhand Ranchi India

ISBN: (纸本)9798350302288

In the present time, there has been many adaptations of Object Detection is developed. Object Detection means catching the object name and it's other characteristics in an image or a video. This field is known to be the most difficult technique in computer background. In this very paper, a new and simplest way of detection images and objects is shown using the machine Learning approach. Two methodologies is adapted in this project. First is Open cv algorithm which is a library of machine learning software and open to all developers. It provide an infrastructure that is common for all computer applications which helps to accelerate machine learning commercial works and products. Second is Yolo algorithm short for You Only Look Once. This algorithm is used for the prediction of objects and images which defines the point location of that object in dimensional way. In this, the technique of object detection is applied to like figures which are small and similar to one another. © 2023 IEEE.

关键词： Computer vision

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Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing 12

Deep Ensemble Learning with Frame Skipping for Face Anti-Spo...

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12th International Conference on image processing Theory, Tools and applications, IPTA 2023

作者： Muhammad, Usman Hoque, Md Ziaul Oussalah, Mourad Laaksonen, Jorma University of Oulu Center for Machine Vision and Signal Analysis Finland Aalto University Department of Computer Science Finland

ISBN: (纸本)9798350325416

Face presentation attacks, also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario. © 2023 IEEE.

关键词： Face recognition

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Omni-TransPose: Fusion of OmniPose and Transformer Architecture for Improving Action Detection 16th

Omni-TransPose: Fusion of OmniPose and Transformer Architect...

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16th Asian Conference on Intelligent Information and Database Systems (ACiiDS)

作者： Phu, Khac-Anh Hoang, Van-Dung Le, Van-Tuong-Lan Tran, Quang-Khai Hue Univ Univ Sci Fac Informat Technol Hue City 530000 Vietnam Cao Thang Tech Coll Fac Informat Technol Ho Chi Minh City 720000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 720000 Vietnam Hue Univ Dept Acad & Students Affairs Hue City 530000 Vietnam

ISBN: (纸本)9789819759330;9789819759347

The field of computer vision research has been experiencing rapid and remarkable development in recent years, aiming to analyze image and video data through increasingly sophisticated machine learning models. In this research domain, capturing and extracting relevant features plays a crucial role in approaching the detailed content and semantics of image and video data. Among these, skeleton data, with the ability to represent the position and movements of human body parts, along with its simplicity and independence from external factors, has proven highly effective in solving human action recognition problems. Consequently, many researchers have shown interest and proposed various skeleton data extraction models following different approaches. In this study, we introduce the Omni-TransPose model for skeleton data extraction, constructed by combining the OmniPose model with the Transformer architecture. We conducted experiments on the MPii dataset, using the Percentage of Correct Key Points (PCK) metric to evaluate the effectiveness of the new model. The experimental results were compared with the original OmniPose model, demonstrating a significant improvement in skeleton extraction and recognition, thereby enhancing the capability of human action recognition. This work promises to provide an efficient and powerful method for human action recognition, with broad potential applications in practical scenarios.

关键词： Computer vision Deep learning Skeleton data

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