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

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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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Data-driven Optimization of Complex systems (DOCS), International conference on

作者： Zhijie Zhang Huang Bai Ljubiša Stanković Junmei Sun Xiumei Li School of Information Science and Technology Hangzhou Normal University Hangzhou China Faculty of Electrical Engineering University of Montenegro Podgorica Montenegro

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression pro-cedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iter-ative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission- augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through exper-iments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Redundancy Signal processing algorithms Signal processing Reconstruction algorithms Propagation losses Sensors Reliability image reconstruction Compressed sensing Optimization

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Deep Learning Approach to Automated Tomato Plant Leaf Disease Diagnosis 2

Deep Learning Approach to Automated Tomato Plant Leaf Diseas...

引用

2nd International conference on Electronics and Renewable systems, ICEARS 2023

作者： Karthika, I. Megha, M. Roshni, M. M. Kumarasamy College of Engineering Dept. of Computer Science Engineering Tamilnadu Karur India

ISBN: (纸本)9798350346640

India's agriculture is primary occupation and it experiences a yearly loss of 35% in crop productivity due to plant-related issues. Advances in technology, such as precision agriculture and the use of Deep learning algorithms, are helping to make this possible. A number of practical and technological challenges, including dataset collecting and labelling, variability in plant leaf images, inter-class similarity, imbalanced datasets, generalization, and real-time detection, are prevalent in the challenging task of detecting plant leaf disease. The main goal of utilizing a convolutional neural network (CNN) for plant leaf disease detection is to precisely categorize images of plant leaves into categories of healthy or diseased. Depending on the particular application scenario, there could be additional goals that researchers try to accomplish with their CNN models in addition to this main one. Tomato is a widely cultivated vegetable and is an important food crop worldwide. Employing machine learning and image processing methods for detecting diseases in tomato plant leaves can be highly efficient. These techniques can be used to analyze images of tomato leaves taken in the field, and an accurate algorithm can be used to identify the presence of disease symptoms. One such approach is using Convolutional Neural Networks (CNNs) algorithm which have been trained on a dataset of images of tomato leaves with and without disease symptoms. This can be done by gathering the images of the healthy tomato leaves and the infected ones and then training a CNN model to detect the difference. The trained model can categorize new images of tomato leaves into healthy or infected, offering a quick and precise method to identify diseases in tomato plants' leaves. © 2023 IEEE.

关键词： image segmentation

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Review on Advanced Vehicle Recognition System: OCR and REST API Integration for Efficient Results 5

Review on Advanced Vehicle Recognition System: OCR and REST ...

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5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Rohit Kumar Saxena, Aumreesh Kumar Jhapate, Arun Shrivastava, Ritu Srivastava, Rajiv Sirt Bhopal India Csit Sirt Bhopal India Cse Sirt Bhopal India

ISBN: (纸本)9798350383522

Vehicle number plate recognition ranks first in traffic management, especially in densely populated areas like India. This paper presents a conceptual approach that integrates state-of-the-art technologies to accurately identify vehicles using video license plate scanning.[1] Powered by a Raspberry Pi and a set of advanced algorithms, the system is ideal for securing confined spaces including educational institutions and high-security roads Using the power of OpenCV, the system catches on and runs video programs to extract sensitive data. This study digs deep into the difficult areas of acceptable behavior and photo appearance, with license plate recognition being a major focus. A notable feature is the integration of Optical Character Recognition (OCR) technology via a REST API, which provides the ability to quickly and accurately extract colors from license plates REST API continuous capture of license plates creates a plate image, which provides key information from these plates.[3]. Experimental findings confirm the capabilities of the system, showing recognition rates in standard with neural network-based strategies for number plate recognition The proposed model opens up possibilities for various applications, from regulatory requirements to checking forged vehicle registrations, extending to activity-monitoring, and encouraging electronic tax collection In together with OCR and REST API integration, this review shows a remarkable walk away progressive system detection vehicle. The convergence assures increased accuracy and skill in number plate recognition, and helps the system for safer, smarter and safer transportation systems. © 2024 IEEE.

关键词： Advanced image processing Automated Number Plate Recognition Efficient Vehicle Identification License Plate Recognition OCR Technology Optical Text Recognition REST API Integration Vehicle Detection System

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Real Time Cheating Detection Pipeline for Online Exams 30

Real Time Cheating Detection Pipeline for Online Exams

引用

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

作者： Dadak, Ahmet Uyan, Ufuk Ozturk, Mahiye Uluyagmur Huawei Turkey R&D Ctr Istanbul Turkey

ISBN: (纸本)9781665450928

Remote exams and job interviews have gained popularity due to pandemics and the advantage of remote working conditions. Most companies and institutions use online platforms for recruitment and examination. However, one of the critical problems of remote exam systems is that the exams cannot be held in a reliable environment. In this work, we present a cheating detection system for online exams. This system provides real-time detection of suspicious events such as another person, electronic device usage, candidate absent status by accessing the microphone and camera stream of the candidate's device through the browser. The cheating detection system can detect other people than candidates, electronic device usage, voice activity, and candidate absence status using state-of-the-art deep learning algorithms. When a suspicious event is detected, its moments are recorded as a video. The advantages of the real time cheating detection pipeline are as follows: 1) The system provides real time suspicious activity detection and produce instant alarm for proctors, 2) the system does not require an external program installation, 3) the number of user the system will serve at the same time has become scalable since it is run through the browser.

关键词： Cheating detection pipeline Face recognition Object detection image processing Voice processing

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Liquid Filling Quality Control System for Beverage Industry Using image processing (CNN)

Liquid Filling Quality Control System for Beverage Industry ...

引用

International conference on Control, Automation and systems ( ICCAS)

作者： B. Chatchapol W. Autanan W. Anan Department of Electrical Engineering Faculty of Electric Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand Robotics Engineering and AI Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Electronic Engineering and Automation Control System Faculty of Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

The beverage industry has been leveraging automation to enhance production efficiency. An essential advancement involves the use of AI and image processing to ensure the high quality of products. image processing systems employ sophisticated algorithms to meticulously inspect specific aspects of the production process (liquid level, lid sealing, text print). The CNN algorithm is primarily utilized to scrutinize water bottle images to verify their quality. Maintaining consistent production speed poses a challenge, underscoring the importance of examining the image processing system from various perspectives. The research proposed implementing a system that oversees liquid filling quality through an image processing algorithm, a camera module, and conveyor belt speed control to address. This system monitors liquid levels and bottle capping while the production line is operational. On average, the system achieves a 98.89 percent accuracy rate in detecting liquid levels and a 100 percent accuracy rate in sealing the lids. Furthermore, the conveyor speed control can adjust automatically to detect variations in product movement speed.

关键词： Liquids image processing Velocity control Production Quality control Inspection Belts Filling Beverage industry Monitoring

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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

作者： Qiang Liu Mahdi Soleymani Hessam Mahdavifar National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.

关键词： image coding Signal processing algorithms Signal processing Acoustics Sensors Decoding Sparse matrices Speech processing Compressed sensing

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Efficient Optimization of Dual Port Circularly Polarized 1*2 Array based Microstrip Antenna using Machine Learning algorithms

Efficient Optimization of Dual Port Circularly Polarized 1*2...

引用

IEEE International conference on Signal processing, Informatics, Communication and Energy systems (SPICES)

作者： Mamta Sood Avinash Rai Department of Electronics and Communication Engineering UIT-RGPV Bhopal India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

This work studies the design and optimisation of a two-port high gain printed antenna using machine learning algorithms. The following are a few crucial characteristics of the suggested radiating structure: (i) In order to shorten the simulation time, machine learning (ML) techniques based on ANN and random forests are used to predict the reflection coefficient (RC) (|S11|) features of the designed radiator; (ii) Circularly polarized waves in the 10.38-10.45 GHz range are produced by using a stair slot; (iii) A 1*2 array structure yields high gain, or 7.5 dBi, within the operating frequency range; and (iv) polarization diversity concept is provided by opposite orientation of aperture, which raises the isolation level to more than 30 dB. It is established that the intended radiator functions between 10.37-10.55 GHz by comparing the findings of the HFSS and CST with the predictions made by the ML algorithm. The suitability of the suggested radiator for X-band applications is approved by good values for the diversity and far-field characteristics.

关键词： Wireless communication Microstrip antenna arrays Time-frequency analysis Machine learning algorithms Signal processing algorithms Stairs SPICE Reflection coefficient Gain Optimization

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image Enhancement Algorithm based on Local Contrast for Convolutional Neural Network-Based Infrared Target Recognition: ∗Note: Subtitles are not captured in Xplore and should not be used 24

Image Enhancement Algorithm based on Local Contrast for Conv...

引用

24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nian, BingKun Ma, LiYuan Zhang, Yan Zhang, Yi Shi, HuaJun The 32nd Institute China Electricity Science and Technology China College of Science National University of Defense Technology China College of Electronic Science National University of Defense Technology China

ISBN: (纸本)9798350319934

In the task of infrared weak and small target recognition, in order to improve the image quality and solve the problem of poor learning ability of convolutional neural network (CNN) due to the imbalance of positive and negative samples, this study proposes a three-stage image enhancement algorithm named adaptive filter for quality enhancement based on local contrast (AFQELC). Firstly, AFQELC analyzes the statistical characteristics of the image and constructs a local contrast adaptive filter (LCAF) to enhance the detailed information of the image, which promotes the deep learning model to learn low-level semantic information. Secondly, principal component analysis (PCA) fusion combines information from the original image and the image enhanced by LCAF to reduce noise. Finally, the gradient component of the original image is extracted to further revise the processing result, which promotes the deep learning model to learn advanced semantic information. The proposed non-data-driven algorithm has a clear and interpretable process, which is superior to other traditional and neural network based image enhancement algorithms. The experiment results show that the quality of images enhanced by AFQELC is improved significantly. In addition, AFQELC can improve the recognition and positioning accuracy of the CNN-based algorithm for infrared target recognition by alleviating the imbalance of positive and negative samples. © 2022 IEEE.

关键词： image enhancement

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Deep Learning-Based Improved Automatic Building Extraction from Open-Source High Resolution Unmanned Aerial Vehicle (UAV) imagery 1

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2nd International conference on Unmanned Aerial systems in Geomatics (UASG)

作者： Maniyar, Chintan B. Kumar, Minakshi Indian Inst Remote Sensing Photogrammetry & Remote Sensing Dept Dehra Dun 248001 Uttarakhand India

ISBN: (数字)9783031193095

ISBN: (纸本)9783031193118;9783031193095;9783031193088

Automatically extracting buildings from remotely sensed imagery has always been a challenging task, given the spectral homogeneity of buildings with non-building features as well as the complex structural diversity within the image. Traditional machine learning (ML) based methods deeply rely on a huge number of samples and are best suited for medium-resolution images. Unmanned aerial vehicle (UAV) imagery offers the distinct advantage of very high spatial resolution, which is helpful in improving building extraction by characterizing patterns and structures. However, with increased finer details, the number of images also increases many folds in a UAV dataset, which require robust processing algorithms. Deep learning algorithms, specifically Fully Convolutional Networks (FCNs) have greatly improved the results of building extraction from such high resolution remotely sensed imagery, as compared to traditional methods. This study proposes a deep learning-based segmentation approach to extract buildings by transferring the learning of a deep Residual Network (ResNet) to the segmentation-based FCN U-Net. This combined dense architecture of ResNet and U-Net (Res-U-Net) is trained and tested for building extraction on the open source Inria Aerial image Labelling (IAIL) dataset. This dataset contains 360 orthorectified images with a tile size of 1500 m(2) each, at 30 cm spatial resolution with red, green and blue bands;while covering total area of 805 km(2) in select US and Austrian cities. Quantitative assessments show that the proposed methodology outperforms the current deep learning-based building extraction methods. When compared with a singular U-Net model for building extraction for the IAIL dataset, the proposed Res-U-Net model improves the overall accuracy from 92.85% to 96.5%, the mean F1-score from 0.83 to 0.88 and the mean IoU metric from 0.71 to 0.80. Results show that such a combination of two deep learning architectures greatly improves the building extract

关键词： Transfer learning Fully convolutional networks image segmentation Building extraction

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Interactive Sign Language Learning System using Deep Learning

Interactive Sign Language Learning System using Deep Learnin...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International conference on

作者： S Ravikumar P. Dimple Praharsha J. Sri Harsha Priya V. Naga Rohit Sai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tamil Nadu

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The “Interactive Sign Language Learning System” is a sophisticated application designed to facilitate the learning process of sign language learners. This comprehensive system encompasses several key features, including sign language alphabet and word recognition, text-to-action conversion for learners, multi-language support, and integrated voice output functionality. The system utilizes advanced algorithms for sign language recognition, employing techniques such as image processing and machine learning to accurately interpret hand gestures and movements. For sign language alphabet and word recognition, a combination of computer vision algorithms, possibly including convolutional neural networks (CNNs) or recurrent neural networks (RNNs), may be employed to analyze input images or video streams and classify them into corresponding sign language symbols or words. Text-to-action conversion involves mapping textual input to corresponding sign language actions or gestures, possibly using natural language processing (NLP) algorithms to understand the semantics of the text and generate appropriate sign language representations. The system’s accuracy, measured in terms of correctly recognized sign language symbols or words, would depend on the effectiveness of the algorithms employed and the quality of the training data. With rigorous development and training, the system aims to achieve high accuracy levels, in sign language recognition and text-to-action conversion tasks.

关键词： Sign language Accuracy Machine learning algorithms image recognition Symbols Training data Speech recognition Natural language processing Classification algorithms Convolutional neural networks

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