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

共 16443 条记录，以下是881-890 订阅

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Preserving Legacies, Pioneering Frontiers: Multi-Sensor image Fusion, from Medieval Manuscripts to UAS-based Sensing 30

Preserving Legacies, Pioneering Frontiers: Multi-Sensor Imag...

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conference on algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

作者： Hassanzadeh, Amirhossein Macalintal, Jose N. G. M. Messinger, David Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781510673816;9781510673809

Data fusion, which involves integrating data from multiple sources, is increasingly valuable across various fields due to its ability to enhance information quality, accuracy, and reliability. This process enables a more comprehensive understanding of complex phenomena by merging diverse datasets, providing insights that are otherwise unattainable. In the realm of remote sensing, where precise data acquisition is critical, fusion techniques have become indispensable, benefiting applications such as object detection, classification, and change detection. While much emphasis has been placed on spatial sharpening techniques in published studies, there remains a notable gap in establishing robust workflows for both lab-based and UAS-based remote sensing data fusion, particularly in the near-infrared (VNIR) and short-wave infrared (SWIR) regions. This study aims to investigate VNIR-SWIR fusion using data sourced from a medieval manuscript in a controlled laboratory environment and from UAS-based sensors in a real-world setting, addressing differences in system parameters and processing workflows. Despite challenges such as image registration issues, our analysis has yielded promising results, underscoring the importance of ongoing refinement in fusion methodologies to ensure comprehensive data interpretation and analysis across diverse datasets and environments.

关键词： data fusion visible to near infrared (VNIR) shortwave infrared (SWIR) Unamnned Aerial systems (UAS)

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Improved Object Detection in Forward-Looking Sonar images with Attention-YOLOv8 and Denoising algorithms 6

Improved Object Detection in Forward-Looking Sonar Images wi...

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6th International conference on Information Communication and Signal processing, ICICSP 2023

作者： Wang, Zitao Cao, Tianyu Shi, Bo Guo, Wenbo Ge, Qiqi Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9798350339994

The utilization of forward-looking sonar (FLS) has become increasingly significant for detecting underwater objects. However, despite its widespread use, target recognition tasks for underwater unmanned vehicles (UUVs) are still predominantly carried out through postprocessing techniques. This can be attributed to the limitations of computing devices deployed on UUVs, which have restricted computational capabilities due to power constraints. Furthermore, conventional object detection approaches designed for natural images are inadequate when applied to sonar images with high levels of noise. To address these challenges and enable UUVs to autonomously detect underwater objects, we propose an innovative solution that involves the integration of an improved version of YOLOv8 with attention mechanism, allowing the model to obtain more feature information while reducing the number of parameters required. Additionally, we developed a comprehensive preprocessing module consisting of removal, contrast enhancement, and noise filtering techniques. Our experimental results demonstrate that the proposed method achieves high accuracy, robustness, and can be efficiently implemented on hardware processors. Our code is available on https:/***/wangzitao7777/underwater-detection. © 2023 IEEE.

关键词： image processing lightweight network object detection sonar image

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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...

引用

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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Effective Integration of Innovative Computer Vision Tools into Electronics Manufacturing Process 7

Effective Integration of Innovative Computer Vision Tools in...

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7th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Adamova, Arina A. Vlasov, Andrey I. Medvedeva, Elena A. Bauman Moscow State Technical University Department of Design and Production Technology of Electronic Equipment Moscow Russian Federation

ISBN: (纸本)9798331531836

The success and accuracy of analysis and processing of raster images of electronic assemblies entering the machine vision algorithms and models within the electronics manufacturing process depend on parameters of these images. This necessitates the development of recording modules for computer vision systems using multifunctional cameras that allow not only capturing but also the maximum possible pre-processing of images. The paper analyzes the processing of raster images using machine vision methods and formulates the key requirements of computer vision algorithms and models for input images. A structural diagram of a computer vision system is presented. In accordance with this diagram, a recording module based on a 2-megapixel camera has been developed. Physical modeling of the module operation has been carried out using the example of processing an image of an electronic assembly. The proposed software and hardware version of the recording module solves the problem of effective implementation of innovative machine vision methods in electronics manufacturing process by pre-processing images on the camera side and their visualization in order to control the success of the system reconfiguration. © 2025 IEEE.

关键词： camera computer vision electronics manufacturing raster image preprocessing recording module

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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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AGIAA-2K: A Fine-grained Dataset for Aesthetic and Alignment Evaluation of AI-Generated images

AGIAA-2K: A Fine-grained Dataset for Aesthetic and Alignment...

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

作者： Bo Hu Nanxiang Li Lihuo He Wen Lu Leida Li xinbo Gao Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China School of Electronic Engineering Xidian University Xi’an China School of Artificial Intelligence Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the advancement of AI-generated content technologies, AI-generated images (AGIs) have become increasingly influential in artistic creation and visual communication. However, the aesthetic quality of AGIs varies significantly due to technical limitations and the influence of user input, underscoring the urgent need for systematic aesthetic evaluation of AGIs. In addition, it is difficult to ensure the consistency of text-to-image, which compresses the application space of AGIs. To address these issues, a fine-grained dataset for Aesthetic and Alignment evaluation of AGIs (AGIAA-2K) is presented. This dataset contains 2,064 images generated using 172 well-designed prompts across six different AGI models, with each image annotated based on the subjective experiment. Then, the rationality of the dataset is verified by data analysis. Finally, the performances of the existing algorithms are evaluated in terms of image aesthetic assessment and text-to-image alignment of AGIs. The results demonstrate that these algorithms cannot effectively evaluate these two aspects. The AGIAA-2K is available at https://***/BoHu90/AGIAA-2K.

关键词： Measurement Technological innovation Systematics image coding Data analysis Visual communication Text to image Signal processing algorithms Signal processing Speech processing

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A novel global method for edge extraction under Poisson noise: Game theory 7

A novel global method for edge extraction under Poisson nois...

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7th International conference on Communication, image and Signal processing, CCISP 2022

作者： Wei, Wenyang Feng, xiangchu Xidian University School of Mathematics and Statistics Xi'an China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

Edge extraction is a central problem in image processing and it is a necessary step for computer vision tasks. In this paper, a novel global method P-GSG for edge extraction of image under Poisson noise is given, which is based on sparse representation. Furthermore, a game model which combines P-GSG with total variation denoising is proposed to get better results. As two players, P-GSG model can apply with iteration latent clean image to robustly get the gradient under the Poisson noise, on the other hand, TV denoising can get an edge-preserving latent clean image, which overcomes the shortcoming of over-smoothing. By cooperation and competition between two tasks, we can attain a satisfactory solution for this game model-Nash equilibrium. The algorithms of P-GSG and TV denoising are given. Based on above algorithms, it is obvious that alternate iteration method is easily used to solve this game model. The effectiveness of these two models is shown by numerical experiments. © 2022 IEEE.

关键词： Game theory

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AutoShape: Automatic Design of Click-Through Rate Prediction Models Using Shapley Value 20th

AutoShape: Automatic Design of Click-Through Rate Prediction...

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20th Pacific Rim International conference on Artificial Intelligence, PRICAI 2023

作者： Fang, Yunfei Mu, Caihong Liu, Yi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Collaborative Innovation Center of Quantum Information of Shaanxi Province School of Artificial Intelligence Xidian University Xi’an710071 China School of Electronic Engineering Xidian University Xi’an710071 China

ISBN: (纸本)9789819970216

As a main part of automated machine learning, Neural Architecture Search (NAS) has been employed in exploring effective Click-Through Rate (CTR) prediction models of recommender systems in recent studies. However, these studies update the architecture parameters by different strategies, such as gradient-based methods or evolutionary algorithms, and may suffer from either interference problems or high time-consuming in the search stage. Besides, the blocks in the search space were usually homogeneous, which means they have the same candidate operations to choose. Such design will result in redundancy in the search space, because many structures are inherently invalid and just increase the complexity of searching. To address the above issues, we implement the three-level automatic design of CTR prediction models using NAS via Shapley value, named as AutoShape. For the search space, we divide it into three parts according to the characteristics of the CTR model. Each part comprises distinctive candidate operations and forms a cell as an individual processing level, which improves the stability of the searched models’ effect and reduces the computational complexity. For the search strategy, we leverage Shapley value, a metric derived from cooperative game theory, to estimate the contribution of the operations in each block and the connections between the blocks, which can find effective models and reduce the time cost. Furthermore, experiments on different benchmark datasets show that the structure obtained from the search performs better than several hand-crafted architectures and is more stable than another NAS-based algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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