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14th International Symposium on Intelligent systems

作者： El-Khatib, S. A. Skobtsov, Y. A. Rodzin, S., I Southern Fed Univ 105-42 Bolshaya Sadovaya str Rostov Na Donu 344006 Russia St Petersburg State Univ Aerosp Instrumentat 67 Bolshaya Morskaya Str St Petersburg 190000 Russia

image segmentation is the process of dividing image into homogenous regions by some charasteristics and is widely used in medical diagnostics. Segmentation algorithms are used for anatomical features extraction from medical images. The Hybrid Ant Colony Optimization (ACO) k-means and Grub Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. As there is no universal algorithm for medical image segmentation, image segmentation is still a challenging problem in image processing and computer vision in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Grub cut. (C) 2021 The Authors. Published by Elsevier B.v.

关键词： MRI image segmentation ant colony optimization k-means algorithm swarm intelligence grub cut

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Algorithm for Changing the Size of a Multi-Element Aperture of a Two-dimensional Recursive-Separable Five-Stage Filter for Digital image processing

Algorithm for Changing the Size of a Multi-Element Aperture ...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Andrey v. Kamenskiy Kristina S. Kupriyanova Andrey v. Podnebesnyy Mikhail I. Kuryachiy Natalya Borodina Akaeva M. Tatyana Dept. of Television and Control Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The paper presents a description of the developed algorithm for changing the size of a multi-element aperture of a recursive-separable five-stage filter for processing digital images generated by specialized optical systems. The developed algorithm has two features: it has the ability to change the size of a multi-element aperture during use; it makes it possible to use the recursive and separable properties. These capabilities make it possible to reduce the processing time of incoming data, which has a positive effect on the efficiency of the optical-electronic imaging system used. The paper presents a block diagram of a two-dimensional recursive-separable five-stage filter, with the help of which an algorithm was developed for changing the size of a multi-element aperture, and a study of the influence of the aperture size on the performance of the developed filter was carried out. As part of the performance evaluation, the developed algorithm was compared with the classical two-dimensional convolution algorithm. Developed algorithm was tested on three test images of different sizes and formats: $1300 \times 700$ Bitmap Picture, 1200x1200 Tagged image File, 2900x1900 Joint Photographic Experts Group. During the processing of test images, the dimensions of the multi-element aperture of the developed filter changed. As the aperture size increases, the performance evaluation results also increase. This shows that speeding up the algorithm is an actual task. However, the developed algorithm takes less time than the classical two-dimensional convolution algorithm, from which it can be concluded that the developed solution is effective for both conventional images and images from optoelectronic systems.

关键词： Optical filters Performance evaluation Convolution Digital images image processing Apertures Filtering algorithms

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Self-annotated Labelling and Training Data for Traffic video Object Detection Using Machine Learning Techniques 7th

Self-annotated Labelling and Training Data for Traffic Video...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Rahul Chiranjeevi, v. Swamy, Maanesh M. Krishna Prasath, M.K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India

ISBN: (纸本)9783031699818

Classical machine learning algorithms are susceptible to objective elements like video quality and the weather, which results in inferior detection results in an erroneous identification of Unmanned Aerial vehicle (UAv) action photos. Deep learning is suggested for identifying cars in UAv video. Thanks to the technology offered by road management systems, real-time visual information is now accessible in thousands of locations on road networks. The first step in identifying or preventing accidents is locating the vehicles on the path of travel. Convolutional neural networks have made substantial advancements in the field of object detection. Enhancing conventional computer vision techniques. However, there are drawbacks because the pre-trained models that are currently available only provide a low detection rate, especially for small objects. The major drawback is that, in order to retrain the automobile maker, they must manually identify the vehicles that appear in the photographs captured by each IP camera on the road infrastructure. This task will be impossible to complete even if we install hundreds of cameras throughout the extensive road network. The technique utilized in this study uses CCTv footage with a comparable range of images with the aim of identifying cars in an image. The spatial brightness of the original sample is first translated using the HSv (Hue, Saturation, value) method to maximize sample diversity and adaptability to different lighting conditions. The performance of the basic SSD is enhanced by incorporating focus loss because of feature extraction. Following a trained neural network model’s examination of the UAv footage, the effectiveness of drone identification is assessed. The results show that the proposed method have a car detection rate of 96.49%. © IFIP International Federation for Information processing 2024.

关键词： Deep learning

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Testing Hypotheses about Covariance Functions of Cylindrical and Circular images

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PATTERN RECOGNITION AND image ANALYSIS 2021年第3期31卷 431-442页

作者： Krasheninnikov, v. R. Kuvaiskova, Yu E. Malenova, O. E. Subbotin, A. Yu Ulyanovsk State Tech Univ Ulyanovsk 432027 Russia

Imaging problems are becoming increasingly important due to the development of systems for aerospace monitoring of the Earth, radio and sonar location, medical devices for early diagnosis of diseases, etc. However, most of the work on image processing is related to images defined on rectangular two-dimensional grids or grids of higher dimensions. In some practical situations, images are defined on a cylinder (for example, images of pipelines, blood vessels, rotation details) or on a circle (for example, images of a facies (thin film) of dried biological fluid, an eye, a cut of a tree trunk). The specifics of the field of assignment of such images must be taken into account in their models and processing algorithms. In this paper, autoregressive models of cylindrical and circular images are considered and expressions of the correlation function are given depending on the autoregressive parameters. Spiral scanning of a cylindrical image can be viewed as a quasi-periodic process due to the correlation of image lines. To represent heterogeneous images with random heterogeneity, "double stochastic" models are used, in which one or several control images set the parameters of the resulting image. The available image can be used to estimate the parameters of the model of its control images. However, this is not sufficient to fully identify the hidden control images. It is also necessary to evaluate their covariance functions and find out whether they correspond to the hypothetical ones. The paper proposes a test for testing the hypotheses about the covariance functions of cylindrical and circular images with a study of its power relative to the parameters of the image model.

关键词： cylindrical and circular images quasi-periodic process autoregressive model identification test significance level power

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Maintaining the Integrity of Online Exams using Computer vision and image processing

Maintaining the Integrity of Online Exams using Computer Vis...

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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： Nanda Kumar K Nandini Guthikonda Mohammad Adil Abdullah S. Sagar Imambi v. Murali Mohan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur AP India

Due to the growing popularity of online learning, there is a need for efficient methods to assure the validity of online tests, especially multiple-choice exams (MCQs). The application of computer vision and picture pre-processing techniques is one strategy that has shown potential in this situation. With the use of these methods, cheating may be identified and stopped by analysing photos of the test-taker and their surroundings. In the context of online MCQ tests, this article addresses the possible uses of computer vision and picture pre-processing, including the use of facial recognition algorithms and image segmentation to detect and extract particular items or characteristics. The employment of these strategies offers enormous potential for maintaining the integrity of online education, even if there are still issues to be resolved, such as privacy concerns and the requirement for precise and trustworthy algorithms.

关键词： Computer vision Privacy Ethics image segmentation image resolution Face recognition Employment

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Underwater image Enhancement Using Gaussian Smoothing and Edge Detection

Underwater Image Enhancement Using Gaussian Smoothing and Ed...

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International Conference on Inventive Computation Technologies (ICICT)

作者： v. Lambodara Aravind C. Bagyalakshmi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Underwater acoustic communication is used for oceanographic data collection, environmental monitoring, disaster alarm systems, and navy applications. Underwater has its peculiarities such strong signal attenuation and limited bandwidths, multipath nature of signals, and ambient noises. All these characteristics bring a great deal of interference probably in transmission with certainty and effectiveness. This work propounded an optimized hybrid model that works concurrently with advanced signal processing and noise-reducing techniques for communication enhancement. The purpose of the proposed model is to develop high signal-to-noise ratio (SNR) received signals with lesser transmission error using time-frequency domain analysis and filtering techniques, which are specifically designed for underwater applications. Thus, the main aim of this work, in fact, is towards the development of a robust underwater communication platform, which ensures a very reliable transfer of data under conditions of excessive noise even in the rapidly changing aquatic environments. Comparison of the proposed model with traditional techniques shows that this model stands much higher than the earlier techniques regarding transmission accuracy, noise resilience, and overall efficiency, thus extending the development of more reliable underwater communication systems toward future marine and defense applications.

关键词： Adaptation models Underwater communication Smoothing methods image edge detection Computational modeling Signal processing algorithms Reliability Underwater acoustics image enhancement Signal to noise ratio

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Block Matching algorithms for the Estimation of Motion in image Sequences: Analysis 25

Block Matching Algorithms for the Estimation of Motion in Im...

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25th KES International Conference on Knowledge-Based and Intelligent Information & Engineering systems (KES)

作者： Rao, K. Srinivas Paramkusam, A., v Darimireddy, Naresh K. Chehri, Abdellah MLR Inst Technol Autonomous Dept CSE Hyderabad India Lendi Inst Engn & Technol Autonomous Dept ECE Vizianagaram India Univ Quebec Dept MCSE Rimouski PQ Canada Univ Quebec Chicoutimi Dept Appl Sci Chicoutimi PQ Canada

Several video coding standards and techniques have been introduced for multimedia applications, particularly the h.26x series for video processing. These standards employ motion estimation processing to reduce the amount of data that is required to store or transmit the video. The motion estimation process is an inextricable part of the video coding as it removes the temporal redundancy between successive frames of video sequences. This paper is about these motion estimation algorithms, their search procedures, complexity, advantages, and limitations. A survey of motion estimation algorithms including full search, many fast, and fast full search block-based algorithms has been presented. An evaluation of up-to-date motion estimation algorithms, based on several empirical results on several test video sequences, is presented as well. (C) 2021 The Authors. Published by Elsevier B.v.

关键词： Type your keywords here separated by semicolons

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Pre-processing algorithms for Accurate Analysis of an image: A Review

Pre-Processing Algorithms for Accurate Analysis of an Image:...

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International Conference on Smart systems and Inventive Technology (ICSSIT)

作者： Yasaswini Satuluri K venkata Ratna Prabha Jairam Swamy Polavarapu Harsha vardhan Battina venu Kota P Ramesh Dept. of Electronics and Communication Engineering V R Siddhartha Engineering College Vijayawada India Dept. of Conservative Dentistry and Endodontics Dr. S&NS Institute of Dental Sciences Chinoutpalli AP India

Real-world databases nowadays are particularly vulnerable to noisy, missing and inconsistent data due to their large size (often several terabytes or so more), as well as the potential that they come from multiple and diverse sources. Poor mining findings will result from low-quality data. To produce the appropriate image for a better analysis, images from the relevant dataset are pre-processed using a variety of currently available approaches. In this work, some of the numerous pre-processing methods are discussed and it is observed that filtering techniques like Gabor filter are more popular than other state-of-the-art techniques like interpolation, kernels etc.

关键词： Histograms Interpolation Convolution Filtering image edge detection Filtering algorithms Adaptive equalizers

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An Efficient ODR for Computer Night vision Using Multi-Scale Retinex Network-Aided Enhanced images

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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International Conference on Power, Energy, Control and Transmission systems (ICPECTS)

作者： Charles Prabu v P. Pandiaraja v Sathiyamoorthi P. Durgadevi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Engineering Government Polytechnic College Dharmapuri Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. Computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)" model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)" model. The You Only Look Once version 7 (Yolov7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%.

关键词： Night vision YOLO Deep learning Tracking Computational modeling image edge detection Surveillance Streaming media Real-time systems Human activity recognition

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Measurement of Asphalt Pavement Crack Length Using YOLO v5-BiFPN

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JOURNAL OF INFRASTRUCTURE systems 2024年第2期30卷

作者： Wang, Sike Dong, Qiao Chen, Xueqin Chu, Zepeng Li, Ruiqi Hu, Jing Gu, Xingyu Southeast Univ Sch Transportat Rd 2 Nanjing 211189 Jiangsu Peoples R China Nanjing Univ Sci & Technol Coll Sci Dept Civil Engn Nanjing 210094 Jiangsu Peoples R China

Pavement cracks are a kind of common distress in road service time, and their length measurement is critical for pavement maintenance. The current automatic method of crack length measurement uses segmentation algorithms to obtain crack curves, which is time-consuming and complex. In this study, an effective method of crack length measurement was proposed and validated. The method consists of a detection module based on an object detection algorithm and a length calculation module. To increase the speed and accuracy of crack detection, an improved pavement crack detection algorithm BiFPN-enhanced YOLO v5 (YOLO v5-BiFPN) based on you look only once version 5 (YOLO v5) and bidirectional feature pyramid network (BiFPN) is proposed, and gamma correction was utilized to process pavement images. YOLO v5-BiFPN was tested in a real pavement image data set and achieved remarkable performance. In the length calculation module, the diagonal length of the crack bounding box output by the object detection algorithm can be defined as the crack length. To validate the measurement method, the true value of crack length was obtained from the segmentation data set by skeletonization. The error between the calculation result of the proposed method and the real value is 3.4%, and the average processing time of each image is 14.2 ms. The developed method addresses the problem of considerable time and financial cost associated with the existing crack length measurement methods.

关键词： Pavement evaluation Crack length measurement Crack detection Gamma correction You only look once version 5 (YOLO v5)

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