Real-Time Object Detection via Cloud-Enabled Deep Learning: A Systematic Review | Chapter 6 | Mathematics and Computer Science: Research Updates Vol. 6

 

Automated vehicles, advanced surveillance systems, AR, and robots are just a few of the many new uses for real-time object recognition.  While deep learning models are becoming increasingly complex and accurate, they might be challenging to execute on edge devices with limited resources due to the computational demands. By offloading computationally intensive processes to scalable cloud infrastructure, cloud-enabled deep learning enables real-time processing without sacrificing detection accuracy, offering an effective alternative.  This study takes a close look at the current setup of cloud-based object recognition methods that work in real time. When considering latency, bandwidth, privacy, and processing costs, the pros and cons of several architectural paradigms are evaluated, including hybrid methodology, distributed inference, and edge-cloud cooperation. Additionally, the developments of lightweight convolutional neural networks (CNNs), single-shot detectors, and model compression techniques are examined, all of which are aimed at real-time performance in cloud environments.  Improving fault tolerance, optimizing data transmission, safeguarding data security and privacy, and developing more adaptive and efficient cloud resource management strategies for dynamic real-time object detection contexts are all areas that could be further explored in this review.

 

 

Author(s) Details

Abdul Razzak Khan Qureshi
Department of Computer Science, Medicaps University, Indore, Madhya Pradesh, India.

 

Ruby Bhatt
Department of Computer Science, Medicaps University, Indore, Madhya Pradesh, India.

 

Govinda Patil
Department of Computer Science, Medicaps University, Indore, Madhya Pradesh, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v6/5867

 

Analysing Image Processing Algorithms Using Correlational Values within the Cloud Platform| Chapter 6 | Mathematics and Computer Science: Research Updates Vol. 8

 

Background: Image processing strategy is an important part of image processing to visualise the performance and outcome of the goal. Image processing is a discipline in which the process's input and output are both images. It is a process that entails elementary operations such as noise reduction, contrast enhancement, and image sharpening. Image analysis is a process that takes images as inputs but produces attributes extracted from those images as outputs (e.g., edges, contours, and the identity of individual objects).

 

Aims: This paper aims to analyse the algorithms of image processing in the cloud platform. Several algorithms are commonly used in image processing and computing techniques. Correlations for the observation matrix were observed to marginalise the images, and results were transmitted to the cloud platform.

 

Methodology: Here, a selection of state-of-art is applied to test image processing execution and timing factor using different strategies and platforms. Among them, the dataset structure and performance of the system can choose a verification algorithm to achieve the final operation. Based on the structure of a real-time image processing system based on SOPC technology is built, and the corresponding functional receiving unit is designed for real-time image storage, editing, viewing, and analysis. Datasets were collected online from the free domain of kaggle.com. Images belong to the traffic light of 250 out of 2056 files. 120 images were selected randomly to process after pre-processing of the images.

 

Results: Studies have shown that the image processing system based on cloud computing has increased the speed of image data processing by 12.7%. Compared with another platform, especially in the case of segmentation and enhancement of the image. This analysis has advantages in image compression and image restoration on a cloud platform. Qualitative and quantitative performances in the cloud platform of the algorithm are compared, and the results of the three indicators show that the platform has better performance than others. The results show that the cloud platform requires less computational time in comparison with others after loading the image file into the system.

 

Conclusion: Different image processing parameters like noise, smoothing, the timing of enhancement and segmentation have a greater effect on the compression effect of the image, including correlational value within the dataset of the image. The larger the correlation, the less compressed the image data is, the faster the image compression rate, and the lower the image's peak entry-to-noise ratio.

 

 

Author(s) Details

Faizur Rashid
Department of Computer Science and Engineering, SCOS, JSPM University, Pune, India.

 

Gavendra Singh
Department of Computer Science and Engineering, SDGI Global University, U.P., India.

 

Jemal Abate
Department of Computer Science, University of Illinois, Brazil.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/5885

 

Novel Automatic Segmentation Approach for Early Brain Tumour Detection: Comparative Evaluation with AI Approaches | Chapter 5 | Medical Science: Updates and Prospects Vol. 1

 

Background: Automatic object detection in medical images is a crucial step in the diagnostic process. The problem of detecting brain tumours at an early stage is well advanced with deep learning algorithms (DLA) such as convolutional neural networks (CNN). The issue lies in the fact that these algorithms necessitate a training phase involving a large database of several hundred images, which can be time-consuming and require complex computational infrastructure.

 

Objective: This study aimed to comprehensively evaluate a proposed method, which relies on an active contour algorithm, for identifying and distinguishing brain tumours in magnetic resonance images.

 

Methods: The proposed algorithm was tested using brain images from the BRATS Challenges 2021, specifically focusing on glioma tumours. The proposed segmentation method is made up of an active contour algorithm, an anisotropic diffusion filter for pre-processing, active contour segmentation (Chan-Vese), and morphological operations for segmentation refinement.

 

Results: Its performance was evaluated using various metrics, such as accuracy, precision, sensitivity, specificity, Jaccard index, Dice index, and Hausdorff distance. The proposed method exhibited higher performance measures than most classical image segmentation methods and was comparable to the deep learning methods. These results indicate its ability to detect brain tumours accurately and rapidly.

 

Conclusion: The results section provided both numerical and visual insights into the similarity between segmented and ground truth tumour areas. The findings of this study highlighted the potential of computer-based methods in improving brain tumour identification using magnetic resonance imaging. Future work must validate the efficacy of these segmentation approaches across different brain tumour categories and improve computing efficiency to integrate the technology into potential clinical processes.

 

 

Author(s) Details

Mohammed Almijalli
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Faten A. Almusayib
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ghala F. Albugami
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ziyad Aloqalaa
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Omar Altwijri
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ali S. Saad
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v1/6551

Automated Grading and Classifying Tear Ferning Images Using a Novel Computer-Based Approach | Chapter 6 | Mathematics and Computer Science: Contemporary Developments Vol. 6

In the current endeavor, the major purpose is to develop a novel computer-based approach for determining the properties of tear ferning (TF). Through the utilization of the newly designed five-point grading system, it is possible to automatically analyze each TF image by utilizing the original TF photographs. This study aims to develop an automated system for grading tear-ferning images to improve the accuracy and efficiency of diagnosing dry eye conditions. A novel approach was introduced, constructing vector characteristics (VC) for each grade using a combination of texture analysis with gray-level co-occurrence matrix (GLCM), power spectrum (PS) analysis, and line segment counting. Three distinct power frequencies were utilized since the VC possessed the ability to differentiate between different frequencies. The differences in likeness that were seen between the pictures served as a source of inspiration for the choosing of line segments. Based on the findings of analysis, it was discovered that each grade of TF reference image contained a unique vector cloud (VC) that displayed notable distinctions from the other grades. Key features from GLCM, PS at specific frequencies and the number of line segments were used to build the VC. The results showed significant differences between the VCs for each grade, indicating the potential for accurate automatic grading. This advancement represents a crucial step towards creating more objective and reliable computer-based diagnostic tools for dry eye conditions.

 

Author (s) Details

Ali S. Saad

Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University Riyadh 11433, Saudi Arabia.

 

Gamal A. El-Hiti
Department of Optometry, College of Applied Medical Sciences, King Saud University Riyadh 11433, Saudi Arabia.

 

Ali M. Masmali
Department of Optometry, College of Applied Medical Sciences, King Saud University Riyadh 11433, Saudi Arabia.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v6/2693

License Plate Recognition and Extraction of Details from Database | Chapter 20 | Leading the Charge: A Guide to Management, Entrepreneurship and Technology in the Dynamic Business Landscape Edition 1

A sort of technology known as number plate recognition (LPR), primarily software, allows computer systems to automatically read a car's number plate number from digital photos. License Plate Recognition systems use the concept of optical character recognition to read the characters on vehicle license plates. Registration plate detection devices rely on optical character processing to comprehend the written information on the automobile's registration tag. Stated differently, LPR produces the characters that are engraved on an automobile's registration plate after receiving an image of the vehicle as input. It displays the vehicle's details and reads the characters. It can make use of already-installed closed-circuit television, and traffic enforcement cameras. It is used by enforcement agencies across the globe to enforce the law, including Figuring out whether an automobile is licensed or registered. On top of that, this is the case utilized by highway agencies and other pay-per-use roadways for electronic toll collecting and traffic flow cataloging. The main motive is to interpret and show registration plate individuals so that cars may be recognized and categorized.

 

Author (s) Details

 

P. Haarathi
Department of Electronics and Communication Engineering, V R Siddhartha college, Andhra Pradesh, India.

 

G. Venkata Subbaiah
Department of Electronics and Communication Engineering, V R Siddhartha college, Andhra Pradesh, India.

 

P. Hema Nandini
Department of Electronics and Communication Engineering, V R Siddhartha college, Andhra Pradesh, India.

 

P. Bala Naveena
Department of Electronics and Communication Engineering, V R Siddhartha college, Andhra Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-98-3/CH20

Enhancing Diagnostic Precision by Image Processing in γ-Camera Scintigraphy for Lower Extremity Inflammation | Chapter 4 | Science and Technology - Recent Updates and Future Prospects Vol. 2

 

The present study highlights the improvement of evaluation of the inflammation extent on the scintigraphic imaging, by utilizing statistical indices (Inflammation Projection Ratio - IPR, skewness, kurtosis and Mean Pixel Value - MPV). Inflammatory processes and infection imaging are forms of tissue characterization by Nuclear Medicine. Image analysis was performed by means of an Interactive Data Language (IDL) tool. Twelve patients were referred for a radionuclide (Tc99m- Leukoscan) scan, by a GE Healthcare gamma camera, on the suspicion of an infectious lesion in the extremities. The findings of the study are that pathological tissues have a higher IPR index (3.12 to 4.32) compared to normal tissue (~ 1). The calculated indices IPR in combination with the processed Isocontouring images, contribute to the conclusion that Tc99m- Sulesomab scintigrams are characterized by high projection ratios specific for each case and demonstrate the full extent of the inflamed area in the lower extremities, with great accuracy. Furthermore, MPV, skewness and kurtosis differ significantly (> 5%) from normal to inflammable extremities. As a conclusion, image processing provides effective information in the structure and facilitates diagnosis semi-quantitatively. Moreover, increased imaging research and discussed the limitations of the study, such as the need for a larger sample size.

 

Author(s)details:-

 

M. Lyra
Department Radiology of Radiation Physics Unit A´, University of Athens, GR-11528, Athens, Greece.

 

S. Kordolaimi
Department of Radiology, Medical School, University of Athens, 1 Rimini Str, 12462 Haidari, Athens, Greece.

 

A. L. Salvara
IATROPOLIS Medical Group, Athens, Greece.

 

Please See the book here :- https://doi.org/10.9734/bpi/strufp/v2/7877C

Real-Time Assessment of Edge Detection Techniques in Image Processing: A Performance Comparison | Chapter 7 | Contemporary Perspective on Science, Technology and Research Vol. 8

This study illustrates the performance analysis of the most commonly used edge detection techniques including Canny, Sobel and Prewitt, highlighting their advantages and disadvantages with respect to different types of datasets.  One of the most important stages in image processing to find and detect discontinuities in intensity change is edge detection. It is a useful tool for identifying various aspects of a picture, including shape, contrast, color, scene analysis, and image segmentation. The technique is very important to recognize all the edges accurately. It helps in object recognition, pattern recognition, medical image processing, motion analysis etc. There are many edge detection operators available in image processing.  After analyzing various parameters like Accuracy, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Edge Detection Processing Time and Qualitative Human Visual Perception on two diverse type of datasets, varied results are found with respect to the techniques used. Among them, the most accurate and fast computed edge detection technique which gives better results on both type of datasets is concluded. Although the Sobel edge detection technique gives relatively poor result and weak performance of detection of edges, however it can be modified and further improved with respect to future work. The entire analyzing process was done under Scilab software. Canny works well also but it can be used for detecting very thin edges with the disadvantage of it cannot detect object very precisely because of detecting small amount of intensity variation. Sobel gives a very bad performance for small objects because it is used for detecting thick edges so sobel is best fit for detecting satellite images of large geographical area images. Future work can also be done for video edge detection and an improved sobel edge detection technique can be proposed which can detect thin edges also to overcome the disadvantage of limitation of geographical area. In future work the platform of comparison can also be change.

Author(s) Details:

Rajshree Kumari,
Department of Computer Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.

Divyanshu Chandra,
M.C.A. Programme, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.

Please see the link here: https://stm.bookpi.org/CPSTR-V8/article/view/14054

Identification of Polyps and Tumors from Endoscopy Images with Deep Belief Network | Chapter 5 | Research and Applications Towards Mathematics and Computer Science Vol. 6

 It has existed demonstrated that a successful figure processing approach can process real-time endoscopic videos to assist specialists in making critical conclusions about cancer patients. An productive diagnostic measure in gastrointestinal area is endoscopy, which us an endoscope with a camcorder and a transmitter which sends television frames. Using the existing software, the representations are taken from original-time endoscopic videos and fed into MATLAB for figure processing. The treated videos' output is then provided back into the host program. Image processing techniques have happened used to identify and advance the visualization of polyps in the gastro intestinal plan, which assists experts in making decisions. The system's goal search out let the specialist or medical practitioner visualize and recognize problematic constructions such as polyps and bleeding spots all the while endoscopic operations. This submitted approach is tested on a recorded gastrointestinal dataset that has ten series videos of 7894 frames.

Author(s) Details:

Nagesh B. S.,
Department of CSE, R. N. S. Institute of Technology, Bengaluru, India.

N. P. Kavya,
Department of CSE, R. N. S. Institute of Technology, Bengaluru, India.

Please see the link here: https://stm.bookpi.org/RATMCS-V6/article/view/12524

Image Processing Techniques for Medical Mammography Masses Detection Applications | Chapter 12 | Research Highlights in Science and Technology Vol. 9

The function of image refine is crucial in the mammography for detecting of any undesired suspicious domains in the mammogram. Quality of image is majorly met in proposed concept enhancement technique for bringing back of efficient feature extractions. SVM and GMM located classifier models are built for reconstructing the performance limits of proposed methodologies. Aim concerning this chapter search out reduce the commotion in the mammogram image in order to embellish the mammogram image. Enhancement methods, such as, contrast elongated and histogram counterweight, morphological, median filters are investigated on mammogram images and results are distinguished for analyzing the representation enhancement. These techniques are used to advance the clarity and denoise of mammogram concepts. It is useful for detecting the tumors in the mammogram efficiently and it is helpful to establish and size of masses or tumors correctly in mammogram. Proposed methodology shows the figure from the selected range of pixels and it show the force of the mammogram image at the indicated pixel range. By this, the mammogram image will be smoothened that will be useful for diagnosing the tumors.

Author(s) Details:

K. Rajendra Prasad,
Department of CSE (CS), Institute of Aeronautical Engineering, Hyderabad, Dundigal, Telangana-500 043, India.

Please see the link here: https://stm.bookpi.org/RHST-V9/article/view/11680

Analysing the Classification of Rice Leaf Diseases on the Basis of Deep Convolutional Neural Network Architectures | Chapter 9 | Research Highlights in Science and Technology Vol. 3

 In the field of farming, timely investigation and acknowledgment of plant leaf diseases assures extreme crop quality and yield. Due to a lack of knowledge about ultimate cutting-edge refined approaches in the field of leaf disease discovery, one of the largest impediments for rice growers is the identification of leaf diseases. Due to the repetitiveness of rice leaf afflictions, a large portion of rice development is disrupted. Early discovery of rice leaf diseases is immediately done manually by farmers, that is extremely late and labor-intensive. However, the requirement of mechanical disease discovery in rice leaves aids producers in more effectively preserving their land harvests. In this review, the major focus act performance analysis of discovery of rice leaf ailments based on the architectures employed. Convolutional affecting animate nerve organs networks are the best method for classifying edible grain leaf diseases, and advances in calculating vision and deep learning placate predictions and come to a close the greatest method for achievement so. Numerous CNN architectures have been resolved for finding best classification depiction based on training from the very beginning, fine tuning or through transfer knowledge. Here, right selection of Deep CNN architectures for classification purposes supports high performance rates established the type of learning employed.

Author(s) Details:

Taruna Sharma,
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

Puninder Kaur,
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

Jasmeen Chahal,
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

Himanshu Sharma,
Department of Electronics and Communication Engineering, JBIET, Hyderabad, India.

Please see the link here: https://stm.bookpi.org/RHST-V3/article/view/10760

A Literature Review on Mathematical Morphology Based Image Segmentation Techniques | Chapter 6 | Recent Progress in Science and Technology Vol. 8

 Digital figure processing is an arising technology and has found boundless applications in biomedical image, stellar imaging, detached sensing and sonar imaging, the study of climatology, seismology, bottom of the sea studies and exploration of soil resources. Image segmentation process of some digital concept has been found main in the second level of processing and the main aim concerning this level of processing is to produce and feed the information to the calculating machine for subsequent greater level image dispose of. Morphology based image separation has occupied a important position in state of art figure processing and it needs a all-encompassing investigation for the betterment of the subject worried and for the ease of handling of the large data by the machines. The present phase has aimed at to present a all-encompassing and exhaustive literature study on differing aspects of separation of digital images utilizing morphological approach.

Author(s) Details:

Pinaki Pratim Acharjya,
Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India.

Subhabrata Barman,
Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India.

Subhankar Joardar,
Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India.

Santanu Koley,
Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India.

Please see the link here: https://stm.bookpi.org/RPST-V8/article/view/10062

Machine Vision Assisted Precision Alignment System for Optical Path Calibration in Point Diffraction Interferometer | Chapter 2 | Techniques and Innovation in Engineering Research Vol. 5

 In point dissemination interferometer (PDI), alignment error 'tween objective convergent spot and diffraction small hole made by stab is inevitable during the tests. This wonder will affect the performance of PDI in attend aspects: 1) great wavefront wrong; 2) reduction on diffraction adeptness; 3) quality of interferograms. All these factors can finally reduce the measurement accuracy of the instrument. Aimed at these technical challenges, we intend machine vision helped precision alignment arrangement for PDI optical path measurement. Firstly, Rayleigh-Sommerfeld vector diffraction belief is used to build mathematical model among adjustment error, diffraction wavefront wrong, numerical aperture, and hole size. To satisfy the necessity of error calibration, blueprint of machine vision helped optical path adjustment system is designed. Then we select magnetron sputtering and electron beam lithography to cloth precision diffraction pinhole. In this stage, adjustment images as well as PDI dissemination efficiency (intensities ratio middle from two points reflected and diffracted beam) are the dominant news to determine alignment mistake in multi-directions. In addition, specialized concept processing algorithm is created that can measure alignment error in pel and physical scope. To accomplish automatic correction, numerical model between measurement and control quantities is built. Finally, implementation and experiment of this form are also introduced. Misalignment on sideways translation (XOY plane), longitudinal defocus (unobstructed path Z) and tilt error (XY tile) are well calibrated and the character of interferograms is also improved. It maybe concluded that the proposed scheme has advantages in accuracy and adeptness of optical path adjustment.

Author(s) Details:

Zhuo Zhao,
State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, No.99 Yanxiang Road, Xi’an-710054, Shaanxi, China, State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun-130033, China and Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, China.

Bing Li,
State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, No.99 Yanxiang Road, Xi’an-710054, Shaanxi, China.

Leqi Geng,
State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, No.99 Yanxiang Road, Xi’an-710054, Shaanxi, China.

Jiasheng Lu,
State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, No.99 Yanxiang Road, Xi’an-710054, Shaanxi, China.

Zheng Wang,
Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, China.

Please see the link here: https://stm.bookpi.org/TAIER-V5/article/view/9735

Detection of Land Use and Land Cover from an Optical Remote Sensing Image: A Review | Chapter 4 | Techniques and Innovation in Engineering Research Vol. 1

 The identification of land use and land cover from an optical image has been a critical research area since since remote sensing images were first developed. The management of numerous catastrophic events like floods, tsunamis, and forest fires makes extensive use of maps of land use and cover, as well as other fields such as agriculture, environmental monitoring, urban planning, and others. In this study, the primary techniques for determining land use and land cover from an optical remote sensing image were investigated. After various ways based only on spectral information, spatio-contextual information, and knowledge-based methods have all been investigated, the importance of strategies based on spatial context and mathematical morphology has finally been argued.

Author(s) Details:

A. V. Kavitha,
Government College For Women (A), Guntur, Andhra Pradesh, India.

A. Srikrishna,
Department of Information   and   Technology,   RVR   JC College of   engineering,   Chowdavaram,   Guntur,   Andhra Pradesh, India. 

Please see the link here: https://stm.bookpi.org/TAIER-V1/article/view/8175

Attendance Capturing and Consolidation Mechanism Using Image Processing and Machine Learning Technique | Chapter 11 | Innovations in Science and Technology Vol. 5

 

Microwave ovens had a crucial role in the twentieth century. This frequency also sees a lot of application development. Microwave tube advancements have aided numerous growths in the recent past. Based on the RF field phase velocity, microwave tubes can be classed as slow-wave or fast-wave devices. A high-power source and amplifier are necessary for millimetre and sub-millimeter wave applications, which can be obtained by designing fast-wave devices. The interaction of the electron with the RF wave in fast wave tubes differs from slow-wave devices, as will be explained further. A brief history is discussed, as well as the many types of fast-wave tubes and their dispersion relationships.

Author(S) Details

Rajib Bag
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India.

Gautam .
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India

Komal Das
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India.

P. Mohini Amma
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India.

View Book:- https://stm.bookpi.org/IST-V5/article/view/5706

A Prominent Study of Head Phantom Image Using Correlation Coefficient | Chapter 03 | New Visions in Science and Technology Vol. 7

 In imaging techniques such as X-Ray and MRI, the only available information is 2D projections of the true 3D environment. In this research, we look at image projections created by the Radon transformation. The Radon Transformation is a basic technique that is utilised in a variety of applications, including radar imaging, geophysical imaging, nondestructive testing, and medical imaging. We created an image reconstruction method that takes as input various projections of the original image. To test and verify the method, we conducted this experiment with an artificially produced image. The most important thing to check is the quality of images reconstructed by an algorithm. So, in this paper, we calculated correlation coefficient, Cutoff rotation angle for an image, and linearity of correlation coefficient on a head phantom MRI picture.

Author(S) Details

Omveer .
Department of Electronics and Communication Engineering, Rajasthan Technical University, Kota, India.

Vinith Chauhan
Department of Technical Education, Uttar Pradesh Technical University, Uttar Pradesh, India.

View Book:- https://stm.bookpi.org/NVST-V7/article/view/4408

Determination of Information Security Using Cryptography and Image Steganography | Chapter 7 | Advanced Aspects of Engineering Research Vol. 16

 For their own vested interests, hackers are frequently willing to hack secret documents. Establishing a secure relationship between the secret message and image quality is the most difficult task. To prevent illegal attacks by an unknown person, the suggested approach combines cryptography and visual steganography techniques. Image and message security will be enabled by this approach. Cryptographic algorithms based on the International Data Encryption Algorithm (IDEA) and steganography algorithms based on the Discrete Cosine Transform (DCT). Cryptography's goal is to encrypt and decrypt the document. Steganography is a technique for securely transmitting secret data over the internet that involves hiding documents within an image with increasing payload. We show a single application in this paper to mask the sender's information, which is an important document and a secret file. Unauthorized users will be unable to see the form. With a payload of 52,400 bytes of information in an image, the PSNR is 90.06 dB.

Author (S) Details

G. Mallikharjuna Rao
ECE Department, Affiliated to Osmania University, Hyderabad, India.

View Book :- https://stm.bookpi.org/AAER-V16/article/view/1992

Research on Automatic Crack Detection for Concrete Infrastructures Using Image Processing and Deep Learning | Chapter 6 | Current Approaches in Science and Technology Research Vol. 3

 Automatic crack detection is a critical task in the generation of a crack map for existing concrete infrastructure inspection. This paper describes an automatic crack detection and classification method based on a genetic algorithm (GA) for optimizing image processing technique parameters (IPTs). Under various complex photometric conditions, the crack detection results of concrete infrastructure surface images remain noise pixels. Following that, a deep convolution neural network (CNN) method is used to automatically classify crack candidates and non-crack candidates. Furthermore, the proposed method is compared to state-of-the-art crack detection methods. The experimental results validate the reasonable accuracy in practice. The final goal was to create a crack map, which necessitated automatic pixel-level accuracy.

Author(s) Details

Cuong Nguyen Kim
Faculty of Highway & Bridge, Mien Trung of Civil Engineering, Vietnam.

Kei Kawamura
Graduate School of Science & Technology for Innovation, Yamaguchi University, Japan.

Hideaki Nakamura
Graduate School of Science & Technology for Innovation, Yamaguchi University, Japan.

Amir Tarighat
Department of Civil Engineering, Shahid Rajaee Teacher Training University, Iran.

View Book :- https://stm.bookpi.org/CASTR-V3/article/view/1411

Vision-based Measurement of Geometric Parameters of Cracks in Concrete | Chapter 5 | Advanced Aspects of Engineering Research Vol. 11

 The geometrical parameters of a cracked concrete surface are estimated using an optical microscope and an 8-bit RGB image generated with a high resolution camera based on close distance photography. The image's pixel intensity distribution can be used to determine factors such as fracture breadth, depth, and shape. To estimate the crack's geometrical dimensions, the image is converted to 16-bit grey scale, and then a mathematical relationship connecting the intensity distribution to the depth and width is derived using the enhanced image. For the crack samples utilised in the research, this connection allows for a 10% and 15% accuracy in estimating the width and depth, respectively. For mathematical processing of picture data and statistical computations of geometric parameters of concrete cracks, OriginLab tools were employed.

If the 8-bit RGB image is synthesised from photos of fractures collected with different light directions, the accuracy should be increased even more.

Author (s) Details

Yuriy Vashpanov
Department of Physics, Odessa State Academy of Civil Engineering and Architecture, Odessa, 65029, Ukraine and Public Safety Research Institute, Konyang University, Nonsan, Chungnam, 32992, Republic of Korea.

Jung-Young Son
Public Safety Research Institute, Konyang University, Nonsan, Chungnam, 32992, Republic of Korea.

Gwanghee Heo
Public Safety Research Institute, Konyang University, Nonsan, Chungnam, 32992, Republic of Korea.

Tatyana Podousova
Applied Mathematics Department, Odessa State Academy of Civil Engineering and Architecture, Odessa, 65029, Ukraine.

Yong Suk Kim
Public Safety Research Institute, Konyang University, Nonsan, Chungnam, 32992, Republic of Korea.

View Book : https://stm.bookpi.org/AAER-V11/article/view/1248