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

共 13657 条记录，以下是731-740 订阅

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image Recognition and Enhancement Using Multi-Scale Retinex and Histogram Equalization

Image Recognition and Enhancement Using Multi-Scale Retinex ...

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2023 IEEE International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Senthil Kumar, R. Rajalakhmi, B. Geethika, Palle Indrani, C. Kolalapudi, Ramya Swathi CSE NHCE Bangalore India

ISBN: (纸本)9781665462631

Nowadays, the need for a safe and at ease device is favoured by using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an aerial surveillance machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output © 2023 IEEE.

关键词： image enhancement

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A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

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2023 International conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Tahmid, Marjana Alam, Samiul Akram, Kalim Asif Ashrafi, Kazi Muhammad East Delta University Dept. of Cse Chittagong Bangladesh East Delta University Dept. of Eee Chittagong Bangladesh Ulm University Dept. of Cse Ulm Germany

ISBN: (纸本)9798350322972

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and Computer Vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages"to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions. © 2023 IEEE.

关键词： Generative adversarial networks

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6th International conference on Smart Computing and Informatics, SCI 2024

6th International Conference on Smart Computing and Informat...

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6th International conference on Smart Computing and Informatics, SCI 2024

ISBN: (纸本)9789819619801

The proceedings contain 46 papers. The special focus in this conference is on Smart Computing and Informatics. The topics include: Resilient Domain Authentication Framework for Enhancing Digital Identity Security;traffic Sign Detection with Pattern Recognition Techniques Using image processing;exploring Advanced Techniques in Natural Language processing and Machine Learning for In-depth Analysis of Insurance Claims;Network Intrusion Detection with SMOTE-ENN and Deep Learning Techniques;leveraging Transfer Learning to Enhance Location Accuracy in Mapping Services: A Case Study of Google Maps;assessment of Enhanced Email Spam Detection System Through Machine Learning algorithms;machine Learning Methods for Predicting Traffic Congestion Forecasting;hybridization of Computational Intelligence Algorithm for Scheduling of Tasks and Balancing of Load in Cloud Network;MDSV: Mobs Detection by Enhanced Fused Feature Base Deep Neural Network from Surveillance Camera;ioT-Based Solution for Enhanced Tracking of Individuals Living with Dementia;a Novel Task Scheduling Algorithm in Heterogeneous Multi-cloud Environment;Evaluating the Integration and Usage of AI in Higher Education;evaluating the Connectional Benefits of Artificial Intelligence in the Digital Classroom;Influence of AI as an Aspect of Modern Education Era in Present World;Hilbert–Huang Transform Framework-Based Email and SMS Spam Detection;the Advancement and Utilization of Artificial Intelligence and Machine Learning in the Financial Industry and Its Impact on Macro and Microeconomics;analysis on the Cutting-Edge Approach to Assess Artificial Intelligence’s Educational Consequences in Contemporary Studies;data Analytics in Sales and Marketing: A Comprehensive Methodology for Business Analysts;Wireless Energy Transfer for UAV (Drone) Using Machine Learning.

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A Comprehensive Review of Low-light image Enhancement Techniques

A Comprehensive Review of Low-light Image Enhancement Techni...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Tian Gao Peilin Tao AMS PLA Systems Engineering Institute Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Enhancing low-light images is a crucial research topic in computer vision, aiming at revealing image details hidden in the darkness and thus recovering images with normal lighting and color. However, there is a large variety of solutions proposed for this problem, and it is not a simple problem for researchers to systematically understand all types of solutions. This paper provides a comprehensive review of various techniques for low-light image enhancement (LLIE), including algorithms, datasets, evaluation metrics, and more. First, we categorize these methods into two broad directions based on the principles of the algorithms and provide a detailed introduction to each. These two directions are further categorized into four subcategories based on their learning approaches. Then we introduce the principles, characteristics, and current dilemmas and challenges of various methods in detail based on each classification. Finally, we discuss the more promising research directions in the future in light of the latest research progress.

关键词： Learning systems Deep learning Measurement Computer vision Reviews image color analysis Lighting Classification algorithms image enhancement Faces

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ShuffleMixer: An Efficient ConvNet for image Super-Resolution 36

ShuffleMixer: An Efficient ConvNet for Image Super-Resolutio...

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36th conference on Neural Information processing systems (NeurIPS)

作者： Sun, Long Pan, Jinshan Tang, Jinhui Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781713871088

Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details. To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features. Experimental results demonstrate that the proposed ShuffleMixer is about 3x smaller than the state-of-the-art efficient SR methods, e.g. CARN, in terms of model parameters and FLOPs while achieving competitive performance. The code is available at https://***/sunny2109/ShuffleMixer.

关键词： Convolution

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Auto-Detection of Handwritten Text Documents using Improved image processing Technique 15

Auto-Detection of Handwritten Text Documents using Improved ...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srinivasan, M. Radha, K. Priya, V. Basha, Shaik Mahaboob Ravi, S. Bannari Amman Institute of Technology Department of Eee Sathyamangalam India Dr.Mahalingam College of Engineering and Technology Department of Cse Coimbatore Pollachi India Department of Microbiology TamilNadu Chennai India School of Engineering Department of Ece Andhra Pradesh Tirupati India Saveetha University Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798350370249

Automobile detection of handwritten text files is vital in document control systems. It includes the process of automatically figuring out and extracting handwritten text from pictures and converting it into editable virtual text. This method is challenging due to the variations in handwriting styles and the complexity of handwritten textual content reputation. Current methods for auto-detection depend upon conventional picture processing strategies, including thresholding, segmentation, and characteristic extraction, which need to be more robust to handle various handwriting patterns. A stepped-forward photograph processing method for automobile detection of handwritten textual content documents is proposed to deal with this issue. The technique uses deep mastering algorithms, including convolutional neural networks (CNNs), to robotically research the features of handwritten textual content and classify it appropriately. The proposed technique additionally employs facts augmentation techniques to boost the dataset's variety, making it more robust for variations in handwriting styles. Experimental consequences on an extensive handwritten textual content file dataset show that the proposed technique outperforms traditional techniques, accomplishing a better accuracy fee. The advantages of this method encompass its adaptability to one-of-a-kind handwriting styles, its velocity, and its potential to deal with handwritten textual content in numerous languages © 2024 IEEE.

关键词： Appropriately Augmentation Consequences Experimental Handwritten Mastering

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FPGA Implementation of Various Division algorithms for image processing Applications - A Comparative Analysis

FPGA Implementation of Various Division Algorithms for Image...

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Signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： Jayanthi B L S Kumar A Someshwaran M Sandya Nandhini Bharadwaj Department of ECE National Institute of Technology Puducherry Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Division is one of the most commonly sort after algorithm for performing image processing operations such as normalization, filtering, enhancement, deconvolution etc. Hence, the design of efficient division algorithm is highly essential in order to obtain a better image processing algorithm The first and foremost step in the implementation of any algorithm in hardware is to identify the number systems used to represent the inputs and outputs. The inputs, outputs and complexity of the algorithm varies based on the number system used. This paper proposes a recursive subtraction based fixed-point division algorithm. This work also provides a comparative analysis on various aspects such as area, power, delay and throughput, on comparing the proposed recursive subtraction based fixed-point division algorithm with the existing floating-point division, restoring integer division and non-restoring integer division algorithms and operator based fixed-point division algorithm.

关键词： Deconvolution Filtering Signal processing algorithms Signal processing Throughput image restoration Delays

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Monitoring Method and Application of Icing Thickness and Icing Type of Distribution Network Overhead Line Based on A Hydrophobic Marker and image Defogging 2

Monitoring Method and Application of Icing Thickness and Ici...

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IEEE 2nd International conference on Power Science and Technology (ICPST)

作者： He, Xingping Yang, Jingsi Zeng, Hanye Li, Qingdong Liu, Chengcheng Zhou, Jiye Zhaotong Power Supply Bur Yunnan Power Grid Co Lt China Southern Power Grid Zhaotong Yunnan Peoples R China

ISBN: (纸本)9798350349047;9798350349030

This paper presents a monitoring method and application of icing thickness (ITs) and icing type (ITe) of distribution network overhead line (DNOL) based on a hydrophobic marker (HM) and image defogging (ID). Firstly, a HM tailored specifically for application in DNOL is devised;Subsequently, image processing (IP) techniques are employed to process the acquired images. These techniques include ID, Canny edge detection to identify edges, Imfill Operator for fill holes (or gaps) within binary images, and Bwperim Operator for precise edge extraction, the application of these operator collectively yields a clear and defined edge image. Finally, using the distinct characteristics of various ITe within the RGB color space and utilizing algorithms, the ITe and ITs are accurately calculated. Consequently, relevant information pertaining to line icing of DNOL is obtained, enhancing the accuracy and reliability of line icing monitoring. This, in turn, bolsters the safety and stability of the power system. The results of this study are anticipated to establish a solid theoretical and practical foundation for icing monitoring on DNOL. Furthermore, they are expected to offer substantial support for ensuring the safe and stable operation of power systems, thereby enhancing their overall resilience and reliability.

关键词： distribution network overhead line hydrophobic marker image defogging icing thickness icing type

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End-to-End Single image Super-Resolution Based on Convolutional Neural Networks 26

End-to-End Single Image Super-Resolution Based on Convolutio...

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26th International conference on System Theory, Control and Computing (ICSTCC)

作者： Ferariu, Lavinia Beti, Iosif-Alin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

Single image super-resolution algorithms aim to increase the resolution of an input image without deteriorating its visual perception. With a strong ability to understand the structure of an image, convolutional neural networks (CNNs) are successfully applied to this problem. Previous studies have shown that human perception is mainly influenced by variations in luminance. In this regard, this paper introduces two CNNs that operate only on the luminance channel, at low computational costs. Each model provides an end-to-end mapping between a low-resolution (LR) and a high-resolution (HR) map. Because upsampling is integrated into CNN, the design allows the control of HR image quality. In addition, the neural architectures can be configured with many layers operating with small LR feature maps, to provide fast run-time image processing. The approach is exemplified in two cases: generate the HR map or a residual map for the luminance channel;the residual map should be added to the map upsampled by interpolation. Besides having improved time performance, the two models can produce HR images with high NIQE scores, as shown experimentally.

关键词： image processing super-resolution convolutional neural networks

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Plant Disease Detection by means of K-Means Clustering and HSV Algorithm

Plant Disease Detection by means of K-Means Clustering and H...

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

作者： Anirudh Reddy, R. Sai Prasanna, K. Mani Teja, B. Deepak Reddy, N. Pranay Sai Reddy, N. Shiva, N. B v Raju Institute of Technology Department of Ece TelanganaMedak Dt Narsapur India

ISBN: (纸本)9798350333602

The productivity of agriculture determines the economy of a country. To boost agricultural productivity and improve product quality, plant diseases must be identified. It is seen that the agricultural production is rapidly growing and various techniques are being used for it. One of the emerging technologies is fogponics. In this technology, plants are grown using fog which is directly exposed to roots instead of water. This methodology has a limitation of being infected with diseases due to a lack of nutrition. Plant leaf patterns can be observed visually to diagnose disease using traditional methods;however, they require the use of human resources. image classification is a technique to detect the diseases of plants that are already trained to model. If the disease is detected, then acknowledgment has been sent to the user that the plant is affected by the disease. This dataset must be uploaded to carry out the software implementation, and specific operations must be carried out on it to obtain the findings, namely whether the disease is detected. Regarding real-time applications, any model (Raspberry Pi) is used to hold substantial amounts of data. Devoid of any datasets for model training, a few different techniques are applied to identify anomalous leaf conditions. A color feature of the leaf area is used in image-processing algorithms to detect plant infections and diseases. © 2023 IEEE.

关键词： image classification

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