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检索条件"任意字段=Conference on Real-Time Image Processing 2006"

共 25678 条记录，以下是221-230 订阅

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A real-time image Capture and processing System for a Wide-area Aerial Camera 8

A Real-time Image Capture and Processing System for a Wide-a...

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8th International conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Wang, Hongmin Zhang, Qian Xu, Yun Liu, Changhai Wang, Baohua Zhou, Ziwei Wen, Gaojin Beijing Institute of Space Mechanics and Electricity Beijing China Institute of Applied Physics and Computational Mathematics Laboratory of Computational Physics Beijing China Troop of 63768 Xian China

ISBN: (纸本)9798350302455

This paper introduces a real-time image capture and processing system for a wide-area aerial camera. The system adopts a very efficient software system architecture, which includes center control, camera control, capture control, platform control, image data processing and other components. image data processing methods such as geo-location based on feature point matching, field stitching based on camera geometric installation relationship, moving target extraction based on sequence image phase correlation matching and frame difference are proposed to enable the camera system to effectively monitor specific areas. The efficiency and usefulness of the system are verified by multiple image processing tasks. © 2023 IEEE.

关键词： Cameras

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Virtual reality image Generation and Enhancement Based on real-time Model Simplification 5th

Virtual Reality Image Generation and Enhancement Based on Re...

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5th International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Luo, Lilei Shandong Vocational College of Science and Technology Shandong Weifang261053 China

ISBN: (纸本)9789819751839

Virtual reality technology is a new practical technology that comes directly from the application and involves many disciplines. It is a comprehensive and integrated technology that combines advanced computer technology, sensing and measurement technology, simulation technology, and microelectronics technology. The virtual image is a dynamic image expression in the field of visual image, which is digitally generated by a computer and has its own unique image construction method. As the application of virtual reality technology deepens, the complexity and realism of virtual scenes are required more and more, far exceeding the processing capacity of computer graphics hardware. Therefore, there is an urgent need to resolve the contradiction between the complexity of the scene and the real-time nature of the interaction. In this paper, based on the review of related literature, virtual reality image generation methods and augmentation technology methods are reviewed. Several problems of simplification of multi-resolution models based on computer technology and the classification of algorithms are constructed, and the practical application of multi-resolution models in solving the problems of complexity and real-time scenes is discussed. It is demonstrated that the realism and practicality of the images are achieved by constructing smooth curve models and reducing the coupling of image errors so that the virtual reality images are generated with improved accuracy and clarity for better research use. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual environments

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Mixed Algorithm real time image processing for Robotic Drones in Deterministic Environments 2

Mixed Algorithm Real Time Image Processing for Robotic Drone...

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2nd International conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Soni, Kirtik Palanivelan, M. Rajalakshmi Engineering College Department of Ece Chennai India

ISBN: (纸本)9798350328202

This paper presents a mixed algorithm approach for real-time image processing on air and land based robotic drones that operate in highly deterministic environments such as automated warehouses. A combination of heuristic, multi state algorithmic solution based on Canny Edge and a CNN deep learning-based solution is proposed to ensure maximum performance out of low power embedded hardware. A heuristic i.e., rule-based approach is used for fixed, simple tasks such as navigation, docking, charging, identifying racks etc. On detecting an edge case or an anomaly beyond the scope of heuristics, the program switches to a deep learning-based approach in real time for extracting context and navigating the complex scenario. This computationally intense mode only lasts for short bursts and switches back to an appropriate, power efficient algorithm. The paper aims to study the effects of different embedded computer vision methodologies taking into account the context switching time and their effect on the computational resources such as CPU, RAM and ROM along with variations in CPU temperature and verify the effectiveness of a mixed algorithm approach on a Raspberry Pi 4 single board computer running 'Raspbian Bookworm' a 64bit Debian based operating system interfaced with a CSI Camera for visual feed. © 2024 IEEE.

关键词： Drones

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Enhanced Soldier Terrain Visualization System Using image processing and time of Flight Cameras with Transparent 5G Antenna 5

Enhanced Soldier Terrain Visualization System Using Image Pr...

引用

5th International conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Varsha, J. Prabhakar, Parimala Department of Electronics and Telecommunication Bangalore India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528539

The Enhanced Soldier Terrain Visualization System Using image processing and time of Flight Cameras with Transparent 5G Antenna project revolutionizes battlefield operations by integrating technologies like ToF cameras, advanced image processing, and Raspberry Pi units. This offers real-time 3D terrain visualization to soldiers via laptop software. The system includes a webcam and ToF camera on a robotic arm, capturing precise 3D data and generating terrain maps. Raspberry Pi efficiently collects and transmits data, enhancing soldier awareness and planning. The custom-made Transparent antenna is designed to handle the load of transmission of data over different frequencies © 2024 IEEE.

关键词： Robotic arms

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real time Quality Detection and Automatic Recognition of Concrete Structures: image processing Algorithm Based on Deep Learning 4

Real Time Quality Detection and Automatic Recognition of Con...

引用

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

作者： Liu, Bing College of Intelligent Manufacturing and Automobil Chongqing Vocational College of Transportation Chongqing402247 China

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set;then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value i

关键词： Decision making

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Improving Endoscopic image Quality through the Use of High Dynamic Range Imaging-Like Method with real-time Performance

Improving Endoscopic Image Quality through the Use of High D...

引用

45th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Kumar, Gurrala Ajay Rahul, G. S. Preejith, S. P. Sivaprakasam, Mohanasankar Indian Inst Technol IIT Madras Healthcare Technol Innovat Ctr HTIC Madras Tamil Nadu India IIT Madras Dept Elect Engn Madras Tamil Nadu India

ISBN: (纸本)9798350324471

High Dynamic Range (HDR) imaging is a digital image processing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in real time. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.

关键词： Endoscopy

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A fast pansharpening method based on object-space-consistency and GPU parallel processing 7

A fast pansharpening method based on object-space-consistenc...

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7th International conference on Advanced Electronic Materials, Computers, and Software Engineering, AEMCSE 2024

作者： Xu, Mingyuan Zhang, Xueqi Cai, Zhengyang Hubei University of Technology No. 28 Nanli Road Hongshan District Wuhan430068 China

ISBN: (纸本)9781510681866

The fusion of panchromatic and multispectral images is a prerequisite for obtaining multispectral images with high spatial resolution (HRMS). The traditional data processing mode requires that the acquired panchromatic data and multispectral data be transmitted to the ground through the star-ground link, and then a series of correction, registration and fusion operations are conducted in order to obtain HRMS. This processing mode has low timeliness and cannot meet the demand of on-board real-time processing. To solve this problem, we perform efficient registration and fusion processing for the images containing the target region acquired in real time during satellite orbit. Specifically, we propose a fast virtualized registration method based on object-space-consistency, which is capable of obtaining accurately registered images under the premise of ensuring real-time performance. On this basis, we utilize GPU acceleration to parallelize the time-consuming steps of the traditional fusion algorithm, thereby achieving rapid fusion processing while guaranteeing the fusion performance. Through experiments on an on-board simulation system, our method has been able to achieve 15.10 times speedup compared to the traditional registration and fusion processing while guaranteeing good performance. © 2024 SPIE.

关键词： image fusion

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FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK AND image processing 15

FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK A...

引用

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

作者： Srilatha, Boddu Ithvika, C.H. Meenakshi, Patil Pradhan, Sasmita Kumari Rani, K. Pushpa Jayanth, M. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad500043 India Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad500043 India Department of of CSE-CyS DS and AI&DS VNR VJIET Bachupally Hyderabad India

ISBN: (纸本)9798350370249

The increased use of modern printing and scanning technologies has led to a significant rise in counterfeit currency production, posing a serious threat to global economies. To tackle this growing issue, our project, titled "Fake currency detection using Convolutional Neural Networks and image processing," introduces an innovative solution that utilizes artificial intelligence (AI) and machine learning for efficient counterfeit detection. Financial institutions, banks, and businesses are facing heightened vulnerability to counterfeit currency, resulting in considerable financial losses and a decrease in the value of genuine money. Current currency detection systems often rely on time-consuming traditional methods and manual inspection, which are prone to human error. Even the counterfeit detection machines in use have limitations when it comes to identifying sophisticated counterfeit notes. Our project addresses these challenges by proposing an advanced system that integrates convolutional neural networks (CNNs) and image processing techniques. Given the advancements in printing and scanning technologies, counterfeiting has evolved into a more sophisticated and widespread problem. Traditional currency detection methods, rooted in hardware and image processing, have proven to be inefficient and time-consuming. Hence, there is a critical need for a more robust and rapid solution to detect counterfeit currency. Our proposed approach employs a transfer-learned CNN, a deep learning model trained on a dataset comprising real and fake currency images. The CNN learns the intricate features of both genuine and counterfeit banknotes, allowing it to accurately identify fake currency in real-time. The transfer learning process enables the CNN to leverage knowledge gained from a diverse dataset, improving its ability to recognize subtle patterns associated with counterfeit notes. The primary components of our project include a diverse dataset with images of real and fake currenc

关键词： Convolutional neural networks

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Layer-by-layer Learning In image processing For Unmanned Aerial Vehicle 7

Layer-by-layer Learning In Image Processing For Unmanned Aer...

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7th IEEE International conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Sineglazov, Victor Chynnyk, Petro NTUU Igor Sikorsky Kyiv Polytechnic Institute Dept. of Artificial Intelligence Kyiv Ukraine

ISBN: (纸本)9798331534141

This work explores an innovative approach to image processing that provides high efficiency and accuracy in computer vision tasks. In this work, step-by-step learning of quantum machine learning models is considered, in which each layer of the model specializes in specific functions, which significantly improves the quality of image recognition and classification. Special attention is paid to the adaptation of this approach to real-time conditions important for UAVs, where the processing of large volumes of data must take place under conditions of limited resources. The results of experiments are presented, which demonstrate the advantages of step-by-step learning in comparison with traditional methods of image processing, which allows to achieve higher accuracy and speed of data analysis during various tasks, such as monitoring, mapping and object detection in the field. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Comparative evaluation of two algorithmic methods to enhance autonomous vehicle vision through de-raining imagery techniques 47

Comparative evaluation of two algorithmic methods to enhance...

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conference on Applications of Digital image processing XLVII

作者： Ajami, Hanieh Nigjeh, Mahdi Kargar Nigjeh, Mahsa Kargar Umbaugh, Scott E. Southern Illinois Univ Edwardsville Comp Vis & Image Proc Lab Dept Elect & Comp Engn Edwardsville IL 62026 USA

ISBN: (纸本)9781510679344;9781510679351

The effectiveness of autonomous vehicles relies on clear visual input, which rain can significantly obstruct. Rain streaks degrade the quality of captured images and videos, affecting both user perception and the functionality of outdoor vision systems, such as those in autonomous vehicles. This visual degradation impacts the vehicle's ability to interpret its environment, increasing the risk of driving in rainy conditions. Researchers have responded to this challenge by developing various rain removal algorithms, ranging from single-image to video-based approaches, each with its own strengths and weaknesses. This research aims to develop two novel, efficient single-image rain removal algorithms that strike a balance between high performance and quick execution. The proposed algorithms will address the need for an effective de-raining technique suitable for real-time use in autonomous vehicles. By improving visibility in rainy conditions, this innovation will enhance the performance and safety of autonomous vehicles, contributing to advancements in the field. A survey involving ten image processing experts and professionals, who evaluated the results of both algorithms based on perceived quality and improvement, revealed that Algorithm 1 received a higher average rating (0.58) compared to Algorithm 2 (0.43). Although Algorithm 1 is slightly preferred based on average participant ratings, Algorithm 2's superior edge preservation and image sharpness make it more favorable for applications demanding high accuracy and detailed image retention. Overall, the project meets the demand for real-time rain removal in autonomous vehicles and provides valuable insights into the effectiveness of Algorithm 1 in de-raining images compared to Algorithm

关键词： Autonomous vehicle vision Rain streaks removal Computer vision enhancement CVIPtools Python Algorithmic methods image segmentation

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