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A Review of Clustering Techniques on image Segmentation for Reconstruction of Buildings 1st

A Review of Clustering Techniques on Image Segmentation for ...

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1st International conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Neguja, Duraimoni Rajan, A. Senthil Alagappa University Tamil Nadu KaraiKudi India Computational Logistics Alagappa University Tamil Nadu KaraiKudi India

ISBN: (纸本)9783031250873

The discovery of a new clustering technique on segmented images based on building structures is a challenging process for researchers. In this chapter, clustering techniques on image segmentation in buildings reform is a mingled process of segments of image. This chapter suggests review of vsrious clustering techniques and the improved strategy for the assembling of partitioned image segments of a representation into several areas according to a similarity trial value. In this chapter, various clustering techniques on distributed particles of image segments is studied as a more complicated procedure that results in computerized model but still common algorithm is not in function. Hence 41 years ago, finding a centralized algorithmic method in clustering separately with help of available data is changed by the lively growth of a broad variety of extremely fussy techniques. Most of the existing clustering techniques are greatly explicit to a definite kind of facts, and a little study is trail to widen common agenda that incorporates the clustering methods. Clustering can be a entirely habitual procedure, but it accomplishes its most excellent outcomes with partially regular techniques, that are directed by a individual machinist. This idea of partial automated procedure obviously engages a situation in which the creature hand will relate with the algorithms and the data in order to produce most favourable clustering methods. The simplest example of the use of a manual interference throughout the charge of clustering outputs on the concern of available procedures. Beyond the kind of input data, the machinist will have to warily choose the finest bespoke process, which major of the moment cannot be done in a routine forum. The prejudiced position of sight of the person is mandatory. Fuzzy C-means clustering, parallel K-means clustering, Hierarchical density-based clustering and more clustering procedures are compared and studied. © 2023, The Author(s), under exclusive l

关键词： image segmentation

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PQMPV: parallel and Quantified Dense Stereo Disparity Estimation Based on Multi-Path Viterbi 8

PQMPV: Parallel and Quantified Dense Stereo Disparity Estima...

引用

8th IEEE International conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Gu, Rongqi Wang, Tianhang Zou, Tianpei Zhang, Bo Li, Zhijun Zhang, Haotian Chen, Guang Tongji University Department of Automotive Engineering Shanghai China Westwell-lab Shanghai China Tongji-Westwell Driverless Commercial Joint Laboratory Shanghai China Tongji University Department of Computer Science and Technology Shanghai China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China University of Science and Technology of China Department of Automation Hefei China

ISBN: (纸本)9798350300178

Stereo depth estimation plays a crucial role in enabling robots, autonomous driving and other applications. As an ill-posed problem, both deep learning based and traditional methods have been extensively studied in recent years. Traditional methods mainly focus on feature matching between multiple images to estimate depth, but often result in blank pixels with zero disparity and also require significant computation time. In this paper, we propose a new stereo disparity estimation algorithm based on the dynamic programming method known as Viterbi. Our approach optimize the range of disparity calculation through image pyramid, combined with Viterbi algorithm for cost aggregation, and accelerates several calculation steps through parallel computing techniques. Additionally, we improve data transmission and processing speed by quantifying data from float to integer. Experiments on the KITTI dataset and MiddEval benchmark demonstrate that our algorithm achieves significant improvements in average error rate and processing speed of disparity map, making it a promising solution for stereo depth estimation in practical applications. © 2023 IEEE.

关键词： Benchmarking

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FPGA Design and Implementation of Approximate Radix-8 Booth Multiplier

FPGA Design and Implementation of Approximate Radix-8 Booth ...

引用

distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Rangarao Orugu Subhash Padamata Yuvaraju Kollati Lakshmeepathi Nakka Yashwanth Nunna Rathnakar Sai Midhilesh Mamidi Electronics and Communication Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In digital systems, multiplication operations are crucial, particularly in applications like image processing. This paper proposes an approximate radix-8 Booth multiplier designed to enhance performance and reduce complexity compared to traditional counterparts. Specifically tailored for signed-unsigned numbers, the proposed multiplier undergoes evaluation via simulation using Xilinx Vivado software with Artix-7 FPGA Board. Leveraging techniques such as modified Booth encoding and parallel partial product generation, it aims to achieve reduced delay and power consumption. The application of this multiplier is demonstrated through integration into Sobel edge detection. Quantitative metrics, including speedup factor and area reduction, are provided to assess performance improvements over existing systems. Furthermore, the proposed design is highly accurate, detecting 98.87% of edges, demonstrating its practicality and usefulness in digital image processing applications.

关键词： Measurement Power demand image coding image edge detection Software Table lookup Delays

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Process Acceleration for HEVC Using parallel Characteristics Calculation and Pixel Array Conversion

Process Acceleration for HEVC Using Parallel Characteristics...

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International conference on Electronics, Information, and Communication (ICEIC)

作者： Yamazaki, Rei Inoue, Toshiyuki Teramura, Yuuki Tsuchiya, Akira Kishine, Keiji Univ Shiga Prefecture Dept Elect Syst Engn 2500 Hassaka Hikone Shiga 5228533 Japan

ISBN: (纸本)9781665409346

To solve the problem of labor shortage due to the decrease in the working population in the construction industry, remote control systems with networked cameras and cockpits are being developed. Currently, the challenges that teleoperation systems faced mainly originate from the transmission of images between sensors and intelligent/control systems. To reduce the delay time that occurs in the transmission of high-definition video, the time required for video compression must be reduced. Therefore, to achieve a video delay time of 150 ms without affecting the operator's operation in a teleoperation system, we proposed, implemented, and verified parallel characteristics operations and pixel array conversion as methods to speed up video compression, and confirmed the speedup of 14.2%.

关键词： compression image-processing latency FPGA H.265 encoding

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Low Light image Enhancement Based on a Pyramid Diffusion Model

Low Light Image Enhancement Based on a Pyramid Diffusion Mod...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Xiang Li Chunling Liu Hui Cheng Dalian University Dalian China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In the process of low light image enhancement, there are problems such as information loss and unclear image details after enhancement. An improved method for low light image enhancement based on the pyramid diffusion model is proposed. The method improves the U-net network structure based on the pyramid diffusion model. A global and local perceptual network architecture is designed for further processing of the downsampled images. The global module captures the global context information through the channel attention mechanism. The local module constructs multi-scale feature block to extract image features at different scales. For provide fine-grained details, the local module uses contrast information to process and enhance the characterization of local features. Finally, an interactive fusion module is designed to fuse different features from the global branch and local branch. The mothod also improves the residual network and uses skip connection with convolution to connect the downsampling and upsampling. Experimental results on the LOL and LOLv2 datasets show that the improved method has better results, which demonstrates the effectiveness of this design.

关键词： Training Information science Fuses Data visualization Network architecture Diffusion models Feature extraction

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Crop Classification methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Undecimated discrete wavelets transform-based image fusion and de-noising in FPGA

Undecimated discrete wavelets transform-based image fusion a...

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International conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Fahad Ahmad Muhammad H. Ahmed Yumna Adnan Department of Electrical Engineering University of Gujrat Gujrat Pakistan

ISBN: (数字)9781837243150

image processing is a vast field of study and has gained significant importance in recent years. image fusion and denoising a re most important methods in the field of image processing. The project develops an image processing system on a Field-Programmable Gate Array (FPGA) platform that uses the Undecimated Discrete Wavelet Transform (UDWT) for image fusion and denoising. By merging several images into a single better image and using sophisticated wavelet techniques to reduce noise the aim is to improve image quality. UDWT preserves image details by maintaining high-frequency components without down sampling. Reconstructing denoised images thresholding to remove noise and dividing images into wavelet coefficients are the steps in the process that culminate in the creation of a superior composite image. Due to its ability to process data in parallel the FPGA platform is preferred over conventional CPU-based techniques for processing large amounts of data quickly. Suitable for real-time applications, this method takes advantage of the intrinsic parallelism of FPGA to process data quickly and efficiently. According to the results the FPGA-based system performs faster and produces better images than traditional techniques making it a reliable option for high-performance image processing application.

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Federated Semi-supervised Medical image Segmentation Based on Asynchronous Transmission 1

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19th International conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Liu, Fangbo Yang, Feng Guangxi Univ Sch Comp & Elect & Informat Nanning 530004 Guangxi Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun Network Technol Nanning 530004 Guangxi Peoples R China Guangxi Univ Key Lab Parallel Distributed & Intelligent Comp Educ Dept Guangxi Zhuang Autonomous Reg Nanning Guangxi Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Most existing medical image segmentation methods under federated learning use labeled data for training. But in practice, high-quality labeled data is scarce. Due to the specialized nature of medical images, manual labeling will waste a lot of manpower and resources. In addition, due to the private nature of medical images, datasets cannot be shared between institutions. Therefore, this paper proposes a semi-supervised medical image segmentation method based on asynchronous federated learning, which purposes to learn a federated model using a small amount of labelled data and a large amount of unlabeled data. First, to generate better pseudo-labels for locally unlabeled data, we set the local as the teacher model and the global as the student model to learn data information in the multi-agency. Then, the global student model is used to guide the generation of the local teacher model and the consistency regularization is performed. In addition, we use the student model to maintain the stability of the global model. Finally, we propose an asynchronous federated algorithm and local iterator mechanism to address the high load and latency problems on the central server caused by differences between client devices in synchronous transmission. Our experiments on two medical image segmentation tasks demonstrate that the method effectively validates the feasibility of federated semi-supervised segmentation and significantly improves model segmentation performance and communication efficiency.

关键词： Federated learning Medical image segmentation Pseudo-label Asynchronous Iterator

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parallel Convolutional Neural Network for Brain Tumor Using MR images 2

Parallel Convolutional Neural Network for Brain Tumor Using ...

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

作者： Lithika, V. Devi, M.K.Kavitha Thiagarajar College of Engineering Department of Computer Science Engineering Madurai India

ISBN: (纸本)9798350328202

We propose a method that utilizes an Auto-Context Convolutional Neural Network (CNN) to learn important local and global image features from 2D patches of varying window sizes. Brain imaging is a crucial first step in many neuroimaging techniques. The precision and efficiency of brain extraction are important to the overall accuracy. Brain extraction algorithms rely on brain atlases that combine questions about anatomy and assume geometry. However, when these assumptions are violated, the effectiveness of this process is limited. As a result, this study presents a CNN-based automated technique for developing an accurate brain extraction tool. We use two different methods to learn local and global images from two dimension patches: 1) Three parallel 2D convolutions based on three different dimensions (axial, coronal and sagittal). This uses a voxel method of implicitly learning 3D data without computationally expensive 3D convolution. 2 U-net architecture-based advanced convolutional network. We employ network post-processing outputs as interactive data, together with raw pictures, to extract local images and brain connections from non-brain tissue. The study proposes a new model for NLM filtering of MRI images. The proposed technique was tested MRI data. We have achieved outstanding results in brain extraction with our CNN algorithm. It has demonstrated better performance than two recently released public reference sets, LPBA40 and OASIS, with Dice overlap coefficients. Our automated content algorithms have enabled us to make significant improvements. We have also evaluated our algorithm's performance in extracting fetal brains from reconstructed MRI datasets with arbitrary orientation Our CNN surpasses previous approaches in extracting fetal brains from MRI. Our algorithm has achieved a Dice coefficient of 95.97%. Iteratively using posterior probability maps and image patches to extract the brain from non-brain tissue. © 2024 IEEE.

关键词： Brain

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Patronus: Countering Model Poisoning Attacks in Edge distributed DNN Training

Patronus: Countering Model Poisoning Attacks in Edge Distrib...

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IEEE conference on Wireless Communications and Networking

作者： Zhonghui Wu Changqiao Xu Mu Wang Yunxiao Ma Zhongrui Wu Zhenyu Xiahou Luigi Alfredo Grieco State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Information Technology Zhejiang Financial College Zhejiang China Jiangxi Academy of Cyber Security Jiangxi China Department of Electrical and Information Engineering and CNIT Bari Italy

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

As Deep Neural Networks (DNNs) are evolving in complexity to meet the demands of novel applications, a single device becomes insufficient for training, leading to the emergence of distributed DNN training. However, this evolution exposes a gap in research surrounding security vulnerabilities on model poisoning attacks, especially in model parallel setups, an area that has been scarcely studied. To bridge this gap, we introduce Patronus, an approach that counters model poisoning attacks in distributed DNN training, accommodating both data and model parallelism. With the employment of Loss-aware Credit Evaluation, Patronus scores each participating client. Based on the continuously updated credit, malicious clients are isolated and detected after multiple epochs by Shuffling-based Isolation Mechanism. Additionally, the training system is reinforced by Byzantine Fault-tolerant Aggregation to minimize malicious client impacts. Comprehensive experiments confirm Patronus's superior reliable and efficient performance over the existing methods under attack scenarios.

关键词： Training Fault tolerance image edge detection Fault tolerant systems distributed databases Artificial neural networks parallel processing

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