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2024 International conference on image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Huang, Shi-Zhao Wang, En-Liang Anhui Xinhua University No. 555 Wangjiang West Road Anhui Province Hefei City230088 China

ISBN: (纸本)9781510680425

A polarization image frame capture device based on Camera Link interface is proposed and implemented. The polarization image frame capture device adopts large-capacity buffer device, multi-bus switching and DSP technology, which solves the problem of multi-channel asynchronous image parallel acquisition and high-speed real-time polarization image synthesis. It can capture and synthesize the larger polarization image frames in real time. © 2024 SPIE.

关键词： Cameras

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FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK AND image processing 15

FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK A...

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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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A Learned image Compression Method for Electricity Tower Monitoring Based on the Transformer-CNN-Based Network 8th

A Learned Image Compression Method for Electricity Tower Mon...

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8th International Joint conference on Web and Big Data and Web-Age Information Management (APWeb-WAIM)

作者： Ding, Xinlei Wang, Yuewei Huang, Xiaohui Chen, Yunliang Li, Jianxin China Univ Geosci Sch Comp Sci Wuhan Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia

ISBN: (纸本)9789819772315;9789819772322

The way to monitor the safety of infrastructure facilities such as power towers by human beings on the ground faces great risks under extreme environmental and climatic conditions. Therefore, automatic, real-time and long-term monitoring of power towers in remote areas in the field through sensors, network communication and other technologies is the trend of today's technology development. However, when the real-time monitoring of high-definition images are captured by the camera and sent to the server for subsequent processing and analysis, the sheer volume of real-time image data causes pressure on the transmission network and the server side. Considering that in the real-time application of remote monitoring technology, the monitoring data obtained from sensors has redundant information, such as similar structure and repetitive background. We only need to extract the image data of the object of interest and compress it before transmission, therefore the image data is significantly transmitted to the server side, improving the efficiency of both network transmission and data processing. In this paper, we propose a learned image compression model by integrating a ResNet50 model and a Transformer-CNN-based network to reduce the image data that needs to be transmitted through the network and processed on the server side. The real-time image data is first sent to the ResNet50 model to extract objects of interest, which are then compressed by the Transformer-CNN network to realize remote monitoring by Learned image Compression (LIC) methods and communication techniques. Experimental results based on datasets collected in real-world scenarios indicate that the proposed solution effectively improves the compression performance compared to state-of-the-art methods. The average improvements in PSNR and MS-SSIM metrics are over 30%.

关键词： Remote monitoring Learned image compression Transformer-CNN-Based Network Target detection

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TinyML-On-The-Fly: real-time Low-Power and Low-Cost MCU-Embedded On-Device Computer Vision for Aerial image Classification

TinyML-On-The-Fly: Real-Time Low-Power and Low-Cost MCU-Embe...

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IEEE Space, Aerospace and Defence conference (SPACE)

作者： Samanta, Riya Saha, Bidyut Ghosh, Soumya K. Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798350367393;9798350367386

Aerial image classification is essential to intelligent surveillance and monitoring systems. Traditional computer vision methods either uses computational offloading to high-end servers or edge devices. However, unmanned aerial vehicles (UAVs) platforms have resource and power constraints. Aerial image classification is complicated and less-expensive UAVs lack processing power and cameras. Even with large-scale computing environments, methods for classifying images are difficult to apply to aerial imagery. We propose TinyAerialNet leveraging TinyML for real-time inference on a resource-constrained ESP32 CAM. The model tested on AIDER dataset, achieves 88% on-device accuracy in the micro-controller with 103.9 KB RAM and 850 milliseconds for inference.

关键词： Aerial image Classification Computer Vision MobileNet TinyML On-device Inference UAV

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Tunable nonlocal metasurfaces based on graphene for analogue optical computation

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OPTICAL MATERIALS EXPRESS 2023年第5期13卷 1475-1487页

作者： Khodasevych, Iryna Wesemann, Lukas Roberts, Ann Iacopi, Francesca Univ Technol Sydney Fac Engn & IT ARC Ctr Excellence Transformat Metaopt Syst Sch Elect & Data Engn Broadway NSW 2007 Australia Univ Melbourne ARC Ctr Excellence Transformat Metaopt Syst Sch Phys Melbourne Vic 3010 Australia

Meta-optical devices have recently emerged as ultra-compact candidates for real-time computation in the spatial domain. The use of meta-optics for applications in image processing and wavefront sensing could enable an order of magnitude increase in processing speed and data throughput, while simultaneously drastically reducing the footprint of currently available solutions to enable miniaturisation. Most research to date has focused on static devices that can perform a single operation. Dynamically tunable devices, however, offer increased versatility. Here we propose graphene covered subwavelength silicon carbide gratings as electrically tunable optical computation and image processing devices at mid-infrared wavelengths.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Extinction coefficients Guided mode resonance image processing Indium tin oxide Photonic crystals Wave front sensing

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WebGL-based image processing through JavaScript Injection 24

WebGL-based Image Processing through JavaScript Injection

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29th International ACM conference on 3D Web Technology (Web3D)

作者： Meyer, Tim Rodosek, Gabi Dreo Haehn, Daniel Univ Bundeswehr Munchen Neubiberg Germany Univ Massachusetts Boston Boston MA USA

ISBN: (纸本)9798400706899

Can we modify existing web-based computer graphics content through JavaScript injection? We study how to hijack the WebGL context of any external website to perform GPU-accelerated image processing and scene modification. This allows client-side modification of 2D and 3D content without access to the web server. We demonstrate how JavaScript can overload an existing WebGL context and present examples such as color replacement, edge detection, image filtering, and complete visual transformations of external websites, as well as vertex and geometry processing and manipulation. We discuss the potential of such an approach and present open-source software for real-time processing using a bookmarklet implementation.

关键词： WebGL WebGL2 web-based image processing Vertex processing JavaScript Injection

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Parallel Hardware Architecture for Medical image processing Using Xilinx-System-Generator 2

Parallel Hardware Architecture for Medical Image Processing ...

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2nd International conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Baali, Mehdi Bourbia, Nadjla Messaoudi, Kamel Bourennane, El-Bay Mohemed Cherif Mssaadia Univ Elect Engn & Renewlable Energies Lab Leer Souk Ahras Algeria Mentouri Bros Univ Lab Study Elect Mat Med Applicat Constantine 1 Algeria Burgundy Univ ImVia Lab Dijon France

ISBN: (纸本)9798350349740;9798350349757

FPGA is increasingly used in latest realizations of real time implementation for a variety of image processing such as medical imaging. In this paper, we present parallel hardware architecture through a co-simulation using the most efficient tool called Xilinx-System-Generator (XSG) which integrated with MATLAB-Simulink and the synthesis tool used is Xilinx-Vivado. We propose a new strategy for FPGA's memory management based on a design of edge detection algorithm already implemented. The goal is to make an optimization for memory use by minimizing the consumption of slices-registers and slices-Luts. This technique was successfully verified in the images obtained and the resource utilization for the proposed architectures show that the new architectures use fewer resources than the existing architecture.

关键词： FPGA real time implementation image processing XSG MATLAB-Simulink

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Improved LYT-Net image Enhancement Model for Underwater Robotics Vision 13

Improved LYT-Net Image Enhancement Model for Underwater Robo...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Li, Wenjie Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Güttin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

—Although underwater robots can replace humans to explore the ocean which is rich in resources but fraught with unknown risks, there are phenomena such as monotonous colors, complex backgrounds and uneven illumination in the underwater environment, which makes it difficult for underwater robots relying on the optical vision system to obtain clear images and is not conducive to subsequent recognition and grasping tasks. In this paper, relevant preparatory work was carried out for underwater image processing applicable to underwater robots, and the underwater imaging model as well as other underwater image processing models were analyzed. Subsequently, a method combining the underwater physical model and the dark channel image was proposed to improve LYT-Net, enabling it to be applicable to mobile embedded devices such as underwater robots. After training and testing on the real datasets LSUI and UIEB, our model achieved good results on important indicators such as PSNR and SSIM. Meanwhile, it met the theoretical requirements of mobile devices in terms of FLOPs, Params and single-frame processing time, which verified its robustness and real-time processing ability in various underwater environments. © 2025 IEEE.

关键词： Underwater imaging

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Integrating image-based LLMs on edge-devices for Underwater Robotics

Integrating Image-based LLMs on edge-devices for Underwater ...

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conference on real-time image processing and Deep Learning

作者： Sundaravadivel, Prabha Roselyn, Preetha J. Narayanaswamy, Vedachalam Jeyaraj, Vincent I. Ramesh, Aishree Khanal, Aaditya Univ Texas Tyler Dept Elect & Comp Engn Tyler TX 75701 USA SRMIST Dept Elect & Elect Engn Chennai TN India Natl Inst Ocean Technol Chennai Tamil Nadu India Saveetha Engn Coll Dept AI & Data Sci Chennai Tamil Nadu India Univ Texas Tyler Dept Chem Engn Tyler TX 75701 USA

ISBN: (纸本)9781510673878;9781510673861

image-based Large Language Models (LLMs) are AI models that can understand the captured images and generate textual content based on the analysis of images or visual data. Incorporating the LLMs for assessing water quality, pressure, and environmental conditions can help analyze historical data and predict potential risks and threats in underwater environments. This can improve the intervention of autonomous underwater vehicles (AUV) and remotely operated vehicles (ROV) during emergencies where the visual data must be interpreted to make informed decisions. While LLMs are primarily associated with processing and generating text, they can be integrated with images through a process known as multimodal learning, where text and images are combined for tasks that involve both modalities. Implementing such frameworks is challenging when deployed in low-power microcontrollers primarily used in monitoring systems. This research proposes evaluating multimodal tokens to enable edge computing in bio-inspired robots to monitor the underwater environment. This can help break down large real-time videos into tokens of text-based instructions associated with the description of images. The mini-robots will transmit the collected "tokens" to the nearest AUV or ROV, where the image-based LLM will be deployed. We propose to evaluate this image-based LLM in our NVIDIA Jetson Nano-based AUV. In the proposed architecture, the mini-robots can move along the length of the water column to capture images of the underwater environment. Our proposed model is evaluated to generate texts for boat and fish images. This proposed framework with integrated image-based tokens can significantly reduce the response time and data traffic in underwater real-time monitoring systems.

关键词： times Roman image area acronyms references

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Implementation of the image super-resolution DWT based algorithm on Raspberry Pi platform for real-time applications

Implementation of the image super-resolution DWT based algor...

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conference on real-time processing of image, Depth, and Video Information

作者： Osorno-Ortiz, Raul J. Ponomaryov, Volodymyr, I Reyes-Reyes, Rogelio Cruz-Ramos, Clara Garcia-Salgado, Beatriz P. Sadovnychiy, Sergiy Inst Politecn Nacl ESIME Culhuacan Ciudad De Mexico Mexico Inst Mexicano Petr Ciudad De Mexico Mexico

ISBN: (纸本)9781510673199;9781510673182

Super resolution (SR) is a technique designed for increasing the spatial resolution in an image from a low resolution (LR) to high resolution (HR) size. SR technology has had a considerable demand in a wide variety of applications to recover HR images, such as medicine, engineering, computer vision, pattern recognition and video production, etc. In contrast to interpolation-based algorithms that often introduce distortions or irregular borders, this study proposes an implementation that can preserve the edges and fine details of an original image through the computation of the wavelet decomposition. Different Discrete Wavelet Transform (DWT) families such as: Daubechies, Symlet, and Coiflet were evaluated. The proposed system was implemented on a Raspberry Pi 4 model B, an embedded device, to get around the PC's mobility limitations, making it possible to create an in-expensive and energy- efficient SR system, reducing their complexity in realtime applications. To investigate the visual performance, SR images have been analysed in subjective matter via human perception view, guaranteeing good perception for the images of different nature from three different datasets such as FullHD (DIV2K), medical (Raabin WBC), and remote sensing (Sentinel- 1). The experimental results of designed implementations appear to demonstrate good performance in commonly used objective criteria: execution time, SSIM, and PSNR (0.742 sec., 0.9164, and 38.72 dB), respectively for images with a super resolution size of 1356 x 2040 pixels.

关键词： Super resolution Discrete Wavelet Transform (DWT) Parallel programing Raspberry Pi real-time processing

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