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

共 14631 条记录，以下是121-130 订阅

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Garbage Detection System Using image processing

Garbage Detection System Using Image Processing

引用

Disruptive Technologies (ICDT), International conference on

作者： Aditya Pandey Praveen Kumar Rai Aditya Singh Aman Patwa Computer Science Engineering GL Bajaj Institute of Technology & Management Gr. Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This research presents a brand new development system for garbage detection using advanced techniques in image processing. This system is primarily to identify and then classify the waste in real time, thus tackling waste management problems. This has been done using Convolutional Neural Networks (CNNs) and image preprocessing to achieve high accuracy in the detection of different waste types such as plastics, metals, and organics. The experimental results prove the model's robustness across diverse environments that make it amenable to large-scale deployment. The work is meant to serve municipalities, industries, and environmental organizations for automating the waste segregation process [1], [2]. The paper deliberates about some image processing techniques on edge detection, segmentation, and feature extraction since these form the basis for waste identification and classification types that rely on them. Integration of such methods into machine learning and deep learning algorithms, especially using convolutional neural networks (CNN), makes the process more accurate and robust in detection. Also discussed in the review are the pre-processing methods, such as noise reduction and contrast enhancement, which improve the quality of input data to achieve better detection results.

关键词： Waste management Accuracy Reviews Noise reduction Metals Organizations Real-time systems Robustness Convolutional neural networks Plastics

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Face Mask Detection in Real Time using Machine Learning algorithms

Face Mask Detection in Real Time using Machine Learning Algo...

引用

Electronics and Renewable systems (ICEARS), International conference on

作者： Kavitha R Tanishq Sultania Padhmashri V Shreya A Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16’s deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time.

关键词： COVID-19 Training Face masks image segmentation Accuracy Computer viruses Face recognition Transfer learning Static VAr compensators Testing

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Automated Immune Cell Classification Using image processing Techniques

Automated Immune Cell Classification Using Image Processing ...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： B. Sathiyaprasad Arva Hari Vardhan Reddy Usama Abdurrahman A. Annie Micheal Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

~ Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated pre-processing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening; it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders.

关键词： Deep learning Accuracy Immunology Computational modeling Biological system modeling Real-time systems Vectors Classification algorithms Streams Immune system

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Radon-Fourier Transform for Timing Correction in Uncoupled Digital Radar Networks

Radon-Fourier Transform for Timing Correction in Uncoupled D...

引用

German Microwave conference

作者： Julian Aguilar Lukas Paulus David Werbunat Alexander Grathwohl Christian Waldschmidt Institut of Microwave Engineering Ulm University Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Uncoupled radar networks offer many desired features such as large virtual apertures and flexibility in sensor placement without costly radio frequency (RF) cables. However, they also introduce novel challenges, especially the recovery of coherency between individual radar sensors. The correction of timing errors due to sampling frequency offsets (SFO) between a transmitter’s digital-to-analog converter (DAC) and receiver’s analog-to-digital converter (ADC) is essential for the coherent processing of uncoupled digital radar network measurements. systems with different error estimation and correction approaches have been proposed recently, which utilize resampling-based algorithms. However, they are costly to implement, i.e. on an FPGA, and require additional signal processing steps. The herein presented Radon-Fourier Transform (RFT)-based algorithm is capable of directly calculating the corrected radar image without any resampling, negating the downside of additional signal processing steps required after resampling. It is shown that the RFT approach can almost completely restore erroneous radar data, and even outperforms the resampling-based algorithm. In this work, an experimental orthogonal frequency-division multiplexing (OFDM) demonstrator is utilized to verify the concepts, which are applicable to other modulation types typically used for digital radar.

关键词： OFDM Computational modeling Signal processing algorithms Radar Transforms Radar imaging Data models image restoration Frequency measurement Frequency division multiplexing

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An Overview on Millimeterwave FMCW Radar Techniques for Precise Ranging and Material Imaging

An Overview on Millimeterwave FMCW Radar Techniques for Prec...

引用

International Workshop on Mobile Terahertz systems (IWMTS)

作者： Kristof Dausien Francesca Schenkel Jochen Altholz Lukas Piotrowsky Nils Pohl Jan Barowski Institute for Microwave Systems Ruhr University Bochum Bochum Germany Institute for Integrated Systems Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

This scientific paper is an outcome of the MARIE project (for more information, see ***) [M04]. This paper provides an overview, on recent developments as well as future challenges and prospects in measurement methods and algorithms for the determination of an object’s geometrical shape, its position, and its electromagnetic properties using FMCW radar sensors. The current research integrates radar imaging techniques with material characterization algorithms to evaluate the dielectric constant and losses, leveraging ultrawideband systems to capture dispersive features and enable µm-precise localization. Unlike single antenna synthetic aperture radar (SAR) methods, multiple-input multiple-output (MIMO) radar enhances imaging by capturing data from multiple channels and varying incident angles, enriching the information obtained. In conjunction with sensor calibration methods and radar-based material characterization, these imaging techniques provide a mobile platform for spatially resolved, precise reflection measurements in magnitude and phase that are used to derive the electromagnetic properties and, subsequently, to provide a semantically annotated material image. Due to the short wavelength and large apertures near-field wave propagation effects must be incorporated into the signal processing to achieve µm-precise ranging and imaging. Furthermore, the impact of environmental influences on the measurement accuracy must be investigated.

关键词： image sensors Accuracy Wavelength measurement Signal processing algorithms Radar imaging Sensor phenomena and characterization Radar antennas Distance measurement Ultra wideband radar Synthetic aperture radar

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DNA Computing: A Breakthrough in image Cryptography - A Comprehensive Review

DNA Computing: A Breakthrough in Image Cryptography - A Comp...

引用

International CSI Computer conference, CSICC

作者： Hesam Noorpoor Asadollah Shahbahrami Department of Computer Engineering University of Guilan Rasht Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Deoxyribonucleic acid-DNA computing, an interdisciplinary field at the frontier between molecular biology and computer science, has become a revolutionary field. DNA computation provides a new approach to data computation and cryptography. The inherent properties of DNA include high parallelism, excellent data density, and stability; it can hence be considered one of the ideal candidates in cryptographic applications, especially in image encryption. This review provides recent advances in DNA-based cryptographic algorithms, their fusion with chaotic systems, and also their applications to practical scenarios that enhance data security. Different algorithms are studied that exploit the encoding capability of DNA, such as substitution, transposition, and hybrid models including crossover and mutation. It also discusses DNA for secure image transmission in applications like healthcare, industrial IoT, and cloud environments. The latter presents scalability challenges and problems regarding key generation along with certain implementation constraints, while at the same time pointing future research toward a perspective of opening up for stronger cryptography.

关键词： Reviews Scalability DNA Medical services Parallel processing Stability analysis Encryption Molecular biology Computer security DNA computing

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Improving Compressive Imaging Recovery via Measurement Augmentation

Improving Compressive Imaging Recovery via Measurement Augme...

引用

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

作者： Romario Gualdrón-Hurtado Roman Jacome Leon Suarez Emmanuel Martinez Henry Arguello Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In compressive imaging systems, the scene is acquired via linear coded noisy projections, known as measurements, requiring a recovery process to estimate the underlying signal. This recovery is inherently ill-posed, posing a challenge for accurate signal recovery. Existing methods that employ prior information about the signal often fail in practical scenarios. In this work, instead of developing a new prior over the signal, we exploit the structure of the low-dimensional measurements to synthesize an augmented measurement set that can be used in various recovery methods to improve its performance. We used a deep neural network to generate the synthetic measurements from the acquired data. We show the benefits of this approach in two schemes, deep learning-based recovery and the plug-and-play (PnP) algorithm. Particularly, our method is interpreted as a non-linear preconditioning technique for the PnP algorithm. We show improved performance for different sensing matrices.

关键词： image coding Atmospheric measurements Imaging Signal processing algorithms Particle measurements Acoustic measurements Sensors Noise measurement Speech processing image reconstruction

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HPDNET: Hyper Prior Dependent Demosaic Neural Network

HPDNET: Hyper Prior Dependent Demosaic Neural Network

引用

International image processing, Applications and systems conference (IPAS)

作者： Youngil Seo Sungho Jun Dongpan Lim Seongwook Song System LSI Division Samsung Electronics Yongin Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. This presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation.

关键词： image quality Deep learning image sensors Artificial neural networks Pressing Feature extraction Rendering (computer graphics) Information filters Computational efficiency image restoration

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Patch based image processing for complex environment characterization

Patch based image processing for complex environment charact...

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International image processing, Applications and systems conference (IPAS)

作者： Corentin Menier Cyril Meurie Timothée Guillemaille Yassine Ruichek Juliette Marais Univ Gustave Eiffel COSYS-LEOST Villeneuve d’Ascq France UTBM Belfort France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy.

关键词： Training Global navigation satellite system Visualization Satellites Three-dimensional displays Accuracy Uncertainty Vegetation mapping Cameras Trajectory

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Proceedings of SPIE - image processing: algorithms and systems iii

Proceedings of SPIE - Image Processing: Algorithms and Syste...

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Imaging processing: algorithms and systems iii

The proceedings contain 59 papers from the conference of SPIE - image processing: algorithms and systems iii. The topics discussed include: the fast parametric slantlet transform with applications;parameterized reversible integer discrete cosine transform;variations of matched filtering for reduced noise amplification;pupil detection in photo ID;template gradient matching in spherical images;and imaging-based dust sensors;equipment and methods.

关键词： image processing

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