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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是301-310 订阅

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Manual and Automatic Feature Engineering in Digital image Forgery Detection Algorithms: Survey 19

Manual and Automatic Feature Engineering in Digital Image Fo...

引用

19th ieee international Colloquium on Signal processing and Its applications, CSPA 2023

作者： Mashaan, Wasen Fahad Ahmed, Ismail Taha University of Anbar College of Computer Sciences and Information Technology Anbar Iraq

ISBN: (纸本)9781665476928

Digital image forgery (DIF) detection has gained importance in recent years. Because of the availability of lowcost, high-resolution digital cameras, issues related to confirming the authenticity of digital images have been highlighted. The term '' manipulation'' refers to the changing of any image's objects or contents without giving any indication that anything has changed. In general, image splicing is one of the most popular techniques used to fake images. image splicing is the process of adding or hiding things by copying portions of one image and pasting them elsewhere in the same image or into a different image. There have been several image splicing forgery detection surveys throughout the past few decades. However, neither the image forgery detection algorithms-based manual engineering features nor the image forgery detection algorithms-based automatic engineering features are covered by these surveys. This research provided a comparative analysis of image splicing by gathering several techniques and classifying them according to the features they employed, based on manual engineering features and automatic engineering features. © 2023 ieee.

关键词： Feature extraction

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Recent Trends of Granular Computing Approaches for image processing in Medical Imaging 7

Recent Trends of Granular Computing Approaches for Image Pro...

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7th international conference on image Information processing, ICIIP 2023

作者： Shambhu, Shankar Das, Prasenjit Bajaj, Karan Kumar, Mukesh Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Uic Chandigarh University Department of Cse Punjab India Lovely Professional University School of Computer Applications Punjab Phagwara India

ISBN: (纸本)9798350371406

The objective of this study is to provide a comprehensive analysis of the current developments in the application of granular computing techniques in the field of biomedical image processing. Granular computing is a methodological approach that utilises granules as a means to effectively tackle a diverse array of diseases. The granules mentioned below represent the elemental constituents of granular computing. This paper offers an academic exploration of the application of granular computing in addressing image processing problems. It encompasses a comprehensive examination of the parameters associated with granular computing, the significance of granular computing in the field of image processing, a diverse range of image processing techniques, and the contributions made by granular computing in overcoming challenges encountered in medical image processing. Furthermore, this study examines the accomplishments of granular computing, performs a comprehensive review of the existing literature on granular computing and image processing methodologies, emphasises the benefits of employing granular computing in contrast to alternative image processing approaches, and ultimately investigates the various health concerns and obstacles associated with the utilisation of granular computing in the context of image processing. © 2023 ieee.

关键词： Medical imaging

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Investigating the Efficiency of Digital image Sampling in Serial image processing

Investigating the Efficiency of Digital Image Sampling in Se...

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2023 ieee international conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Saravanan Saini, Rajat Vijayaraghavan, P. Patil, Swati B. Prabha, Savita Singh, Vaishali Department of Animation and Virtual Reality Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350329773

The reason of this look at is to observe the efficiency of virtual photograph sampling in serial image processing. This investigation covers the impact of sampling strategies on image great, the efficiency of facts compression, and the computational complexity of algorithms used in serial photograph processing. The studies is carried out the usage of numerous open source libraries and methods that are suitable for studying digital photos and the results of making use of virtual photograph sampling strategies. Several experiments are designed to degree the impact of different sampling techniques on serial photograph processing, including easy pixel sampling, down sampling, and advanced techniques including adaptive non-uniform sampling and virtual filtering. Additionally, the appropriateness of the contemporary state of current sampling algorithms is assessed. The outcomes of this look at are anticipated to provide beneficial insights for designers of serial image processing algorithms and to pick out ability areas for improvement. Moreover, this paintings is expected to provide hints for optimizing the effectiveness of virtual image sampling techniques in terms of photograph quality, computational complexity, and statistics compression. © 2023 ieee.

关键词： Computational complexity

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A MULTI-PURPOSE FOOD RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

A MULTI-PURPOSE FOOD RECOGNITION SYSTEM USING CONVOLUTIONAL ...

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2023 ieee international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Baby Shamini, P. Nivetha, R. Rituja Yadav, R. Hemala, R. Department of Computer Science andEngineering R.M.K Engineering College India

ISBN: (纸本)9798350305708

The objective of this paper is to build the prototype of a personalized multi-purpose food recognition system for the purpose of recognizing the type of food, tracking the caloric content and giving the caloric content of the food, and provide the record of the user's eating habits. Convolution Neural Network and image processing techniques have been found increasingly useful in the food industry, particularly for food detection applications. Research in this area suggests of using CNN to improve product quality. Here, We are going to use CNN based automatic food detection system which will be detecting the various food and is designed along with multiple features. This type of approach enhances the user experience of finding and analyzing the type of food that better suits their taste. © 2023 ieee.

关键词： CNN Deep Learning Food recognition system Foodindustry image processing

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Prediction and processing of Environment Geography images Using Deep Learning Techniques

Prediction and Processing of Environment Geography Images Us...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Rahunathan, L. Vijayakumar, P. Sivabalaselvamani, D. Nekhaa, T. Nekitha, M. Yaswanth, Paruchuri Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India

ISBN: (纸本)9798350313987

image processing and prediction have grown in popularity in recent years. The term 'image processing' refers to the modification and analysis of digital images using various techniques and approaches. This research study contains four types of images: land, water, cloud, and green area. In addition, models such as CNN and imageNet are compared. Further, the images are used as input, and deep learning techniques are used to analyze various images and predict the results for both dataset and real-time images. © 2023 ieee.

关键词： Convolution Neural Networks Deep Learning image processing and prediction imageNet

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Violent Speech Detection in Educational Environments 20

Violent Speech Detection in Educational Environments

引用

20th ACS/ieee international conference on computer Systems and applications (AICCSA)

作者： Chnini, Manel Fredj, Nissaf BenSaid, Fatma Kacem, Yessine Hadj Univ Gafsa Fac Sci Gafsa Gafsa Tunisia Univ Sfax CES Lab Sfax Tunisia Univ Sfax ReGIM Lab Sfax Tunisia

ISBN: (纸本)9798350319439

Nowadays, social networks allow people to interact by exchanging messages, publishing public or private photos or videos. But sometimes they become a space of toxic language to criticise, insult, hate, and attack. In this context, researchers promote a strong intention to study, analyse and detect hate speech. By automating its detection, the spread of anxiety and the rise of hateful content can be limited, especially among children in the online schools. However, with the absence of online database of vulgar English speech by Students in schools, detecting violent speech becomes a difficult task. In this paper, we propose a new dataset-based framework for the detection of students violent speech using natural language processing and learning techniques. This focuses on a proposed "Students's Violent Speech (SVS)" dataset with 7056 tagged tweets. The dataset is collected and pre-processed to be analyzed to show the performance and accuracy of the proposed model.

关键词： Violent speech Students Detection Dataset collection Natural language processing

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Research and Application of Content-based image Hash Retrieval Algorithm 21

Research and Application of Content-based Image Hash Retriev...

引用

21st ieee/ACIS international conference on Software Engineering Research, Management and applications, SERA 2023

作者： Luo, Xinqun Tan, Xingyu Communication University of China School of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350345889

In order to store and manage a large amount of image information, we built a database retrieval system and studied and applied content-based image retrieval algorithms. We found that the difference hashing algorithm performs better than the perceptual hashing algorithm, the mean hashing algorithm and the histogram feature extraction algorithm in the scenario of handling a large number of images. Finally, we applied the difference-value hashing algorithm to a database retrieval system for a film festival to achieve in storing and retrieving a large amount of film materials. © 2023 ieee.

关键词： Search engines

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PERCEPTION-ORIENTED OMNIDIRECTIONAL image SUPER-RESOLUTION BASED ON TRANSFORMER NETWORK 30

PERCEPTION-ORIENTED OMNIDIRECTIONAL IMAGE SUPER-RESOLUTION B...

引用

30th ieee international conference on image processing (ICIP)

作者： An, Hongyu Zhang, Xinfeng Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728198354

Omnidirectional image (ODI) super-resolution (SR) is an important technique in augmented reality and virtual reality applications to address the low-resolution problem caused by limitations in capturing devices or bandwidth. The ODI projection distortion makes it challenging to apply existing SR methods. In this paper, we propose an ODI SR method by leveraging the characteristics of ODIs and human visual characteristics. Specifically, we firstly design a perception-orientated adaptive loss function by jointly utilizing saliency map and latitude map. In our proposed ODI-SR network, we introduce an attention module to aggregate multi-scale information and leverage spherical convolution to adapt to the spheric format of ODIs. Furthermore, we design a data augmentation strategy for ODIs according to viewpoint distribution to further improve the visual quality of SR images. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance according to both qualitative and quantitative evaluations.

关键词： Omnidirectional image super-resolution perceptual quality

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image Spam detection in E-mails using Grasshoppers optimization technique

Image Spam detection in E-mails using Grasshoppers optimizat...

引用

2023 international conference on computer, Electronics and Electrical Engineering and their applications, IC2E3 2023

作者： Mallampati, Deepika Hegde, Nagaratna P Osmania University Dept. of CSE Telangana Hyderabad India Vasavi College of Engineering Dept of Cse Telangana Hyderabad India

ISBN: (纸本)9798350338003

one of the most frequent types of digital communication is email. Spam is unsolicited mass mail, whereas image spam includes spam text incorporated in images. This type of spam threatens email communications because spammers use it to avoid text spam filters. This paper provides a support vector machine (SVM) based classification algorithm and a biology optimization strategy to categorize emails as spam or ham. In this study, we examine image spam detection strategies using different combinations of image processing and machine learning algorithms. The grasshopper optimization algorithm (GOA) is an approach that statistically models and simulates the behavior of natural locust swarms. We investigate and analyze the performance of Grasshopper optimizations using several evaluation measures, such as accuracy, precision, recall, f1-score, and convergence rate to the global optimum solution. © 2023 ieee.

关键词： Support vector machines

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Performance Comparison of Deep Learning Models for computer Generated image Detection 5

Performance Comparison of Deep Learning Models for Computer ...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Sychandran, C.S. Shreelekshmi, R. APJ Abdul Kalam Technological University Department of Computer Science and Engineering College of Engineering Trivandrum Kerala Thiruvananthapuram695016 India APJ Abdul Kalam Technological University Department of Computer Applications College of Engineering Trivandrum Kerala Thiruvananthapuram695016 India

ISBN: (纸本)9798350334128

With the advancement in digital image processing technology, there is a huge proliferation of computer-generated images. They are widely used in interactive games, film industry and cartoons. computer-generated images are photo-realistic because they are created using complex algorithms and high-end graphics processing power to accurately mimic real-world objects and environments' lighting, textures, and colors. Since computer-generated images are more realistic, even human eyes can barely distinguish them. So verifying image authenticity and differentiating computer-generated images from photographic images are inevitable. This paper investigates the performance of different deep learning models for detecting computer-generated images. We experiment on three datasets: DSTok, Columbia PRCG and Rahmouni. Experimental results show that the hybrid Xception ensemble outperforms the state-of-the-art methods and achieves the highest accuracy, precision, recall, F1 score and specificity on three benchmark datasets. © 2023 ieee.

关键词： Deep learning

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