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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16444 条记录，以下是1601-1610 订阅

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Oscillatory Neural Network as Hetero-Associative Memory for image Edge Detection 2022

Oscillatory Neural Network as Hetero-Associative Memory for ...

引用

Annual Neuro-Inspired Computational Elements conference (NICE)

作者： Abernot, Madeleine Gil, Thierry Todri-Sanial, Aida Univ Montpellier CNRS LIRMM Montpellier France

ISBN: (纸本)9781450395595

The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical image processing methods performed at the edge, such as feature extraction or edge detection, use convolutional filters that are energy, computation, and memory hungry algorithms. But edge devices and cameras have scarce computational resources, bandwidth, and power and are limited due to privacy constraints to send data over to the cloud. Thus, there is a need to process image data at the edge. Over the years, this need has incited a lot of interest in implementing neuromorphic computing at the edge. Neuromorphic systems aim to emulate the biological neural functions to achieve energy-efficient computing. Recently, Oscillatory Neural Networks (ONNs) present a novel brain-inspired computing approach by emulating brain oscillations to perform auto-associative memory types of applications. To speed up image edge detection and reduce its power consumption, we perform an in-depth investigation with ONNs. We propose a novel image processing method by using ONNs as a Heterogeneous Associative Memory (HAM) for image edge detection. We simulate our ONN-HAM solution using first, a Matlab emulator, and then a fully digital ONN design. We show results on gray scale square evaluation maps, also on black and white and gray scale 28x28 MNIST images and finally on black and white 512x512 standard test images. We compare our solution with standard edge detection filters such as Sobel and Canny. Finally, using the fully digital design simulation results, we report on timing and resource characteristics, and evaluate its feasibility for real-time image processing applications. Our digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz system frequency) respecting real-time camera constraints. This work is the first to explore ONNs as hetero-associative memory for image processing applications.

关键词： Edge detection Hetero-Associative Memory Neuromorphic Computing Oscillatory Neural Networks

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DiffWA: Diffusion Models for Watermark Attack

DiffWA: Diffusion Models for Watermark Attack

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Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： xinyu Li Xi’an Jiaotong University Xi’an China

With the rapid development of deep neural networks (DNNs), many robust blind watermarking algorithms and frameworks have been proposed and achieved good results. At present, the watermark attack algorithm can not compete with the watermark addition algorithm. And many watermark attack algorithms only care about interfering with the normal extraction of the watermark, and the watermark attack will cause great visual loss to the image. To this end, we propose DiffWA, a conditional diffusion model with distance guidance for watermark attack, which can restore the image while removing the embedded watermark. The core of our method is training an image-to-image conditional diffusion model on unwatermarked images and guiding the conditional model using a distance guidance when sampling so that the model will generate unwatermarked images which is similar to original images. We conducted experiments on CIFAR-10 using our proposed models. The results shows that the model can remove the watermark with good effect and make the bit error rate of watermark extraction higher than 0.4. At the same time, the attacked image will maintain good visual effect with PSNR more than 31 and SSIM more than 0.97 compared with the original image.

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Conditional Diffusion Process for Inverse Halftoning 36

Conditional Diffusion Process for Inverse Halftoning

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Jiang, Hao Mu, Yadong Peking Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781713871088

Inverse halftoning is a technique used to recover realistic images from ancient prints (e.g., photographs, newspapers, books). The rise of deep learning has led to the gradual incorporation of neural network designs into inverse halftoning methods. Most of existing inverse halftoning approaches adopt the U-net architecture, which uses an encoder to encode halftone prints, followed by a decoder for image reconstruction. However, the mainstream supervised learning paradigm with element-wise regression commonly adopted in U-net based methods has poor generalization ability in practical applications. Specifically, when there is a large gap between the dithering patterns of the training and testing halftones, the reconstructed continuous-tone images have obvious artifacts. This is an important issue in practical applications, since the algorithms for generating halftones are ever-evolving. Even for the same algorithm, different parameter choices will result in different halftone dithering patterns. In this paper, we propose the first generative halftoning method in the literature, which regards the black pixels in halftones as physically moving particles, and makes the randomly distributed particles move under some certain guidance through reverse diffusion process, so as to obtain desired halftone patterns. In particular, we propose a Conditional Diffusion model for image Halftoning (CDH), which consists of a halftone dithering process and an inverse halftoning process. By changing the initial state of the diffusion model, our method can generate visually plausible halftones with different dithering patterns under the condition of image gray level and Laplacian prior. To avoid introducing redundant patterns and undesired artifacts, we propose a meta-halftone guided network to incorporate blue noise guidance in the diffusion process. In this way, halftone images subject to more diverse distributions are fed into the inverse halftoning model, which helps the model to lear

关键词： image reconstruction

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Overview of image Classification Methods Based on Deep Learning

Overview of Image Classification Methods Based on Deep Learn...

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Computers, Information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Jinke Huang Tiancai Yang Zhenze Jia Equipment Management and UAV Engineering College Air Force Engineering University Xi’an China Chinese Unit 93525 of the People’s Liberation Army Shigatse China Air Force Communication NCO School Dalian China

image classification is a method of classifying different categories of objects based on the different characteristics of objects in an image. Most of the traditional image classification algorithms use shallow structures, which have obvious deficiencies in performance and generalization ability. The image classification algorithm based on convolutional neural network has good performance indicators and can solve the image classification problem well.

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Pioneering Early Parkinsons Disease Detection with Deep Learning and image processing Innovations 10

Pioneering Early Parkinsons Disease Detection with Deep Lear...

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10th International conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Srinivasarao, Narisetty Khan, Mohammad Basith Anwar Haneesha, Pullipati Meghana, Palaparthi Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350384369

Parkinson's disease, a neurological disorder which affects the nervous system, manifests as unintentional and uncontrollable movements in the body. With over 6 million individuals globally affected, early detection of PD proves challenging, given its varied symptoms. The manifestations of the disease can differ among individuals, with symptoms typically initiating as a tremor in one hand and progressively impacting the entire body over time. Typically, symptoms begin with a hand tremor and progress throughout the body. Currently, conventional clinical approaches do not possess the capability to detect Parkinson's disease during its initial phases. Diagnosis relies on medical history, brain MRI images, and manual symptom observation, a process that is time-consuming and often insufficient for early detection. The potential of deep learning for early PD detection is explored using datasets comprising spiral drawings from both normal and PD-affected individuals. CNN and ResNet50 algorithms are applied to spiral drawings, enhancing the model's ability to capture intricate patterns. The training process utilizes 60% of the dataset, with the remaining 40% reserved for testing. In the case of spiral drawings, the ResNet50 algorithm proves instrumental. It transforms spiral drawings into pixels, facilitating classification. During testing, the pixels of current drawings are compared with previously trained CNN and ResNet50 models to identify the disease. The amalgamation of results from ResNet50-enhances spiral drawing datasets significantly and improves disease detection accuracy more than CNN. Individual's data can be given as input into the system for efficient disease detection. © 2024 IEEE.

关键词： Ability testing

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Shack-Hartmann wavefront reconstruction speed up with embedded GPU 13

Shack-Hartmann wavefront reconstruction speed up with embedd...

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13th International conference on Information Optics and Photonics, CIOP 2022

作者： Fan, Yao Duan, Yaxuan Kou, Jingwei Zhang, Weigang Liu, Qing Da, Zhengshang Advanced Optical Instrument Laboratory Xi'an Institute of Optics and Fine Mechanics Chinese Academy of Sciences Xi’an710119 China Xi'an Key Laboratory of High Power Laser Measurement Technology and Instrument Xi’an710119 China

ISBN: (纸本)9781510660632

Shack-Hartmann sensor is widely used in adaptive optics systems, and laser beam quality measurements. The traditional method separates measures and calculations, and the wavefront reconstruction algorithm is slow to implement on the host computer. In this paper, the embedded GPU is introduced to Shack-Hartmann sensors' wavefront phase reconstruction. A parallel calculation method is proposed to speed up the wavefront phase reconstruction process. The experiment result shows the algorithm speed improves 50×with the image size of 2592×2048 pixels. © 2022 SPIE.

关键词： Graphics processing unit

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Integration of image processing and Machine Learning Technique applied in Agriculture Sector Using Python

Integration of Image Processing and Machine Learning Techniq...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International conference on

作者： Bishnu Deo Adarsh Mishra Adarsh Singh Amit Verma Vivak Saxena Department of Electronics and Communication Galgotia’s College of Engineering and Technology Greater Noida India CWPRS Pune

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

image processing has emerged as a crucial technology in agriculture, facilitating tasks such as crop monitoring, disease detection, and yield estimation. Python, with its extensive libraries and tools, has become a popular choice for implementing image processing algorithms in this field. This paper explores the applications of image processing in agriculture, emphasizing the capabilities of Python. It reviews key Python libraries, techniques, and real-world applications, demonstrating how Python enhances agricultural image processing.

关键词： Photovoltaic systems Technological innovation Reviews image processing Crops Machine learning Agriculture Libraries Telecommunication computing Yield estimation

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A Novel Extraction Algorithm for Pixel Recognition in AI images with Feature Point

A Novel Extraction Algorithm for Pixel Recognition in AI Ima...

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Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： Jiajia Fan Bo Yuan Zhuang Jin College of Computer Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In the era of big data, image processing still faces significant bottlenecks compared to other fields of computer science. In this paper we studied the feature point extraction algorithm based on gray points and the stereo matching technique, both of them play important roles in image processing. The feature point extraction algorithm based on gray points includes Moravec algorithm and Susan Algorithm. The results indicate that it can discover that simplified inverse filter tracking (SIFT) algorithm is the best algorithm among all the feature point extraction algorithms, which has have strong stability, fast calculating speed and high accuracy. In stereo image matching technique, we found that dense matching algorithm plays an important role in target detection and tracking in the three-dimensional reconstruction operations. By comparing the matching process between traditional nearest neighbor matching and this paper's matching algorithm, we concluded that the matching relationship in this algorithm is entirely correct.

关键词： Matched filters Target tracking image recognition image matching Object detection Feature extraction Stability analysis Classification algorithms Standards image reconstruction

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Hardware Accelerated Thermal image processing for the Detection of Induction Motor Faults Based on Statistical Features 49

Hardware Accelerated Thermal Image Processing for the Detect...

引用

49th Annual conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Alvarado-Hernandez, Alvaro Ivan Osornio-Rios, Roque Alfredo Zamudio-Ramirez, Israel Antonino-Daviu, Jose Alfonso Universidad Autonoma de Queretaro Facultad de Ingenieria Queretaro Mexico Instituto Tecnológico de la Energía Universitat Politecnica de Valencia Valencia Spain

ISBN: (纸本)9798350331820

Condition monitoring and predictive maintenance of induction motors have great relevance in industrial applications. Nowadays, there are different techniques to analyze electronic signals from different types of sensors. At the same time, the application of FPGA-based hardware acceleration has gained traction in recent years due to the increasing demand for time and energy optimization. However, the application of hardware-accelerated algorithms in condition monitoring applications has yet to be explored despite its potential to improve online fault detection systems. This paper presents the implementation of an FPGA-based hardware-accelerated thermal image processing pipeline on the PYNQ Z2 board based on two image statistical features (mean and standard deviation) to detect five different induction motor mechanical fault conditions: misalignment, unbalanced load, bearing defect on the outer race, two broken rotor bars, and a healthy case that was used as reference. The hardware implementation of the thermal image statistical feature computation made it possible to reduce the computational load and the computation time on the development board. © 2023 IEEE.

关键词： Induction motors

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3rd EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

3rd EAI International Conference on Cognitive Computing and ...

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3rd EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

ISBN: (纸本)9783031289743

The proceedings contain 22 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical systems. The topics include: Performance Evaluation of Fast DCP Algorithm for Single image Dehazing;Non Destructive Analysis of Crack Using image processing, Ultrasonic and IRT: A Critical Review and Analysis;ioT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time;Voice Based Objects Detection for Visually Challenged Using Active RFID Technology;depth Estimation and Navigation Route Planning for Mobile Robots Based on Stereo Camera;Water Level Forecasting in Reservoirs Using Time Series Analysis – Auto ARIMA Model;water Quality Monitoring Using Remote Control Boat;study of Smart City Compatible Monolithic Quantum Well Photodetector;frequency Reconfigurable Antenna for 5G Applications at n77 and n78 Bands;solar Energy Prediction using Machine Learning with Support Vector Regression Algorithm;analysis of Acoustic Channel Model Characteristics in Deep-Sea Water;A Comprehensive Review on Channel Estimation Methods for Millimeter Wave MIMO systems;comparison of Acoustic Channel Characteristics in Shallow and Deep-Sea Water;price Estimation of Used Cars Using Machine Learning algorithms;Machine Learning Framework for Identification of Abnormal EEG Signal;alzheimer’s Disease Detection Using Ensemble of Classifiers;publishing Data Objects in Data Aware Networking;youTube Comment Analysis Using Lexicon Based Techniques;Information Theoretic Heuristics to Find the Minimal SOP Expression Considering Don’t Care Using Binary Decision Diagrams;single image Dehazing Through Feed Forward Artificial Neural Network.

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