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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Sliding Window Detection Algorithm of Grabbing Safety and Grabbing Effect Based on DEM with applications to Unmanned Bridge Grabbing Work

Sliding Window Detection Algorithm of Grabbing Safety and Gr...

引用

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

作者： Yifei Zou WHU Wuhan University

ISBN: (纸本)9781665464697

In the process of grabbing and issuing of the unattended system for fine particles dry bulk materials, the three-dimensional distribution of dry bulk materials is continuous, extremely irregular and fast dynamic. The selection of grabbing area is related to the grabbing safety and grabbing effect. In the extreme case of three-dimensional distribution, the improper selection of grabbing area may lead to accidents such as turning of grabs, closing rope out of groove, poor grabbing effect, or unsatisfied grab efficiency. Based on the distribution characteristics of dry bulk materials, a sliding window detection algorithm based on three-dimensional feature distribution is proposed in this paper. Before grabbing, the detection and analysis are carried out according to the distribution of materials, and the influence characteristics of relatively safe and effective distribution are judged, such as central depression, superelevation slope and other characteristics. The Digital Elevation Model (DEM) three-dimensional feature distribution model of the whole area is established, and the sliding window is further used to detect safety and efficient grab areas. The simulation results show that the sliding window detection algorithm based on DEM three-dimensional feature distribution can predict a more efficient and safe grab area according to the distribution of stacking materials, which provides a new detection method for the application of unattended bridge crane grab engineering.

关键词： Bridges Solid modeling Simulation Grasping Feature extraction Turning Data models

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Study on High-Efficient Derusting Method for vision-Based Derusting Robot 1

引用

14th International conference on Intelligent Robotics and applications (ICIRA)

作者： Liao, Shanwei Fang, Haitao He, Kai Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China Shenzhen Key Lab Precis Engn Shenzhen Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Shenzhen Guangdong Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China

ISBN: (数字)9783030891343

ISBN: (纸本)9783030891343;9783030891336

In this paper, a new type of high-efficient derusting method for wall-climbing-cleaning robot is proposed in order to surmount the shortcomings of current wall-climbing-cleaning robots. It takes advantage of Open Source Computer vision Library (OpenCV) to establish a detection system, and uses a development framework called QT to do the experimental analysis. The experimental results show that the system can complete real-time image recognition in the working environment of the wall-climbing cleaning robot, which makes the foundation for the intelligent trajectory planning of the wall-climbing-cleaning robot.

关键词： Wall-climbing robot image processing Real-time control

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Contactless robust 3D palmprint identification using photometric stereo 44

Contactless robust 3D palmprint identification using photome...

引用

conference applications of Digital image processing XLIV

作者： Smith, Lyndon N. Langhof, Max P. Hansen, Mark F. Smith, Melvyn L. Univ West England Ctr Machine Vis Bristol Robot Lab Bristol S16 1QY Avon England

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

Palmprints are of considerable interest as a reliable biometric, since they offer significant advantages, such as greater user acceptance than fingerprint or iris recognition. 2D systems can be spoofed by a photograph of a hand;however, 3D avoids this by recovering and analysing 3D textures and profiles. 3D palmprints can also be captured in a contactless manner, which is critical for ensuring hygiene (something that is particularly important in relation to pandemics such as COviD-19), and ease of use. Prior work includes low-resolution (relatively unreliable) 3D analysis of wrinkles, or higher resolution ridge analysis that usually employs a commercial (contact based) palmprint scanner. This gap between low and high-resolution palmprint recognition is bridged here using high-resolution non-contact photometric stereo. A camera and illuminants are synchronised with image capture to recover high-definition 3D texture data from the palm, which are then analysed to extract ridges and wrinkles. This novel low cost approach can tolerate distortions inherent to unconstrained contactless palmprint acquisition. Features are found using discrete Fourier transforms. After alignment to a global ridge pattern, feature correspondences are matched, enabling reliable non-contact palmprint identification. The system was evaluated on a medium-sized database and matching was achieved with 0.1% equal error rate, which shows that the approach can achieve accurate and user-friendly palmprint recognition.

关键词： 3D machine vision biometrics texture analysis palmprint recognition

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Occupancy Detection for HVAC Systems Using IoT Edge Computing and vision-Based image processing

Occupancy Detection for HVAC Systems Using IoT Edge Computin...

引用

IEEE International conference on Utility and Cloud Computing (UCC)

作者： Tariq Akhtar Shaheen Khatoon Azhar Mahmood School of Architecture Computing and Engineering University of East London London United Kingdom

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Energy efficiency, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems, is a critical challenge in modern building management due to the increasing energy demands and environmental impacts. This paper focuses on developing optimized object detection models using machine vision for occupancy detection in office environments, aiming to improve HVAC efficiency. The primary objective is to compare three models—YOLOv8n, YOLOv9c, and YOLOv10n—against the Faster R-CNN baseline, emphasizing detection speed, computational efficiency, and small object detection. Data collection involved creating a custom dataset of 1,728 images from office environments, annotated with eight object classes, including persons and office devices. Preprocessing techniques such as grayscale conversion, image resizing, and augmentation improved the model’s ability to detect objects under various conditions, including occlusion and varied camera angles. The models were evaluated based on mAP@50, mAP@50-95, and detection speed. YOLOv9c outperformed Faster R-CNN in speed and accuracy, achieving a mAP@50 of 88.0% and mAP@50-95 of 59.8%, making it the most balanced model. YOLOv8n demonstrated the fastest detection speed, ideal for real-time applications, while YOLOv10n, though less accurate, provided a strong trade-off between speed and precision. Despite these successes, challenges remain, particularly in small object detection and dataset size. Future work includes expanding the dataset to 100,000 images, improving detection of smaller objects, and integrating the object detection models into real-time HVAC control systems. Moreover, deployment on edge devices, transfer learning, and integration with Building Management Systems (BMS) for dynamic HVAC control represent promising areas for future research.

关键词： YOLO HVAC Accuracy Computational modeling image edge detection Data augmentation Real-time systems Energy efficiency Data models Optimization

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Navigation System for Olfactory Mobile Robot by Using machine vision System 7

Navigation System for Olfactory Mobile Robot by Using Machin...

引用

7th IEEE International conference on Smart Instrumentation, Measurement and applications, ICSIMA 2021

作者： Rahardi, Gamma Aditya Anam, Khairul Chaidir, Ali Rizal Larasati, Devita Ayu University of Jember Department of Electrical Engineering Jember Indonesia

ISBN: (纸本)9781728175225

Combustion of fuels to produce heat or other forms of power has been the cornerstone of industrial processes. Liquefied Petroleum Gas (LPG) is an alternative fuel, which is used primarily as a fuel in most spark-ignited internal combustion engines. Therefore, the incidence of leakage in gas cylinders needs to be considered, in order to avoid fire, poisoning, and even death to those around it. Olfactory mobile robot can be used to detect gas content. Thus, the source of the gas leak in the industry and in the storage area for LPG gas cylinders can be identified. This paper emphasizes on robot navigation by using machine vision System (MVS) to speed up the process of finding the exact location of the gas leak source. Several tests were carried out on several aspects. The test results on the motor drivers show that the robot can move well. While the MVS testing shows that the programming algorithm for image processing is able to recognize track borders. Then, the results on gas sensors testing show that the robot can find the source of the gas leak and can adjust it to the required robot speed. The last test, which is on the whole system, shows that the duration needed to find the source of the gas leak is in accordance with the distance from the source of the gas leak. © 2021 IEEE.

关键词： Mobile robots

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A Deep Feedforward Neural Network Model for image Prediction 2

A Deep Feedforward Neural Network Model for Image Prediction

引用

2nd International conference on Emerging Electrical Energy, Electronics and Computing Technologies 2020, ICE4CT 2020

作者： Lee, C.C. Rahiman, M.H.F. Rahim, R.A. Saad, F.S.A. Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Pauh Putra Campus Perlis Arau02600 Malaysia Perlis Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia

Deep learning is a sub-field of machine learning, which inspired by the structure of human brain where it is composed with a lot of neurons that each of them able to perform a simple operation and interact with each other to make a decision. In recent years, deep learning has grown exponentially and have brought revolutionary advances in computer vision-based applications such as image classification, object detection, simultaneous localisation and mapping (SLAM), action and activity recognition, age estimation as well as human pose estimation. Based on the literature review, deep learning techniques applied in the vision-based application such as Convolutional Neural Networks (CNNs), Restricted Boltzmann machines (RBMs), Autoencoder and Sparse Coding mostly focused on solving the feature extraction and classification problems. However, these techniques do not apply to the image processing applications such as image fusion and image reconstruction that require a quantitative knowledge to solve some problems in such fields. The process of obtaining quantitative knowledge in image fusion and reconstruction is a regression problem. In this case, a deep feedforward neural network is applied to solve the regression problem due to its ability to map complicated nonlinear functions. © Published under licence by IOP Publishing Ltd.

关键词： image fusion

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Comprehensive Review of Various Optimization Algorithms for image Captioning 3

Comprehensive Review of Various Optimization Algorithms for ...

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3rd International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Juzer Gabajiwala, Hamza Kamlesh Jethwa, Nishant Joshi, Parth Bimal Anurag Mishra, Arundhati Natu, Prachi MPSTME NMIMS Department of Computer Engineering Mumbai India

ISBN: (纸本)9781665438117

Automatic image captioning is a prominent and challenging research problem in the field of computer vision and natural language processing with applications ranging in biology, automotive and several other domains. Human brain being a complex organ, is able to constantly identify and establish all the intricate details of a scene in real-time, which a machine fails to imitate. However, with neoteric advances in Artificial Intelligence, image captioning has witnessed significant progress. This paper provides a comprehensive review of the latest advancements in caption generation with an emphasis on encoder-decoder and generative adversarial network frameworks. A comparative analysis is performed on existing approaches and methodologies along with an overview of commonly used datasets and evaluation metrics. © 2021 IEEE.

关键词： Generative adversarial networks

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[WIP] Unlocking Static images for Training Event-driven Neural Networks

[WIP] Unlocking Static Images for Training Event-driven Neur...

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International conference on Event-based Control, Communication, and Signal processing (EBCCSP)

作者： Nicolò Carissimi Gaurvi Goyal Franco Di Pietro Chiara Bartolozzi Arren Glover Event-driven Perception for Robotics Italian Institute of Technology Genova Italy

ISBN: (数字)9781665453493

ISBN: (纸本)9781665453509

Event driven cameras have the potential to revolutionise the real-time visual sensory processing paradigm. These asynchronous sensors detect change in the environment with low latency and high dynamic range, allowing for orders of magnitude faster systems than the state of the art using intensity cameras. On the other hand, deep artificial neural networks have refashioned machine vision in the last decade, greatly expanding the reach of viable tasks, supported by the creation of many large scale image datasets. In this work, we present a modality to leverage these large scale datasets for the purpose of training off-the-shelf deep learning architectures and re-appropriating them for event-based tasks. To this end, we describe an event representation, EROS, and a method to convert images to an EROS-like representation such that image datasets can train neural networks for event driven applications.

关键词： Training visualization Statistical analysis machine vision Signal processing Cameras Sensor systems

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A comparative analysis on the existing techniques of wheat spike detection 2

A comparative analysis on the existing techniques of wheat s...

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2nd International conference for Emerging Technology, INCET 2021

作者： Thakur, Anupam Kumar Singh, Suraj Goyal, Neha Gupta, Kapil National Institute of Technology Department of Computer Applications Haryana Kurukshetra India

ISBN: (纸本)9781728170299

Wheat is one of the essential crops for humans. A fair amount of work is done related to wheat, such as determining the phonological stages of wheat plants, the reckoning number of wheat spikes, and detecting diseases like Fusarium head blight(FHB) in wheat plants, measuring the quality of wheat grains. Wheat head detection is a primary task among all the tasks. Object detection is nowadays affecting researchers, and several approaches are related to machine learning, image processing, computer vision, and deep learning are discussed in the literature. This paper provides a comparative analysis of variegated object detection methods used in different applications on wheat head detection and various challenges for wheat detection. With comparative analysis, we are comparing mostly used CNN-based object detection models. © 2021 IEEE.

关键词： Object detection

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Robust and faster zeroth-order minimax optimization: complexity and applications 24

Robust and faster zeroth-order minimax optimization: complex...

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Proceedings of the 38th International conference on Neural Information processing Systems

作者： Weixin An Yuanyuan Liu Fanhua Shang Hongying Liu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China College of Intelligence and Computing Tianjin University China Medical School Tianjin University China and Peng Cheng Lab Shenzhen China

ISBN: (纸本)9798331314385

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and rely on some strict restrictive conditions for ZO estimations. To address these issues, we design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to O(dε-6) to find an ε-stationary point of the function ψ for nonconvex-concave (NC-C) problems, where d is the variable dimension. To the best of our knowledge, ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems. Moreover, ZO-GDEGA requires weaker conditions on the ZO estimations and achieves more robust theoretical results. As a by-product, ZO-GDEGA has advantages on the condition number for the NC-strongly concave case. Experimentally, ZO-GDEGA can generate more effective poisoning attack data with an average accuracy reduction of 5%. The improved AUC performance also verifies the robustness of gradient estimations.

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