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

共 12948 条记录，以下是461-470 订阅

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Acceleration of image processing algorithms based on a Single Board Computer and FPGA co-design 11

Acceleration of image processing algorithms based on a Singl...

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11th International conference on Modern Circuits and systems Technologies (MOCAST)

作者： Kokotis, Petros Vourvoulakis, John Int Hellen Univ Dept Comp Informat & Telecommun Engn Serres Greece

ISBN: (数字)9781665467179

ISBN: (纸本)9781665467179

During recent years, various hardware platforms were developed, each one suitable for use in different kind of applications. Platforms based on FPGAs, DSPs, GPUs, Single Board Computers, microcontrollers extend processing capabilities and functionality in comparison with traditional personal computers based on a single CPU. Furthermore, co-design combines advantages from different types of processing units, rendering such architectures more attractive to researchers. In this paper, we achieve acceleration of image processing algorithms using a hardware platform based on a Raspberry Pi Single Board Computer and a custom designed FPGA HAT (Hardware Attached on Top) for RPi. The FPGA HAT consists of a Cyclone 10LP device The FPGA undertakes a computationally demanding load such as robotic vision algorithms exploiting parallelism, while the RPi can apply higher level operations such as running ROS (Robot Operating System). In order to overcome bottleneck in exchanging data between RPi and FPGA, a 16-bit parallel customized protocol was developed from scratch. The achieved transfer rate was about 50 Mbytes/sec when multi threaded software was implemented for the RPi. An image edge detector was implemented in order to verify the system performance. When only the RPi was used the processing rate was 48fps for images with resolution 512x512 pixels. RPi and FPGA co-design achieved processing rate 170fps for the same resolution images, which means an acceleration of about 350%. The proposed system was also evaluated in terms of power consumption.

关键词： fpga accelerator raspberry pi

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Performance Analysis for Trichoscopy and Trichogram Using Deep Learning and image processing—A Survey 8th

Performance Analysis for Trichoscopy and Trichogram Using De...

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8th International conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Jain, Divyanshu Masurkar, Purva Kakde, Shreyash Khot, Mohammed Siddique Waghmare, Aditya Gohil, Unnati Pawar, Rahul Patel, Dhananjay Patil, Pradeep Vidyalankar Institute of Technology Wadala Mumbai India Srihtvak Skin Hair Laser STD Leprosy Pharma and Wellness Center Baner Pune India

ISBN: (纸本)9789819994854

Hair-related diseases are pervasive and can significantly impact individuals’ confidence and emotional well-being. Accurate diagnosis of these conditions poses challenges even for experienced professionals. However, the integration of technology, particularly deep learning and artificial intelligence (AI), has been showing bright results in the field of Trichology. This paper presents a comprehensive review of the techniques and technologies developed in Trichology. We begin by delineating common hair diseases, trichoscopy image acquisition methods, and available datasets. We also examine existing frameworks and tools that facilitate the creation of trichoscopy-based algorithms, along with frequently used evaluation metrics. The techniques studied in this paper involve hair loss detection using Mask R-CNN, Kaggle network, DEX-IMDB-WIKI and DEX-ChaLearn networks, deep learning-based scalp image analysis using limited data through ResNet, ResNeXt, DenseNet, and XceptionNet, image processing using grid line selection method, machine learning using SVN and KNN, and hair and scalp disease recognition using deep learning and image processing. The different algorithms used in all the mentioned techniques are analyzed, giving us a brief knowledge of how Trichology is applied with the help of technology to benefit professionals in accurately diagnosing different hair loss parameters and diseases. This review offers an overview of recent advancements in hair disease diagnosis using trichoscopy and Trichogram, emphasizing opportunities for further enhancement in this rapidly evolving field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Enhancing Liver Cancer Detection with AI-Powered image processing 4

Enhancing Liver Cancer Detection with AI-Powered Image Proce...

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4th International conference on Sustainable Expert systems, ICSES 2024

作者： Suresh Kumar, A. Rajathi, S. Vargheese, Vineetha Sudha, M. Anju, A. Lokeshkiran, P. Department of Computer Science and Engineering Rathinam Technical Campus Tamil Nadu Coimbatore India Karpagam Institute of Technology Department of Information Technology Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Rathinam Technical Campus Tamil Nadu Coimbatore India

ISBN: (纸本)9798331540364

This project aims to identify liver tumors using deep learning techniques. The core of our approach is a sophisticated neural network trained on real CT scans of patients with liver tumors. By leveraging advanced signal-processing algorithms and deep learning models, our AI method accurately distinguishes between tumors and healthy liver tissue. Accurate identification of liver tumors on CT images is crucial for planning effective radiation therapy. CT and MRI scans provide detailed images of the internal structure of the body by analyzing tissues. Our primary objective is to develop an AI-driven neural network that can reliably identify liver tumors in these images. Given the variability in liver location among individuals, our approach employs specific techniques to ensure precision. Using U-net technology, our goal is to enhance the speed and accuracy of tumor identification, thereby improving patient access to timely and accurate diagnoses. This project underscores the significant potential of deep learning applications in health care. © 2024 IEEE.

关键词： Computerized tomography

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Organelles segmentation in scanning electron microscopy images based on image processing 24

Organelles segmentation in scanning electron microscopy imag...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International conference on Education Digitalization and Computer Science, EDCS 2024

作者： Liu, Qiuhan Zhang, Qiang Zheng, Zhaohui Changjiang Institute of Technology Wuhan430212 China Wuhan Railway Vocational College of Technology Wuhan430200 China Wuhan Institute of Technology Wuhan430079 China

ISBN: (纸本)9798400710360

Organelles are important structures and functional systems within cells, which are of great significance for understanding life phenomena and disease mechanisms. Therefore, the detection and segmentation of organelles in scanning electron microscopy images is a common requirement in biological image analysis. However, due to differences in factors such as cell type, cell morphology, and cell density, the analysis of specific data often requires highly customized algorithms and inefficient and cumbersome manual operations. Therefore, this article proposes a new method for organelle segmentation in scanning electron microscopy images. This method first reduces the problem of noise in the image through Gaussian filtering technology, and then uses the Canny operator to perform edge detection on the image to preliminarily obtain the edges of organelles. Then, morphological operations such as dilation and corrosion, as well as connecting contours, are used to obtain more accurate organelle shapes and edge features. Finally, based on the outer rectangle features of the contours, the rectangles are deleted and merged, and the processed rectangles are used to label the recognized organelles. The method proposed in this article has certain innovation and practicality, which can effectively achieve automated segmentation and localization of organelles in scanning electron microscopy images, providing certain reference and reference value for research and application in related fields. © 2024 Copyright held by the owner/author(s).

关键词： image segmentation

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3rd International conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2023

3rd International Conference on Information Technologies and...

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3rd International conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2023

ISBN: (纸本)9783031603174

The proceedings contain 18 papers. The special focus in this conference is on Information Technologies and Intelligent Decision Making systems. The topics include: Comparative Analysis of Traditional Machine Learning Approaches for Time Series Clustering Under Colored Noise;on the Open Transport Data Analysis Platform;investigation of the Characteristics of a Frequency Diversity Array Antenna;comparative Analysis of Fuzzy Controllers in a Truck Cruise Control System;implementation of a Blockchain-Based Software Tool to Verify the Authenticity of Paper Documents;development of Methods and algorithms for Dimension Reduction of Space Description for Pattern Recognition Problem;service for Checking Students’ Written Work Using a Neural Network;implementing a Jenkins Plugin to Visualize Continuous Integration Pipelines;elimination of Optical Distortions Arising from In Vivo Investigation of the Mouse Brain;quantum Fourier Transform in image processing;choosing an Information Protection Mechanism Based on the Discrete Programming Method;application of Machine Learning Methods for Annotating Boundaries of Meshes of Perineuronal Nets;diagnostics of Animals Diseases Based on the Principles of Neutrosophic Sets and Sugeno Fuzzy Inference;the Technique of processing Non-Gaussian Data Based on Artificial Intelligence;development of Automation and Control System of Waste Gas Production Process Based on Information Technology;machine Learning and Data Mining.

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Parameters Optimization of Dynamic CT Pulmonary Perfusion Imaging Analysis Technology Based on Artificial Intelligence

Parameters Optimization of Dynamic CT Pulmonary Perfusion Im...

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2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Li, Chuangui Liu, Xiaocheng Zhang, Xinhui Zhou, Guijuan Zhu, Yuexiang First Affiliated Hospital of Hebei North University Department of Radiology Zhangjiakou China University of Chinese Medicine Dongzhimen Hospital of Beijing Department of Radiology Beijing China

ISBN: (纸本)9798350315455

In recent years, with the rapid development and popularization of artificial intelligence technology, CT has been more and more widely used in the field of clinical medicine as a non-volume micro body diagnostic method. In this paper, the artificial intelligence software is used to build a dynamic CTR lung scanning image based on the wavelet neural network model, and the genetic algorithm is used to improve the traditional visual lung image quality analysis model to improve the diagnostic efficiency. At the same time, the matrix representation weight value of the relationship between different types of disease maps is obtained by changing the threshold value to calculate the acoustic difference between the same CT tissue to determine its membership function and carry out joint optimization, After that, the effect of this technology was tested, and the results showed that the artificial intelligence based dynamic CT pulmonary perfusion imaging technology had faster generation time and higher quality than traditional algorithms in image processing, so as to achieve accurate mining of results and reduce the number of misdiagnosis. As a result, this method achieved 15 s for imaging test which comparatively lesser than Traditional imaging algorithms. © 2023 IEEE.

关键词： artificial intelligence dynamic CT lung image lung perfusion imaging parameter optimization

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Review on the image Encryption with Hyper-Chaotic systems 5th

Review on the Image Encryption with Hyper-Chaotic Systems

引用

5th International conference on Soft Computing and Signal processing, ICSCSP 2022

作者： Pathak, Arghya Pal, Subhashish Karmakar, Jayashree Mondal, Hrishikesh Mandal, Mrinal Kanti Department of Physics National Institute of Technology Durgapur713209 India Department of Physics Dr. B. C. Roy Engineering College Durgapur713206 India MUSE Lab Indian Institute of Technology Gandhinagar382355 India Department of Physics Durgapur Government College Durgapur713214 India

ISBN: (纸本)9789811986680

image encryption using a chaotic system is a widely used technique. But, cryptanalysis of most chaotic encryption algorithms pointed out the disadvantages or drawbacks of the chaotic systems. As a remedy, a new and more efficient chaotic system with higher dimension is used recently and named as a hyper-chaotic system. The conditions of an efficient encryption algorithm can be fulfilled effectively by a hyper-chaotic system. Recently, several hyper-chaotic systems are proposed by different researchers, and some of them are used in image encryption techniques. But, some recent cryptanalysis on hyper-chaotic image encryption shows the drawback of these system. This work will discuss different hyper-chaotic systems on the basis of their efficiency to use in image encryption. This discussion will also include the parameters based on which one can choose an efficient hyper-chaotic system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chaotic systems

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A Generative Network with Dual-Domain Discriminators for Low-Dose Stationary Sources CT Imaging 6

A Generative Network with Dual-Domain Discriminators for Low...

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6th International conference on Digital Medicine and image processing, DMIP 2023

作者： Bai, Xiao Cheng, Ying Chen, Linjie Yang, Shuo Wang, Huamin Wang, Zhe Wu, Jiayi Cao, Guohua School of Biomedical Engineering ShanghaiTech University Shanghai China

ISBN: (纸本)9798400709425

Recent development of clinical Computed tomography (CT) technologies has led to research for novel CT systems that allow safer and faster imaging, such as low-dose cardiac CT imaging via stationary CT. However, the complex data acquisition schemes in stationary CT often cause severe artifacts and noise in the resulted images;this calls for the development of a new kind of image reconstruction algorithms. Recent advancements in deep learning have shown remarkable progress in medical image reconstruction, processing, and analysis. In this paper, we propose a generative network with dual-domain discriminators for low-dose CT reconstruction in a stationary CT system. The image-domain discriminator optimizes the generation network by comparing the generated CT images with the reference images, while the sinogram-domain discriminator preserves the structure of the sinograms and suppresses the noise. The network incorporates uncertainty to automatically adjust the weights of a multi-term loss function, eliminating the need for the manual tuning of hyperparameters in the loss function. The results from our numerical experiments demonstrate the effectiveness of our proposed reconstruction algorithm for low-dose imaging in stationary CT. © 2023 ACM.

关键词： image reconstruction

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Point spread function modeling for coherent plane-wave compounding ultrasound imaging 15

Point spread function modeling for coherent plane-wave compo...

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15th International conference on Information Optics and Photonics, CIOP 2024

作者： Zhou, Bowen Liu, Songde Tian, Chao School of Engineering Science University of Science and Technology of China Anhui Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Spatial resolution is a critical measure of the imaging performance of ultrasound imaging systems and is determined by various system factors. So far, no comprehensive studies have been reported to elucidate the impact of system factors on spatial resolution, which is generally defined as the full width at half maximum (FWHM) of the point spread function (PSF). This work aims to model the PSF of ultrasound imaging systems based on a linear transducer array and a coherent plane-wave compounding (CPWC) method and study how pulse-echo sequences, transducer parameters, and beamforming algorithms impact PSF. The Verasonics simulator and UltraSound ToolBox are used in numerical simulations for ultrasound signal generation and image reconstruction, respectively. Experiments are conducted to verify simulation results. Numerical and experimental results show that the value of axial resolution is negatively correlated with the center frequency of the ultrasonic transducer;the value of lateral resolution is positively correlated with the number of transmitted plane waves of the CPWC method and the element width of the ultrasonic transducer and is negatively correlated with the maximum steering angle of the CPWC method and the aperture size and center frequency of the ultrasonic transducer. Different beamforming algorithms show varying effects on spatial resolution and image quality. This work is expected to help predict and interpret the quality of ultrasound images produced by practical ultrasound imaging systems and is beneficial for the design and development of ultrasonic transducers, pulse-echo sequences, and beamforming algorithms for enhanced spatial resolution. © 2024 SPIE.

关键词： Optical transfer function

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Research on Polarization Imaging Target Detection Method Based on Multi-View 3

Research on Polarization Imaging Target Detection Method Bas...

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3rd International conference on Computer Graphics, image, and Virtualization, ICCGIV 2023

作者： Cui, Hao Wang, Xin Pu, Xiankun Shi, Lei Zhou, Zhiqiang Gao, Jun School of Computer and Information Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China

ISBN: (纸本)9781510671720

Traditional multi-view target detection can only obtain the light intensity information of the target, and its detection ability is very limited in complex environments;Traditional polarization target detection mainly relies on a single camera to obtain the polarization information of the target, making it difficult to directly obtain all the polarization information of the target in some complex backgrounds. In order to overcome the limitations of the two detection methods mentioned above, this paper studies a multi angle polarization target detection method based on a polarization array system. This method mainly uses traditional multi perspective image processing algorithms to process polarized images and achieve polarization image stitching. To address the issue of the inability to directly concatenate polarized images in low light environments, this paper proposes the introduction of image enhancement algorithms to achieve the concatenation and target detection of multi angle polarized images in low light environments. Based on the characteristics of polarization feature images, a fusion method based on polarization features was studied to improve the contrast between the target and the background. © 2023 SPIE.

关键词： image enhancement

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