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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. Computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： image segmentation

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Interactive Sign Language Learning System using Deep Learning 2

Interactive Sign Language Learning System using Deep Learnin...

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2nd International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

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

ISBN: (纸本)9798350368413

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. © 2024 IEEE.

关键词： Convolutional neural networks

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image Understanding Through visual Question Answering: A Review from Past Research 23th

Image Understanding Through Visual Question Answering: A Rev...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Yanda, Nagamani Tagore Babu, J. Aswin Kumar, K. Taraka Rama Rao, M. Ranjith varma, K.v. Rahul Babu, N. GMR Institute of Technology Rajam532127 India

ISBN: (纸本)9783031648465

visual Question Answering (vQA) lies at the crossroads of computer vision, natural language processing, and deep learning, captivating researchers across various AI domains. This dynamic field involves processing an image alongside a corresponding textual question, generating, or selecting an answer from provided options. The past five years have witnessed substantial advancements in vQA and visual reasoning, fueled by deep learning and extensive annotated datasets. This study presents a comprehensive literature review, delving into the current state-of-the-art from four perspectives: problem definition, existing datasets, literature review, and evaluation metrics. Through a critical analysis, we address dataset limitations and scrutinize contemporary algorithms. Here we use multimodal Fusion, which achieves the state of the art compared to existing methodologies. Moreover, we explore potential future research directions to inspire innovative solutions and applications in this evolving domain, aiming to propel vQA into new realms of exploration and practical utility. This project will allow users to input an image and image-related text so that it will aid the question-answering system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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YOLO-based anomaly activity detection system for human behavior analysis and crime mitigation

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SIGNAL image AND vIDEO processing 2024年第SUPPL 1期18卷 417-427页

作者： Ganagavalli, K. Santhi, v. Bannari Amman Inst Technol Sathyamangalam India PSG Coll Technol Coimbatore India

Detecting the objects and tracking them is a wonder with the help of the recent technologies. In today's life, video surveillance is commonly present at maximum places. As the population increases, the crime rates also keep on increasing at different cases. The crime identification, dealing with the crime scenes and tracking down the criminal is a major task which involves numerous man powers. If there is a methodology for detecting and preventing the crime, it would be very much helpful to the public and also to the authorities. So the main objective of the proposed system is to automate the crime identification and crime tracking process by using the video surveillance with deep learning algorithms. Also, to alert the public before a crime takes place in public areas. Because of the timely alert, further crime activities may be avoided or the loss incurred may be reduced. The system also helps in identifying the criminals and tracking the information about the crime scenes, thus providing more useful information to the authorities in solving the cases. According to recent research works, the YOLO algorithm achieves higher accuracy with multi-object detection. In this paper, a framework with improvised YOLO algorithm is illustrated. The algorithm is fine-tuned with different hyperparameters for achieving the AUC with 0.91 for detecting vandalism behavior and 0.8299 over all the 14 classes of crime activities. The result of the proposed system is compared with the existing systems with parameters like training loss, testing loss, precision and F1 score.

关键词： Anomaly detection Abnormal behavior Anomaly activities Yolo Hyperparameters Multi object detection

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An Area-Efficient FPGA Implementation of a Real-Time Multi-Class Classifier for Binary images

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 2022年第4期69卷 2306-2310页

作者： Attarmoghaddam, Narges Li, Kin Fun Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 3P6 Canada

Developing image classification modules in embedded systems is a complex task due to the limited resources available. In this brief, a multi-class image classifier using HOG feature extractor and SvM classifier is proposed for binary images. The novelty of the proposed system is applying two steps of binarization to the HOG technique to improve processing speed and area efficiency. First, HOG features are extracted from binary images to simplify the feature extraction process. Second, block normalization of the HOG is replaced with binarization to reduce hardware resource utilization. Compared to a similar existing work, our system speeds up the classification process while utilizing fewer hardware resources, with an 11.4% higher classification accuracy using the same setting.

关键词： Hardware Feature extraction Support vector machines Clocks Histograms Classification algorithms image classification HOG SvM FPGA hardware implementation image classification binary image

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Automated Detection and Analysis of Road Cracks and Pothole Depths using Computer vision and Depth Imaging 15

Automated Detection and Analysis of Road Cracks and Pothole ...

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

作者： Makkena, Manohar Lingamallu, Geyani Digavalli, veda Harshitha Gudupalli, vamshidhar Reddy Manikandan, v.M. Panchikkil, Shaiju Srm University-AP Andhra Pradesh India

ISBN: (纸本)9798350370249

Maintaining road infrastructure is essential to effective transportation systems and public safety. This research provides a new method for pothole depth estimation and automatic road crack detection using computer vision techniques. Our method utilizes convolutional neural networks (CNNs) for classifying road images into 'With Crack' or 'Without Crack' categories with high accuracy. Additionally, we employ image processing algorithms to detect and highlight cracks, providing insights into their lengths and percentages. Furthermore, we introduce a monocular depth estimation model to assess pothole depths, aiding in prioritizing road repair efforts. Experimental results demonstrate the effectiveness of our approach in accurately identifying road defects and estimating their severity. This research contributes to the advancement of intelligent infrastructure management systems, enabling proactive maintenance and ensuring safer roads for communities. © 2024 IEEE.

关键词： CNN Heat Map image Analysis image Classification Monocular depth estimation

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Localization of Dengue virus Likelihood Using image processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo v4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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A novel method for indian vehicle registration number plate detection and recognition using CNN

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Journal of Intelligent and Fuzzy systems 2024年第4期46卷 8563-8585页

作者： Pandey, vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception v3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： image classification

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Design of a 128-channel transceiver hardware for medical ultrasound imaging systems

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IET CIRCUITS DEvICES & systems 2022年第1期16卷 92-104页

作者： Kidav, Jayaraj Pillai, Perumal M. Deepak, v Sreejeesh, S. G. Natl Inst Elect & Informat Technol Calicut Kozhikode 673601 Kerala India

In this work, the design and development of a 128-channel transceiver hardware for medical ultrasound imaging systems and research is presented. The proposed hardware solution integrates the analog front-end (AFE) sections, high voltage transmit pulser sections, field programmable gate array (FPGA)-based transmit beamforming and control logic, time gain compensation (TGC) and continuous (CW) Doppler functional circuits, and the necessary power supplies (high voltage (Hv) and low voltage (Lv)) into a single board. In addition, it integrates pervasive segments like power, clock tree sections, and power management and debugger logic. The developed transceiver solution helps to advance the research in medical ultrasound imaging techniques and technologies. To prototype an ultrasound imaging system, the developed hardware can be interfaced with a 128-channel ultrasound transducer array and an FPGA-based signal processing module. As the transceiver hardware is designed with commercially available chipsets, it provides the flexibility to programme the ultrasound AFE signal chain, transmit beamforming and the arbitrary transmit wave pattern. Besides, compared to the commercial open ultrasound research scanners, the flexibility to interface FPGA-based signal processing module helps to investigate the performance of hardware realisation of various ultrasound signal processing algorithms. Moreover, the work realises a single-board transceiver solution for multichannel ultrasound system fulfilment.

关键词： biomedical transducers arbitrary transmit wave pattern medical image processing transceivers analog front-end sections hardware realisation FPGA-based signal processing module time gain compensation clock tree sections field programmable gate array-based transmit beamforming high voltage transmit pulser sections 128-channel transceiver hardware single-board transceiver solution multichannel ultrasound system fulfilment ultrasonic transducer arrays medical ultrasound imaging systems debugger logic ultrasound AFE signal chain power management field programmable gate arrays power supplies array signal processing biomedical ultrasonics hardware solution ultrasound signal processing algorithms 128-channel ultrasound transducer array continuous Doppler functional circuits

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image Search Engine with Recognition 4

Image Search Engine with Recognition

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kumar, Jmsv Ravi Dheeraj, Bellaganti Gowtham, Geddada Babu, Dokala vivek Kiran, Bonthu Asha Srikanth, M. Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331523923

The image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use. © 2025 IEEE.

关键词： Motion estimation

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