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U Can't (re)Touch This - A Deep learning Approach for Detecting image Retouching 21st

U Can't (re)Touch This - A Deep Learning Approach for Detect...

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21st international conference on image Analysis and processing (ICIAP)

作者： Aumayr, Daniel Schottle, Pascal Management Ctr Innsbruck Digital Business & Software Engn Innsbruck Austria

ISBN: (纸本)9783031064302;9783031064296

Mobile applications make it easier than ever to edit images and share these images on the Internet. Even if this image manipulation is not a criminal offense in most cases, retouched images can have a number of negative effects. The goal of this work is to provide a solution for an automatic recognition of image retouching. For this, we adapt a well known convolutional neural network (CNN) from [5] to the domain of RGB images and create a data set to train it. Specifically, we process 1 000 images with nine different filters using Snapseed, an image editing app. Then, we use these 10 000 images in two different experiments to train the adapted CNN. The first experiment compares each single filter with the original images, while the second experiment tries to distinguish all ten classes at once. Overall, the first experiment achieves an accuracy of over 99% for most classes, and the second experiments a total accuracy of roughly 88%. Finally, we analyze, why some of the classes have lower accuracies.

关键词： image forensics image retouching Deep learning Convolutional neural network

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Bangla and Nagri Language Detection and Classification Using Deep learning Methods on Text Document image 1

Bangla and Nagri Language Detection and Classification Using...

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1st IEEE international conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Abu Naser Mojumder, Md. Islam, Ashraful Islam, Tanzidul Das, Showman Chowdhury, Zakaria Rahman, Ashfakur Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350348057

The development of an automated document processing system and a multilingual OCR relies heavily on accurate language recognition and classification. In the prior many researchers did a lot of research on several renowned languages. As a result of their rarity, complexity, and difficulty of use, Bangla subcontinent languages, such as Sylhoti Nagri, Chatgaya, etc., have received comparatively less attention from researchers than more widely used languages. In this research, we utilized deep learning models in an effort to categorize and detect Bangla and Sylhoti Nagri languages. Here, YOLOv5 and YOLOV7 were used to recognize Bangla and Nagri text from scanned document images, while VGG16 and ResNET50 were used to classify the text. VGG16 outperformed ResNET50 in classification with an accuracy of 97.23% and 95.89% in train and test respectively, while the YOLOv5 model outperformed the YOLOv7 model in terms of detection accuracy (99.41% vs. 94.12%). © 2023 IEEE.

关键词： Deep learning

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A Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth 3rd

A Systematic Review: How Computer Vision is Transforming Agr...

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3rd international conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： Computer Vision Artificial Intelligence Economic Growth Agriculture Management Agricultural Automation machine learning

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Smart Traffic Police Helmet: Using image processing and IoT 1st

Smart Traffic Police Helmet: Using Image Processing and IoT

引用

1st EAI international conference on Intelligent Systems and machine learning, ICISML 2022

作者： Hafeez, Shoaib Karnati, Ramesh Velpuru, Muni Sekhar Hyderabad India Hyderabad India

ISBN: (纸本)9783031350771

Traffic is a major issue in any major city, and Hyderabad is no different. The Hyderabad traffic police commission is doing its best to control the traffic in the country with 2nd largest population in extreme conditions like heat and pollution. To help the traffic commission, who are controlling the traffic every day under the scorching sun, we have built a multi-purpose helmet that can help the traffic police while on duty. The helmet is installed with a camera and raspberry pi. Instead of using a handheld smartphone to take the picture of vehicles, the helmet snaps a high-resolution picture using a button. These pictures are sent to the AWS cloud using a pre-configured raspberry pi. These images are processed in the cloud where information like the number plate, color, and vehicle types is extracted. All these images can now be filtered according to date and time and viewed on a user-friendly interface. © 2023, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image processing

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Towards the Creation of AI-powered Queries Using Transfer learning on NLP Model - The THESPIAN-NER Experience 21st

Towards the Creation of AI-powered Queries Using Transfer Le...

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21st international conference on image Analysis and processing (ICIAP)

作者： Bombini, Alessandro Castelli, Lisa Felicetti, Achille Niccolucci, Franco Reccia, Anna Taccetti, Francesco Ist Nazl Fis Nucl Florence Sect Via Bruno Rossi 1 Florence Italy PIN Prato Italy

ISBN: (纸本)9783031133244;9783031133237

Tools for HEritage Science processing, Integration, and ANalysis (THESPIAN) is a cloud system that offers multiple web services to the researchers of INFN-CHNet, from storing their raw data to reusing them by following the FAIR principles for establishing integration and interoperability among shared information. The injection in the CHNet cloud database of data and metadata (the latter modelled on a CIDOC-based ontology called CRMhs [20]) is performed by using the cloud service THESPIAN-Mask. THESPIAN-NER is a tool based on a deep neural network for Named Entity recognition (NER), which will ease the data extraction from the database, enabling users to *** *** files and obtain named entities and keywords to be fetched in the metadata entries of the database. The neural network, on which THESPIAN-NER relies, is based on a set of open-source NLP models;transfer learning was employed to customise the Named Entity recognition output of the models to match the CRMhs ontology properties. The service is now available in alpha version to researchers on the CHNet cloud.

关键词： AI in natural language processing and cultural heritage applications Named entity recognition FAIR data management Cloud services

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Research on the Architecture of Electronic Target Intelligent processing System based on Artificial Intelligence 1

Research on the Architecture of Electronic Target Intelligen...

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1st international conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Zhao, Minyi Xi'an University of Science and Technology Xi'an710054 China

ISBN: (纸本)9781665497107

With the massive increase of similar electronic devices, the requirements for the capacity, timeliness and accuracy of electronic target intelligence processing are also getting higher and higher. The defects existing in the existing processing flow are analyzed, based on the intelligent processing technology. The electronic target intelligent processing architecture is proposed, and the main functions, processes and technical performance of the main intelligent processing technologies are analyzed. It is verified that the intelligent processing method based on the deep learning neural network algorithm has better target shape information. Good multi-target detection and recognition ability. At the same time, under the condition of the ground test hardware platform, the detection time of a single frame image is 0.7s, and the average detection and recognition accuracy rate can reach 91.89%. © 2022 IEEE.

关键词： Computer architecture

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Design and Development of Artificial Intelligence based Real-Time Traffic Sign Identification Scheme using Novel learning strategy 1

Design and Development of Artificial Intelligence based Real...

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1st IEEE international conference on Emerging Research in Computational Science, ICERCS 2023

作者： Tamilselvi, M. Iyswariya, A. Thapa, K. Naresh Kumar Princy, B. Anni Vinithra Banu, T. Pandi, V. Samuthira Saveetha Institute of Medical and Technical Sciences School of Engineering Department of Ece Tamil Nadu Chennai India R.M.K. Engineering College Department of Ece Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of Ece Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350359756

The identification of traffic signs, also known as traffic sign recognition or TSR, is one of the most important aspects of research that has to be done before fully autonomous driving systems can be developed. When it comes to the data that are taken in, autonomous driving systems have very specific criteria. The data must be trustworthy and the system must be able to do situational analysis in real time;there is no room for complicated transformations or complex image processing techniques. This endeavour is made considerably more challenging by the fact that it takes place in an urban environment, which features a greater number of relocating or distracting objects (such as traffic signs, ads, parked cars, people, and so on). In spite of the abundance of approaches that have been published, solutions are often only tested on highways, in rural areas, or at very slow speeds. The development of traffic sign detection scheme, which involves the creation of unmanned vehicles, has moved forward in a consistent manner. Finding a way to eliminate interference from various environmental circumstances is one of the most essential technological difficulties. This is necessary in order to ensure that traffic sign recognition and identification can be carried out in an accurate and time-effective manner. However, because of the limitations of colour and edge detection, conventional visual object recognition is almost entirely dependent on the extraction of visual features. In this paper, a novel traffic sign identification is introduced called learning strategy for Traffic Sign Detection (LstSD), in which it follows the Artificial Intelligence (AI) logic to improve the resulting metric to perform accurate identification of road side traffic signs in an intelligent manner. This learning strategy LstSD is cross-validated with the conventional learning method called Convolutional Neural Network (CNN) to prove the efficiency of the proposed scheme in clear manner. © 2023 IEEE.

关键词： Autonomous vehicles

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Design and Implementation of 4-bit High Speed Array Multiplier for image Coding 1st

Design and Implementation of 4-bit High Speed Array Multipli...

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1st EAI international conference on Intelligent Systems and machine learning, ICISML 2022

作者： Aylapogu, Pramod Kumar Badita, Kalivaraprasad Geetha, P. Sama, Namratha Department of ECE Vardhaman College of Engineering Hyderabad India Guntur India School of Engineering and Technology Mohan Babu University Tirupati India

ISBN: (纸本)9783031350771

Multipliers are the utmost commonly used elements in today’s digital electronics. In digital signal processing systems, hardware multiplication is critical for obtaining high data throughput. Based on the increasing applications of electronic devices, various types of multipliers have emerged. Array Multiplier is the most fundamental multiplier of all. The main goal of our project is to create an optimized and fast 4-bit array multiplier. Parallel Array Multipliers are used in DSP applications to do multiplication at high speeds while meeting performance criteria. The simulations are run on the Xilinx 14.7 ISE Design Suite. © 2023, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image coding

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Alzheimer's disease detection through Deep learning techniques: A study 1

Alzheimer's disease detection through Deep learning techniqu...

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1st international conference on Computational Science and Technology, ICCst 2022

作者： Thamizharasi, M. Lakshmi, M. Srm Institute of Science and Technology Computer Science and Engineering Chennai India Srm Institute of Science and Technology Data Science and Business System Chennai India

ISBN: (纸本)9781665476553

Alzheimer's disease is one of the commonly occurring disease in which the common cause of dementia called as memory loss will happen resiliently and damage the brain activity. It also produces cognitive abilities and serious impact of interference with the day today life. Disease contains 60% to 80% of dementia cases in the early stages are predicted and further actions are initiated to alert the patient behaviour from abnormal activity. The direct impact of Alzheimer's disease (AD) interfere with the regular activity and make complexity in making decisions, thinking capability, problem solving and speaking. The evaluation of artificial intelligence (AI) created numerous ways of analysing strategies, helpful for making the early prediction. In spite of image processing technology, machine learning models are created to analyse the disease features, symptoms, Chronic records to perform AD detection. The role of deep learning algorithm incorporated with image processing, deep feature extraction, and deep feature fusion enhances the scope of research in AD analysis. Further the benefit of deep learning algorithm to provide search detection mechanism. The presented study discusses various criteria of AD detection and tabulated the findings. © 2022 IEEE.

关键词： learning algorithms

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Ancient coin classification based on recent trends of deep learning 1

Ancient coin classification based on recent trends of deep l...

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Manzoor, Sehrish Ali, Nouman Raees, Muhammad Khan, Khan Awais Ayub, Muhammad Usama Ahmed, Afzal AJK Mirpur10250 Pakistan

Automatic ancient coin classification based on pattern recognition is an open research problem. Ancient coins have significant value in cultural heritage and the originality of coins is imperative for their significance. Ancient coin categorization is complex due to the variety in coin designs, purposes, and symbol interpretability thus, drawing a growing amount of research. Here, we present a framework for automatic recognition performed on ancient roman coins. The framework uses a hierarchical knowledge structure of coins complemented with a CNN-based category classifier. We focused on roman republican image datasets for coin identification using CNN Alex-net architecture. recognition comprises feature-based identification of comparable image matching. Points of interest are detected and described by local characteristics as their appearance. Results are evaluated using the accuracy and precision metrics on Roman Republican Coin Dataset (17546 images) and Roman Coins Dataset (180 images). Our model outperforms various deep learning methods on this dataset, with a classification ac-curacy of more than 96%. © 2022 Copyright for this paper by its authors.

关键词： image classification

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