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

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Diffusion models as plug-and-play priors 36

Diffusion models as plug-and-play priors

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36th conference on Neural Information processing systems (NeurIPS)

作者： Graikos, Alexandros Malkin, Nikolay Jojic, Nebojsa Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Univ Montreal Mila Montreal PQ Canada Microsoft Res Redmond WA USA

ISBN: (纸本)9781713871088

We consider the problem of inferring high-dimensional data x in a model that consists of a prior p(x) and an auxiliary differentiable constraint c(x, y) on x given some additional information y. In this paper, the prior is an independently trained denoising diffusion generative model. The auxiliary constraint is expected to have a differentiable form, but can come from diverse sources. The possibility of such inference turns diffusion models into plug-and-play modules, thereby allowing a range of potential applications in adapting models to new domains and tasks, such as conditional generation or image segmentation. The structure of diffusion models allows us to perform approximate inference by iterating differentiation through the fixed denoising network enriched with different amounts of noise at each step. Considering many noised versions of x in evaluation of its fitness is a novel search mechanism that may lead to new algorithms for solving combinatorial optimization problems. The code is available at https://***/AlexGraikos/diffusion_priors.

关键词： image segmentation

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Exploring Data as Quantum Bits: Analysis of Quantum image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

引用

2023 IEEE International conference on Quantum Technologies, Communications, Computing, Hardware and Embedded systems Security, iQ-CCHESS 2023

作者： Gopakumar, Parvathy Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Pala Kottayam India

ISBN: (纸本)9798350314946

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum Information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum image processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era. © 2023 IEEE.

关键词： Qubits

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Deep Learning-Based Diagnosis of Rice Sheath Rot Disease: A Wavelet-Filtered Approach for Feature Extraction and Analysis

Deep Learning-Based Diagnosis of Rice Sheath Rot Disease: A ...

引用

2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Atheeswaran, Athiraja Maheshkumar, K. Jayareka, K.S. Janani, B. Lakshmanan, K. Sobiyaa, P. Malla Reddy College of Engineering Department of Cse Telangana Hyderabad India Bannari Amman Institute of Technonology Dept of Computer Science and Design Tamilnadu Erode India Sona College of Technology Dept of Computer Science and Engineering Tamilnadu Salem India Dr.NGP Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Hindusthan College of Engineering and Technology Dept of Cse Tamilnadu Coimbatore India Bannari Amman Institute of Technonology Dept of Information Technology Tamilnadu Erode India

ISBN: (纸本)9798350359688

A major danger to rice farming, rice sheath rot disease affects the quantity and quality of one of the main food crops grown worldwide. This research uses a large dataset gathered from many sources, such as agricultural universities, research centres, and first hand field observations, to provide a unique method of diagnosing Rice Sheath Rot disease using cutting-edge deep learning algorithms. Our work is primarily focused on creating a dependable and effective diagnostic tool using state-of-the-art image processing and machine learning methods. Wavelet filters are applied in the first step of our approach to pre-process photos. This method is essential for eliminating noise and improving the quality of the dataset, which is made up of high-definition photos of rice plants with the disease Sheath Rot. The colour, shape, and texture of the sick spots are then analyzed using advanced feature extraction techniques. When separating plant tissues that are diseased from those that are not, these characteristics are essential. Convolutional Neural Networks (CNN), our primary diagnostic model's foundation, were selected due to their shown efficacy in picture recognition and classification applications. The preprocessed and feature-enhanced dataset is used to train the CNN model, giving it the ability to recognize the complex patterns and traits connected to Sheath Rot illness. The architecture of the model is built to maximize efficiency and accuracy, guaranteeing quick and accurate illness identification. By utilizing AI and deep learning, the proposed method seeks to not only help farmers and agronomists identify diseases early on but also make a substantial contribution to the area of agricultural disease diagnostics. Combine that our method, which combines CNN-based analysis with wavelet-filtered pre-processing establishes a new standard for the identification and treatment of Rice Sheath Rot disease. This might result in improved crop management techniques and more pot

关键词： image enhancement

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Comparative image processing and Object Detection in Aerial Search and Rescue

Comparative Image Processing and Object Detection in Aerial ...

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Cognitive Robotics and Intelligent systems (ICC - ROBINS), International conference on

作者： Sushil Rawat Abhay Singh Rawat Vikas Singh Mewad Himadri Vaidya Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and image processing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and image processing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive image processing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with image processing can improve the performance of model. This research shows that combining image processing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding image processing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions.

关键词： image quality image processing Brightness Object detection Search problems Autonomous aerial vehicles Real-time systems

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Hybrid prognosis of drill-bits based on direct inspection 5

Hybrid prognosis of drill-bits based on direct inspection

引用

5th International conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Bernini, Luca Malguzzi, Ugo Albertelli, Paolo Monno, Michele Politecn Milan Dept Mech Engn Via Masa 1 I-20156 Milan Lombardy Italy MUSP Macchine Utensili Sistemi Prod Str Torre Razza I-29122 Piacenza Emilia Romagna Italy

Within the paradigm of industry 5.0, manufacturing systems are seeking for human-centred production, where the operator finds high-level supervision tasks. In this context, low-level decision making should be performed by machines themselves. In this paper, a hybrid prognosis algorithm is developed to automatically inspect the cutting edges of drill-bits and to predict their Remaining Useful Life (RUL) and the associated probability density function. The solution relies on the automatic measurement of flank wear through convolutional filtering and edge detection. Prognosis exploits particle filter, which updates multi-layer perceptron with online data, to adaptively predict drill-bits RUL. The solution reduces the experimental preliminary run-to-failures needed for training standard machine learning algorithms, exploiting them in a real-time adaptive scenario, while predicting tool RUL under untested and variable cutting process operations. The algorithm uses direct wear observations, taken during set- up times (e.g., tool changes, workpiece change), thus not interfering with the process. (c) 2023 The Authors. Published by ELSEVIER B.V.

关键词： Hybrid prognostics Direct drill-bit inspection image processing Remaining Useful Life prediction

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Vision-based Human Identification with Face and Nametape Recognition in Aerial Casualty Monitoring System 32

Vision-based Human Identification with Face and Nametape Rec...

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32nd IEEE International conference on Robot and Human Interactive Communication (RO-MAN)

作者： Lee, Jaeyeon Quist, Ethan Chambers, Jonathan Peel, Justin Roman, Kelly Fisher, Nathan TATRC Ft Detrick MD 21702 USA Arete Arlington VA USA

ISBN: (纸本)9798350336702

In emergency rescue scenarios, rapid identification of human casualties is a critical first step in enhancing emergency medical response. This task can be limited by the physical and cognitive capacity of rescue personnel, who are exposed to significant risk. The use of small unmanned aerial systems (sUAS) equipped with autonomous casualty assessment abilities can reduce these limitations and risks by enabling remote casualty detection, identification, and vitals assessment, providing standoff protection, and eliminating the need for human personnel to access the potentially hazardous scene. This paper presents a vision-based casualty assessment framework and specifically discusses our casualty identification software, which is designed to recognize the faces of casualties and identify their nametapes in images captured by sUAS under realistic conditions. Our approach addresses the limitations of the sUAS-captured long-distance images to enable accurate identification in challenging casualty monitoring situations. The face and nametape recognition algorithms will be integrated into the larger casualty perception framework and embedded into sUAS platforms to assist with emergency rescue operations. The total casualty perception system will detect, identify, and evaluate the condition of casualties from a remote location, providing standoff protection to first responders and rapid information to inform a suitable medical treatment plan.

关键词： Aerial image processing Neural network Face and text recognitions Synthetic text data Human detection identification monitoring Unmanned Aerial systems

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Benchmarking and Analyzing 3D-aware image Synthesis with a Modularized Codebase 37

Benchmarking and Analyzing 3D-aware Image Synthesis with a M...

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Wang, Qiuyu Shi, Zifan Zheng, Kecheng Xu, Yinghao Peng, Sida Shen, Yujun Ant Group China HKUST Hong Kong CUHK Hong Kong ZJU China

ISBN: (纸本)9781713899921

Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final performance in terms of generality. Following the most popular and effective paradigm in this field, which incorporates a neural radiance field (NeRF) into the generator of a generative adversarial network (GAN), we build a well-structured codebase, dubbed Carver, through modularizing the generation process. Such a design allows researchers to develop and replace each module independently, and hence offers an opportunity to fairly compare various approaches and recognize their contributions from the module perspective. The reproduction of a range of cutting-edge algorithms demonstrates the availability of our modularized codebase. We also perform a variety of in-depth analyses, such as the comparison across different types of point feature, the necessity of the tailing upsampler in the generator, the reliance on the camera pose prior, etc., which deepen our understanding of existing methods and point out some further directions of the research work. We release code and models here to facilitate the development and evaluation of this field. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Generative adversarial networks

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An Efficient Hybrid Technique for Automatic License Plate Recognitions 1

An Efficient Hybrid Technique for Automatic License Plate Re...

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1st International conference on Recent Developments in Electronics and Communication systems, RDECS 2022

作者： Devi, Terli V. Satyanarayana, V. Singh, Mahesh K. Department of ECE Aditya Engineering College Surampalem India Department of ECE Aditya Engineering College A.P. Surampalem India

ISBN: (纸本)9781643683607

Automatic License Plate Recognition (ALPR) is an embedded real-time technology that automatically recognizes a vehicle's license plate. There are numerous uses, ranging from complex security to shared spaces, parking to acceleration. The ALPR is used to control the traffic and to take hold for those who violate the traffic rules. Automatic recognition of vehicle license plate numbers has become critical in our daily lives due to the rapid expansion of autos and transportation systems, which makes it difficult for the general public to keep up with and monitor. A license plate identification system uses image processing theory and soft computing technology to identify cars based on their number plates. Using fewer and simpler algorithms can reduce both calculation time and execution time needed by most number plate localization approaches. This is because most of these techniques require several operations. License plate recognition is the most used approach in traffic-related applications. Various approaches, tactics, and algorithms have been developed for the identification and recognition of license plates. In the context of Indian license plate identification, only a few research have looked at the topic. Character segmentation and character recognition are two of the three key steps in identifying a license plate. © 2023 The authors and IOS Press.

关键词： image segmentation

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Optimal Target Interception using Foldable Quadrotor with Rotating Arms

Optimal Target Interception using Foldable Quadrotor with Ro...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Zakaria Mehal Yasser Bouzid Mohammed Guiatni Saddam Hocine Derrouaoui Complex Systems Control and Simulators (CSCS) Laboratory Ecole Militaire Polytechnique Bordj el Bahri Algeria Ecole Superieure Ali Chabati Algires Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

This article addresses a critical problem in the field of target interception using a new foldable quadrotor with rotating arms. The primary challenge is maximizing linear acceleration to enhance the quadrotor’s ability to intercept targets effectively. To address this challenge, we introduce a novel optimization framework. Our study employs two distinct optimization algorithms, namely the genetic algorithms and the whales optimization algorithm, to ascertain the maximum attainable linear acceleration for the foldable quadrotor. The results obtained are confirmed through the use of our innovative foldable quadrotor.

关键词： Heuristic algorithms Signal processing algorithms Life estimation Signal processing Whale optimization algorithms Optimization Robots

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Unveiling the Depths: A Comprehensive Analysis of Natural Language processing and Generative Adversarial Neural Networks for Text Generation Models in Deep Learning 1

Unveiling the Depths: A Comprehensive Analysis of Natural La...

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1st International conference on Circuits, Power, and Intelligent systems, CCPIS 2023

作者： Agarwal, Rashi Agarwal, Himanshu Pandey, Senam Gehu India

ISBN: (纸本)9798350315769

Deep Learning comes under Machine Learning that accomplishes more power and flexibility by learning to present different concepts or relations of real world to simpler concepts. We use Deep learning fundaments in this paper because it has massive amount of data that helps in innovations. We include these neural networks of deep learning because it comes with a high accuracy rate with lower computations. Natural processing Language (NLP) and Generative Adversarial Network (GAN) are the methods that individually contribute to the text generation method. Although these are two different technologies giving the output for some common motive where Text generation plays a very important role in smart translations and dialogue systems. This review paper presents a model centered around text generation. This is done because combinedly we want to present what can be different approaches to look at a model like this. To solve the problem of unnecessarily used large texts, unsatisfactory feedback, NLP is used for text generation, GANN is used for text generation model, image generation etc. Finally, this is done to reduce time complexities, speed, efficiency in process because this is noticed that learning for a problem plays a vital role in education to enhance features. © 2023 IEEE.

关键词： Learning algorithms

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