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

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Proceedings - 37th International conference on VLSI Design, VLSID 2024 - held concurrently with 23rd International conference on Embedded systems, ES 2024

Proceedings - 37th International Conference on VLSI Design, ...

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37th International conference on VLSI Design, VLSID 2024

ISBN: (纸本)9798350384406

The proceedings contain 124 papers. The topics discussed include: a novel approach to control a DC-DC converter using its empirical physical model;optimizing medical image analysis: leveraging efficient hardware and AI algorithms;DFT static verification using early RTL exploration and debug for mobile SoC and edge AI applications;reconfigurable processing-in-memory architecture for data intensive applications;parallel-series diode-based ring amplifier for switched capacitor circuits;enhancing output corruption through GSHE switch based logic encryption;on managing test-time, power, and layer assignment in 3D socs with built-in-self-repair modules;a compact low-power 29 Gb/s pseudo random quaternary sequence generator in 65 nm CMOS;and CAD tools pathway in hardware security.

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Mobile mapping platform with integrated end-to-end data processing chain for smart city applications 7

Mobile mapping platform with integrated end-to-end data proc...

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conference on Remote Sensing Technologies and Applications in Urban Environments vii Part of SPIE Remote Sensing conference

作者： Stahl, Bastian Reiterer, Alexander Albert Ludwigs Univ Freiburg Dept Sustainable Syst Engn INATECH Emmy Noether Str 2 Freiburg Germany Fraunhofer Inst Phys Measurement Techn IPM Georges Kohler Allee 301 Freiburg Germany

ISBN: (纸本)9781510655423;9781510655416

We present the concept of a mobile measurement platform paired with an end-to-end data processing chain that enables analysis of multimodal sensor data in real time for smart city mapping. The proposed system can be integrated on mobile platforms into everyday traffic in urban environments. The online pre-processed and compressed information can then be used to directly update a cloud-based digital twin. This enables the creation of a virtual image of entire cities and generates data that can be used for real time Urban Information Modeling, and thus a valuable planning tool to provide up-to-date information at any time. The generated data form the basis for decision-making on improving mobility flows for smart transportation systems and autonomous vehicles and the survey of infrastructure and vegetation for sustainable urban development. The proposed concept is achieved using energy efficient embedded sensors and processing units in combination with computational optimized software architectures close to the sensors.

关键词： Mobile Mapping System Dataprocessing Real-time Smart City

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AG-Mono: A Monocular Dataset for Unmanned Air Vehicles 30

AG-Mono: A Monocular Dataset for Unmanned Air Vehicles

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30th IEEE Signal processing and Communications Applications conference (SIU)

作者： Simsek, Bugra Giden, Ibrahim Halil Bilge, Hasan Sakir ASELSAN Girisimcilik Merkezi Ankara Turkey Gazi Univ Ankara Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

One of the most important information needed while performing unmanned aerial vehicles (UAV) operations is about the platform location and the environment. Such platforms mostly use GNSS signals outdoors. However, in indoor areas where GNSS signals cannot be received or in situations where signals are jammed, it is not possible to obtain location information using these signals. For that reason, alternative navigation systems have become so crucial. One of the most preferred systems among navigation technologies is the visual simultaneous localization and mapping (vSLAM) method performed using RGB cameras on the UAVs. In this study, an open monocular image dataset called AG-Mono was created and published online to test the performance of vSLAM algorithms. This dataset was created at three different exposure times using a handheld platform, and it includes video sequences at 640x480 image resolution. The experimental area where the images were created is a closed corridor with 16.5 x 4.5 meters and four sharp corners.

关键词： Unmanned Aerial Vehicles (UAVs) Visual SLAM (vSLAM) Monocular image Dataset

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Proceedings - International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

Proceedings - International Conference on Augmented Intellig...

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2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

ISBN: (纸本)9781665489621

The proceedings contain 245 papers. The topics discussed include: artificial intelligence system based embedded real-time system power optimization and adaptability;real-time power transmission and transformation online monitoring based on convolutional neural network algorithm;multi-stage lung cancer detection and prediction using image processing techniques;a comprehensive analysis of text analysis algorithms for e-mail itemization;automatic seed generation for weighted test pattern generation;a review of video stabilization algorithms;nuclei localization in pap smear images for ovarian cancer visualization;a survey on alleviating the naive bayes conditional independence assumption;human activity recognition using relative relationship between different human body regions;survey on various methods and algorithms used for plant pest and diseases;vibrating motor based wearable vibrating analog alarm;detection of Indian fake currency using image processing;smart traffic light control system using image processing;knowledge graph curation from text via ontologies;a review on natural language processing based automatic question generation;the role of convolutional neural network in vehicle detection on spatial - temporal road traffic data;and application programming interface (API) based student activity tracking system.

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Towards Automation of Reference Data Generation for ADAS/AD Functions Development - ALiVA Framework

Towards Automation of Reference Data Generation for ADAS/AD ...

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2024 International Automotive CAE conference on Road to Virtual World, RVW 2024

作者： Mardhekar, Amogh Pawar, Rushikesh Mohod, Rucha Shirudkar, Rohit Hivarkar, Umesh N. FEV.io India

The advancements towards autonomous driving have propelled the need for reference/ground truth data for development and validation of various functionalities. Traditional data labelling methods are time consuming, skills intensive and have many drawbacks. These challenges are addressed through ALiVA (automatic lidar, image & video annotator), a semi-automated framework assisting for event detection and generation of reference data through annotation/labelling of video & point-cloud data. ALiVA is capable of processing large volumes of camera & lidar sensor data. Main pillars of framework are object detection-classification models, object tracking algorithms, cognitive algorithms and annotation results review functionality. Automatic object detection functionality creates a precise bounding box around the area of interest and assigns class labels to annotated objects. Object tracking algorithms tracks detected objects in video frames, provides a unique object id for each object and performs distance ranging. A unique feature of cognitive algorithms is the elimination of non-realistic objects of interests which appear in billboards or advertisements on buses/trucks. The framework also has a feature of event detection like overtaking scenarios or pedestrians/animals crossing the roads. Annotation review functionality is provided where assessment and correction of auto annotated data can be done manually. The results can be saved in standard file formats such as txt, csv, Json and open ASAM, ensuring compatibility across different systems. ALiVA replaces traditional annotation methods, thereby reducing the effort, the need for skilled resources and the time required to annotate large datasets. This eliminates human biases, manual errors and inconsistencies. ALiVA is validated for numerous customer requirements and offers a large amount and variety of data to quantify the benefits offered. Some of the distinguishing features are models and functionalities that are optimi

关键词： image annotation

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A Novel Machine Learning Algorithm for Prostate Cancer image Segmentation using mpMRI

A Novel Machine Learning Algorithm for Prostate Cancer Image...

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2023 International conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Shukla, Tushar Dhar Kalpana, K. Gupta, Richa Kalpanadevi, D. Walid, Md. Abul Ala Keshav Kumar, K. Chandigarh University Uittr Department of Physical Education Punjab India Presidency University Yelahanka School of Cse and Is Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Krishnan koil India Department of Computer Science and Engineering Khulna9203 India Department of Humanities and Mathematics Hyderabad India

ISBN: (纸本)9798350333602

Recently, the advancements in technology and the changes in lifestyle behaviors of people leads to a sedentary routine of everyday habits. For this reason, numerous cancers have been developed and causes death for millions of people every year. Although, cancer is a deadly disease, early detection can help for survival. Especially for prostate cancer (PCa), early detection helps to cure the disease. Several researches have been done in medical image processing using Artificial Intelligence (AI) algorithms, yet accuracy and computational complexities limits the performance. With the intension of introducing a novel model for PCa detection from multi-parametric Magnetic Resonance Imaging (mpMRI), this study introduces an enhanced image segmentation model using the efficiency of Machine Learning (ML) algorithm together with Moth Flame Optimization (MFO) Algorithm to eradicate the previous issues. Generally, segmentation of an image is a partition of the image into multiple regions which enhances the classification performances. The major phases in this research includes 1. Data Pre-processing, 2. Feature Extraction, and finally,3. Segmentation. In data pre-processing, noises in the input images are eliminated using Gaussian filtering. The efficiency of MFO is employed to extract the optimal features from the images, and the extracted images are further subjected for U-Net segmentation. Moreover, the performance of the proposed model is validated through a comparative analysis over state-of the-art models in terms of DSC. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Implementation of dual-phase grating system phase contrast information extraction based on generative adversarial networks 4

Implementation of dual-phase grating system phase contrast i...

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4th International conference on Laser, Optics, and Optoelectronic Technology, LOPET 2024

作者： Zeng, Jiacheng Huang, Jianheng Zhu, Minghui Li, Jiaqi Lei, Yaohu Liu, Xin Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781510681903

In the dual-phase grating system of X-ray phase-contrast imaging, the reduction in structural requirements for the absorption grating relaxes the constraints on its aspect ratio and period, thereby broadening the applicability of X-ray phase-contrast imaging techniques in a wider market. Traditionally, the extraction of phase-contrast information primarily relies on phase-stepping methods or Fourier transform algorithms, which often introduce artifacts and blurring into the images. To achieve higher quality image restoration, this study introduces generative adversarial networks (GANs) for high-quality image reconstruction. Our approach uses ideal images as labels and images containing object stripe information as inputs, utilizing GANs for feature learning to facilitate the transformation from object stripe images to high-quality phase-contrast images. The network also employs transfer learning to process previously unseen object stripe images and generate corresponding phase-contrast images. This technique not only significantly enhances image resolution but also substantially reduces artifacts and blurring in the image processing, paving the way for high-precision demands in medical diagnostics and industrial inspection. © 2024 SPIE.

关键词： image resolution

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Automatic Volume Control Using image processing and Deep Learning Techniques: A Review

Automatic Volume Control Using Image Processing and Deep Lea...

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Intelligent systems for Cybersecurity (ISCS), International conference on

作者： Kinshuk Theraja Sakshi Gupta Deepak Kumar Gupta Amity Institute of Information and Technology Amity University Noida

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This paper presents an innovative approach to automatic volume control using image processing and deep learning techniques. The ability to automatically adjust volume levels based on environmental factors and user preferences has significant implications for various audio applications, including teleconferencing systems, smart devices, and public address systems. By combining image processing algorithms with deep learning models, this paper aims to develop a robust and adaptive volume control system capable of accurately adjusting audio levels in real-time. The paper discusses the theoretical foundations, technical implementation, experimental results, and potential applications of the proposed automatic volume control system.

关键词： Deep learning Visualization Teleconferencing Solid modeling image processing Software algorithms Process control

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I2P-Rec: Recognizing images on Large-scale Point Cloud Maps through Bird's Eye View Projections

I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps ...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Zheng, Shuhang Li, Yixuan Yu, Zhu Yu, Beinan Cao, Si-Yuan Wang, Minhang Xu, Jintao Ai, Rui Gu, Weihao Luo, Lun Shen, Hui-Liang Zhejiang Univ Coll Opt Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Ningbo Res Inst Ningbo 315100 Peoples R China Homo Technol Co Ltd Beijing 100192 Peoples R China

ISBN: (纸本)9781665491907

Place recognition is an important technique for autonomous cars to achieve full autonomy since it can provide an initial guess to online localization algorithms. Although current methods based on images or point clouds have achieved satisfactory performance, localizing the images on a large-scale point cloud map remains a fairly unexplored problem. This cross-modal matching task is challenging due to the difficulty in extracting consistent descriptors from images and point clouds. In this paper, we propose the I2P-Rec method to solve the problem by transforming the cross-modal data into the same modality. Specifically, we leverage on the recent success of depth estimation networks to recover point clouds from images. We then project the point clouds into Bird's Eye View (BEV) images. Using the BEV image as an intermediate representation, we extract global features with a Convolutional Neural Network followed by a NetVLAD layer to perform matching. The experimental results evaluated on the KITTI dataset show that, with only a small set of training data, I2P-Rec achieves recall rates at Top-1% over 80% and 90%, when localizing monocular and stereo images on point cloud maps, respectively. We further evaluate I2P-Rec on a 1 km trajectory dataset collected by an autonomous logistics car and show that I2P-Rec can generalize well to previously unseen environments.

关键词： Stereo image processing

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Chaos Theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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