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检索条件"任意字段=Conference on Real-Time Image and Video Processing"

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High-speed and Low-latency 3D Sensing with a Parallel-bus Pattern 10

High-speed and Low-latency 3D Sensing with a Parallel-bus Pa...

引用

International conference on 3D Vision (3DV)

作者： Miyashita, Leo Tabata, Satoshi Ishikawa, Masatoshi Univ Tokyo 7-3-1 HongoBunkyo Ku Tokyo Japan Tokyo Univ Sci 1-3 KagurazakaShjinjuku Ku Tokyo Japan

ISBN: (纸本)9781665456708

High-speed 3D shape sensing is an essential technology for three-dimensional recognition and manipulation in dynamic scenes. However, conventional high-speed sensing methods mainly focus on the image capturing speed and the actual processing time to obtain a point cloud is not optimized in the measurement scheme and sensing pattern configuration. On the other hand, measurement latency is critical to respond in real-time and physically handle the dynamic scenes. This paper introduces a physically stereo-rectified projector-camera system for high-speed and low-latency 3D sensing with fast sequential memory access in the decoding process. Moreover, we configure a structured light pattern named "Parallel-bus pattern" with a De Bruijn torus and clock lines to maximize information density and robustly decode the pattern as data transfer in a parallel bus interface. We measured dynamically moving and deforming objects with the proposed system and evaluated the measurement performance. As a result, the developed 3D sensing system with the parallel-bus pattern achieved 27 K-points measurement at higher than 1000 fps with 0.336 ms latency and 0.838 mm accuracy on average.

关键词： De Bruijn torus high speed image processing low latency spatially encoded structured light

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Performance Enhancement in Agriculture Sector Based on image processing 3

Performance Enhancement in Agriculture Sector Based on Image...

引用

3rd International conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

作者： Hassan, Ali Rehman, Faisal Ashraf, Muhammad Shahbaz Ashfaq, Ahmed Sharif, Hanan Zeeshan, Rana Akram, Salman Akram, Hira Lahore Leads University Department of Computer Science and Information Technology Lahore Pakistan

ISBN: (纸本)9781665461412

In the realm of agriculture and horticulture, machine vision and soft computing approaches have shown promise in overcoming the limitations of traditional methods for identifying plant illnesses utilizing various plant components. In all relevant studies such as fruit grading, leaf lesion area identification, and so on, image segmentation is the first and most important step. In order to diagnose the illness more effectively, a robust approach for numerous crops employing diverse plant components such as Fruit, Flower, and Leaf has been suggested in this study. Before segmentation, the acquired real-time pictures are pre-processed for illumination normalization and color space conversion. To enhance the segmentation outcomes, the conventional ML, image processing and deep learning approaches scheme has been made adaptable, and edge detection transformations have been implemented. To separate the sick regions from photos, the aim function of the Machine Learning approach has been adjusted, and cluster centers have also been upgraded. The many classes of plant illnesses noticed by shooting various types of images of diseases in plants, including many popular plants such as grapes, apple, and tomato. In terms of both broad human observation and computing time, the results achieved are superior. © 2022 IEEE.

关键词： image enhancement

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image Aesthetics Assessment Via Learnable Queries

Image Aesthetics Assessment Via Learnable Queries

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhiwei Xiong Yunfan Zhang Zhiqi Shen Peiran Ren Han Yu School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba-NTU Singapore Joint Research Institute Nanyang Technological University Singapore Alibaba Group Hangzhou China

image aesthetics assessment (IAA) aims to estimate the aesthetics of images. Depending on the content of an image, diverse criteria need to be selected to assess its aesthetics. Existing works utilize pre-trained vision backbones based on content knowledge to learn image aesthetics. However, training those backbones is time-consuming and suffers from attention dispersion. Inspired by learnable queries in vision-language alignment, we propose the image Aesthetics Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries to extract aesthetic features from pre-trained image features obtained from a frozen image encoder. Extensive experiments on real-world data demonstrate the advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1% in terms of SRCC and PLCC, respectively.

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AI-Generated video Forgery Detection and Authentication

AI-Generated Video Forgery Detection and Authentication

引用

International conference for Convergence of Technology (I2CT)

作者： Ayush Kumar Tiwari Aman Sharma Poonam Rayakar Manish Kumar Bhavriya Nisha Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Deep learning has a variety of uses and issues it can solve in the real world, but it also has some limitations. The growing use of AI-Morped videos is one of the most recent and complicated issues. "AI Morphed videos," which are digitally manipulated still or moving visuals, are made using deep learning techniques. In an AI Morphed video, the target’s face is superimposed over the original image so that the altered digital data can be used for online frauds, extortion, pornography, etc. It is getting harder and harder to manually discern between true and false as deep learning develops. Therefore, research and development in the field of AI Morphed video detection are crucial. An overview of the several AI Morphed video detection strategies is completed in time for the classification of feature-based, temporal-based, and deep feature-based AI Morphed video detection. The comparison research is based mostly on the key features used, including the face detection architecture, the deep learning architecture, whether it is video-based or image-based, the dataset used, the frames size, and the dataset size used. Along with the comparison, a semi-supervised GAN architecture is also proposed and built to recognize the AI Morphed video.

关键词： Deep learning Visualization Reviews Face recognition Switches Feature extraction Generative adversarial networks

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In-Sensor Learning on Magnetic Tunnel Junction Devices Using the Forward-Forward Algorithm

In-Sensor Learning on Magnetic Tunnel Junction Devices Using...

引用

Sensors and Information Technology (ICSI), International conference on

作者： Jiayuan Wang Minhui Ji Liyuan Yang Yueguo Hu Mengchun Pan Jiafei Hu Peisen Li College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Stomatology The Fourth Military Medical University Xian China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Aiming at the real-time processing requirements in edge computing devices, we construct in-sensor computing units based on magnetic tunnel junction (MTJ) arrays, which realizes in-situ computation of vector-matrix multiplication with parameters dynamically programmed to adapt to different tasks. Different from the previously used back-propagation (BP) algorithm, we innovatively introduce the forward-forward (FF) algorithm, which utilizes the positive and negative samples “Goodness” optimization mechanism to reduce the computational complexity. In the noisy image classification task, the FF algorithm reduces the training time by more than 90% compared to the BP algorithm and significantly improves the convergence speed when perfect classification results are achieved.

关键词： Training Heuristic algorithms real-time systems Classification algorithms Computational efficiency Noise measurement Junctions Magnetic tunneling image classification Convergence

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Malaysia Traffic Sign Recognition for Autonomous Vehicles with Textual Information Using Computer Vision

Malaysia Traffic Sign Recognition for Autonomous Vehicles wi...

引用

IEEE International conference on Signal and image processing Applications (ICSIPA)

作者： Chear Li Xian Usman Ullah Sheikh Syed Abdul Rahman Syed Abu Bakar Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Traffic sign recognition is crucial for the safe and efficient operation of autonomous vehicles. While previous research has primarily focused on traffic sign recognition in foreign countries, these studies often face limitations such as differing traffic sign designs, language barriers in textual information, and varying environmental conditions. In this paper, we propose a traffic sign detection and recognition system tailored for Malaysia, utilizing Convolutional Neural Networks (CNNs) and Optical Character Recognition (OCR). In this paper, we propose a traffic sign detection and recognition system utilizing You Only Look Once (YOLO) V8 for object detection and EasyOCR to process textual information on selected traffic signs. Our system achieves a mean Average Precision (mAP) of 0.824 and an average processing time of 1.2 seconds per frame, which is comparable to existing literature. Furthermore, the complexity of our method is significantly reduced, enhancing its potential for real-time processing applications, as evidenced by its efficient processing time.

关键词： YOLO image recognition Accuracy Roads Face recognition Optical character recognition Assistive technologies

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BAHAGAP: Design and Development of an image processing-Based Detection and Monitoring System for Early Flood Warnings in High-Risk Zones

BAHAGAP: Design and Development of an Image Processing-Based...

引用

Advanced Research in Computing (ICARC), International conference on

作者： Jan Ricky Almazan Jan Cloyd H. Naval Racell A. Saludes Jeru Pohl Avanzado Nuel Jayson Alcantara Juliet O. Niega Department of Computer Engineering National University - Philippines Calamba City Philippines

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Flooding is one of the major natural disasters that poses serious threats to human life, property, and ecological systems, especially in areas prone to inundation like Calamba, Laguna, Philippines. The rising occurrence of flooding due to climate change and urbanization emphasizes the need for effective early warning systems to mitigate its impacts. The BAHAGAP flood detection and monitoring system developed here runs on solar energy. It uses the techniques of image processing to improve early warning functionalities and strengthen community resilience. BAHAGAP uses advanced image processing to monitor flood levels in real-time and integrates hardware components such as rain gauges, Raspberry Pi 4B microcontrollers, and a solar power system to ensure uninterrupted operation during power outages common in extreme weather. The system provides a sustainable and efficient solution specific to high-risk zones and addresses weaknesses in traditional flood management strategies. Its energy-efficient design, scalability, and ease of deployment make it adaptable to other flood-prone regions. The image processing helps detect the actual flood level with precision and also sends timely warnings to local governments and residents so that they may make informed decisions. In addition, a flood detection model is also trained to obtain high system performance. BAHAGAP integrates hardware reliability with software capabilities, thus giving a holistic approach to flood surveillance. The new system has been a step forward in flood identification and early alert systems to meet the ever-growing need for sustainable solutions to reducing the impact of flooding. The study calls attention to the fundamental role of technology in enhancing resilience to disasters and puts BAHAGAP as an example for further flood management schemes in vulnerable regions.

关键词： image processing Disasters System performance Urban areas Alarm systems real-time systems Hardware Power system reliability Floods Resilience

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Segment Anything in Light Fields for real-time Applications via Constrained Prompting

Segment Anything in Light Fields for Real-Time Applications ...

引用

IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Nikolai Goncharov Donald G. Dansereau University of Sydney Australian Centre For Robotics (ACFR) School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Australia

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Segmented light field images can serve as a powerful representation in many computer vision tasks exploiting geometry and appearance of objects, such as object pose tracking. For those images, segmentation presents an additional objective of recognizing the same segment through all the views. Segment Anything Model 2 (SAM 2) allows for producing semantically meaningful segments for monocular images and videos. Using the video SAM 2 on less general 4D functions such as light fields is ineffective. In this work, we present a novel segmentation method that adapts SAM 2 to the light field domain without retraining or modifying the model. By utilizing epipo-lar constraints, our method produces high quality and view-consistent masks, outperforming the SAM 2 video tracking baseline and working 7 times faster, moving towards a real-time segmentation speed. We achieve this by exploiting the epipolar geometry cues to propagate the masks between the views, probing the SAM 2 latent space to estimate their occlusion, and further prompting SAM 2 for their refinement. The code and additional materials are available at https://***/Projects/LFSAM/.

关键词： Geometry image segmentation Computer vision Adaptation models video tracking Computational modeling Semantics Light fields real-time systems videos

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Design and Implementation of a Fast Convolution Algorithm for Embedded Platform 11

Design and Implementation of a Fast Convolution Algorithm fo...

引用

11th International conference of Information and Communication Technology, ICTech 2022

作者： Yin, Zhenyu Zhang, Feiqing Wang, Jiangbo Xu, Fulong Fan, Chao Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software Shenyang110168 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

In recent years, deep learning has been gradually applied to the industry with great success. As the demand for the lightweight intelligent devices increases, the deployment of deep learning models on embedded platforms to meet users' needs for real-time performance has become a trend in the development of intelligence. However, due to the pursuit of higher accuracy, existing deep learning frameworks are becoming richer in functionality and more complex in computation. A large amount of memory requirements and computational power demands make it challenging to deploy neural network computing frameworks on embedded platforms with limited resources and computational power. The WPOC algorithm is proposed and integrated into the Darknet framework to address real-time image processing based on the Winograd algorithm. Tested on the ZYNQ-7010 platform was passed. The results show that the WPOC algorithm proposed in this paper can effectively speed up image recognition by about six times under the VGG-16 model while ensuring the same accuracy rate. © 2022 IEEE.

关键词： image recognition

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Revolutionizing Road Safety: An Innovative Approach to Pothole Detection and Prevention Using XAI and Deep Learning

Revolutionizing Road Safety: An Innovative Approach to Potho...

引用

Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Senbagavalli M Sheila Mahapatra Afrah Fathima M. K Ruchita L Dept. of CSE & IT Alliance University Bangalore India Department of EEE Alliance University Bangalore India Dept. of CSE Alliance University Bangalore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This paper presents an automated pothole detection system using deep learning, and Explainable AI (XAI), which improves model transparency and decision-making. High-quality images and videos undergo image processing, including edge detection, feature extraction, and deep learning, enabling precise identification and classification of potholes, even in challenging environments. Evaluating potholes is crucial for road maintenance, safety, and reducing costs related to vehicle damage and road repairs. Traditional methods, such as manual inspections and sensors, are costly and time-consuming. The proposed real-time system can spot problem areas before they become severe potholes.

关键词： Deep learning Roads image edge detection Urban areas Maintenance engineering Road safety real-time systems Safety Convolutional neural networks Smart phones

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