Object Detection by Stereo Vision Images E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
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Seitenzahl: 373

Veröffentlichungsjahr: 2023

Beschreibung

OBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems. Audience Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.

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Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Object Detection by Stereo Vision Images

Edited by

R. Arokia PriyaAnupama V PatilManisha BhendeAnuradha ThakareandSanjeev Wagh

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2022 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-84219-4

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. Therefore, this book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems.

Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Briefly stated, since it is a pioneer reference in this particular field, it will be a significant source of information for researchers who have been longing for an integrated reference. It is ideally designed for researchers, academics, and post-graduate students seeking current research on emerging soft computing areas; and it can also be used by various universities as a textbook for graduate/post-graduate courses. Many professional societies, IT professionals, or organizations working in the field of robotics will also benefit from this book.

A chapter-by-chapter synopsis of the topics covered in this book follows:

In

Chapter 1

, Deepti Nikumbh

et al.

present data conditioning techniques for medical imaging. Digital images have a tremendous influence on today’s world and have become an essential component in the clinical medical field. Significant advancements in the processing of medical images, and improvements in diagnosis and analysis, have transformed medical imaging into one of today’s hottest emerging fields for implementation and research. Image pre-processing techniques along with image segmentation and image processing algorithms are useful tools that pave the way for advancement in the medical field with wide applications such as cancer detection, fingerprint identification and many others using pattern matching, feature extraction and edge detection algorithms.

In

Chapter 2

, Shravani Nimbolkar

et al.

present an analytical study for pneumonia detection using machine learning and deep learning techniques. This chapter studies different types of lung diseases and how their diagnosis can be aided using these techniques. Experimentation with different machine learning models, like CNN and MLP, and pre-trained architectures, like VGG16 and ResNet, are used to predict pneumonia from chest X-rays.

In

Chapter 3

, Kavita R. Singh

et al.

explore the advanced application of a contamination monitoring system using IoT and GIS. Contamination/pollution is one of the biggest challenges where environmental issues are concerned. The authors analyze particular areas that are more contaminated/polluted in Nagpur City, Maharashtra, India, by calibrating the air quality index as an IoT-based air pollution monitoring framework and plotting the data using a geographical information system. Additionally, the data analysis, which is done with the help of Tableau software and different parameters like air quality index, temperature, etc., is provided to the end user through the android application.

In

Chapter 4

, Rajani P.K.

et al.

present the new area of video error concealment using particle swarm optimization. Video transmission over wired or wireless channels, such as the internet, is the fastest growing area of research. The proposed method is a novel method in the spatio-temporal domain that can significantly improve the subjective and objective video quality. There are many algorithms for video error concealment. These optimized algorithms should be used for obtaining better video quality. Particle swarm optimization (PSO), which is one of the best optimized bio-in-spired algorithms, is used to conceal the errors in different video formats. Correlation is used for detection of errors in the videos and each error frame is concealed using PSO algorithm in MATLAB.

In

Chapter 5

, Nalini Jagtap explores enhanced image fusion with guided filters. She proposes the modified guided filtering approach called “novel guided filtering” to overcome blurring and ringing effects. The primary step in this approach is to design the guidance image and generate the base and complex components based on that image. The edge detection operator plays a significant role in deciding the guidance image. The focus map is generated using low-rank representation, which is based on a detailed part of the original image. The built-in characteristic of removing ringing and blurring effects using LRR helps to develop artifact-free/noiseless detail-enhanced image fusion. First, guided filters are applied on a focus map; then the guided filter output is used to generate the resultant all-in-one fused image. In this case, ringing and blurring effects are removed using guided filters in the resultant fused image.

In

Chapter 6

, Tejaswini Yesugade proposes deepfake detection using LSTM-based neural network. The rapid growth of social media and new developments in deep generative networks have improved the quality of creating more realistic fake videos, which are called deepfake videos. Such deepfake videos are used in politics to create political turmoil, for blackmail, and terrorism. To reduce the harm that can be done using such methods and prevent the spread of such fake images or videos, the author proposes a method that can detect such deepfakes and a new method to detect AI-generated fake videos using an algorithm such as CNN and LSTM. This method will detect deepfakes by using ResNext50 and LSTM algorithms, which have an accuracy of around 88%.

In

Chapter 7

, Kavita Shinde

et al.

present various approaches for classification of fetal brain abnormalities with MRI images. Magnetic resonance imaging of fetuses allows doctors to observe brain abnormalities early on. Therefore, since nearly three out of every 1,000 fetuses have a brain anomaly, it is necessary to determine and categorize them at an earlier stage. The literature survey finds less work is involved in the classification of abnormal fetal brain based on conventional methods of machine learning, while more related work is conducted for the segmentation and feature extraction using different techniques. In this chapter, the authors review different machine learning techniques used for the complete MRI processing chain, starting with image acquisition to its classification.

In

Chapter 8

, Chinnaiah Kotadi

et al.

explore a method to analyze COVID-19 data using a machine learning algorithm. The authors analyze past COVID-19 data to raise awareness of COVID-19 second wave conditions and precautions against the delta variant. They also provide COVID-19 cases such as confirmed cases, cured patients’ cases, and death rates in India. Also, by using a machine learning algorithm, the states of India in which the most cases and deaths occurred are provided.

In

Chapter 9

, Manish Sharma

et al.

explore an intelligent recommendation system for evaluating teaching faculty performance using adaptive collaborative filtering. This system uses the deep learning model for the evaluation and enhancement of the performance of teachers in educational institutions. To give a recommendation framework, this work incorporates numerous elements such as student assessment, intake quality, innovative practices, experiential learning approaches, and so on. The dataset derived from an educational institute’s ERP was used to train and test the proposed recommender. The performance of the proposed recommender system was evaluated using the real-time data of teachers and other stakeholders from an educational institute apart from some secondary parameters. The comparative analysis of various techniques along with the performance comparison based on accuracy, precision, and recall are well furnished.

In

Chapter 10

, Manisha Blende

et al.

propose a virtual moratorium system. By using the proposed system, the banker will get all the information regarding a customer who has opted for the moratorium. The user will interact with the chatbot and submit the moratorium request and then chatbot will ask questions based on customers’ responses. Rasa natural language processing (NLP) and Rasa natural language understanding algorithm will classify the intents from the user responses. Intents will be compared with predefined patterns to extract the specific data. These responses will be stored in the NoSQL (MongoDB) database, and these data will be shared with the banker, who will further analyze them. The main purpose of this study is to help the banker know whether the customer who has applied for the moratorium is genuine. With this system, both the customer and the banker will be able to save time and effort. The proposed system will allow customers to register the moratorium at any time and from anywhere using a dedicated web platform and android application as well as some social media platforms. The complete chatbot moratorium system will be encrypted with secure encryption algorithms (AES-256, SSH). This system not only contains moratorium functionalities but also has some extra features, like News and Updates, which are crawled from various genuine news platforms and official banking sites. With all of these features, REST API services are also available for further enhancements and integration into multiple platforms.

In

Chapter 11

, Vandana TulsidasChavan

et al.

explore efficient land cover classification for urban planning. They propose the development of a land cover classification system that can classify images efficiently based on the land cover without any human intervention. Land cover categorization analysis for multispectral and hyperspectral pictures is evaluated. It provides a unique perspective on LU/LC change assessment and tactics at each level of the land cover classification process. The primary goal of this classification is to motivate future researchers to work accurately and to assist land resource planners, urban development managers, forest department personnel, and government officials in taking critical actions to maintain our precious planet’s ecology.

In

Chapter 12

, Pradnya Patil

et al.

present a study on data-driven approaches for fake news detection on social media platforms. The authors discuss the drawbacks and benefits of the increased sharing of information or news on social media and how it is becoming more difficult for social media users to distinguish between what is true and what is fake. Therefore, a data-driven analysis technique is increasingly being used in a variety of decision-making processes. Similarly, it may be used to detect fake news on social media platforms, allowing fraudulent material to be caught quickly and its lateral movement to be restricted before it reaches millions of consumers. Since it’s critical to have a mechanism in place that will assist social media users and communities in identifying bogus news, this will help detect fake news in a very efficient way and categorize news into fake, real, and unclear types.

In

Chapter 13

, Suvarna Patil

et al.

present a novel method to measure distance for object detection for automotive applications using a 3D density-based clustering approach for an advanced depression detection system. It determines whether a stereo vision-based item detection system is effective. Even though this approach has a fault in that it removes areas of the image that aren’t required for detection, the proposed method has been shown to provide reliable detection of potential obstacles as well as precise assessment of obstacle position and magnitude. This study proposes a method for detecting artifacts using 3D density-based clustering after deleting such regions with segmentation, in which the depth map was created by scaling the picture points to a scaled XZ plane. Then, using typical object grouping methods, the depth map can be easily segmented. The first ingenious encroachment was based on the separation of identifiable things.

In

Chapter 14

, Arokia Priya

et al.

discuss the intelligence developed for a system for estimating the depth using the connected components method. The proposed expert system focuses on using a stereo vision camera for capturing the left and right images, and finding the disparity between the objects by using blob detection instead of pixel disparity. The chapter evaluates and creates a system that finds the distance of the object placed in front of both the cameras using the disparity between two objects. It focuses on a single object with background and without background. The process is carried out both in CPU and GPU and the time complexity is analyzed apart from the accuracy of depth.

In closing, we are indebted to Martin Scrivener, for the tremendous support that he has given us since the inception of this book. It was only with the cooperation, enthusiasm, and spirit of the authors and reviewers that we could make it a grand success. Finally, we would like to thank you, the contributors, for your interest in the book and we encourage you to continue to send us your invaluable feedback and ideas for further improvement of our book.

Editors

R. Arokia Priya, Anupama V Patil, Manisha Bhende, Anuradha Thakare, Sanjeev Wagh

May 2022

1Data Conditioning for Medical Imaging

Shahzia Sayyad1, Deepti Nikumbh1*, Dhruvi Lalit Jain1, Prachi Dhiren Khatri1, Alok Saratchandra Panda1 and Rupesh Ravindra Joshi2,3†

1 Shah and Anchor Kutchhi Engineering College, University of Mumbai, Mumbai, India

2 Loknete Gopalrao Gulve Polytechnic, Nashik, India

3 North Maharashtra University, Jalgaon, India

Abstract

Digital images are tremendously influencing today’s world and have become an essential component in the medical and clinical field. The significant advancement in the processing of medical images and the improvements in diagnosis and analysis have transformed medical imaging into the most emerging fields in the light of the day for implementation and research. Medical scanners are used to create pictures of the human body. These pictures are used in the diagnosis of diseases. In medical imaging, contrast and image quality are the challenges being faced. For human interpretation or computer analysis, image enhancement makes the picture clear. The image enhancement process does not increase the data internal information material, but it may be used to emphasize the features of importance in order to identify the pictures more efficiently. Image preprocessing is like an operation at the lowest stage that is implemented on images. It is used to generate image data that improves the features relevant to further processing or removes unwanted distortion. Medical image processing is the technique of using various algorithms for processing medical images to obtain enhanced, restored, coded, or compressed images and gain valuable information. It has the ability to image the physiologic conditions that support tumor development and growth before it forms a sizable mass. Hence, it is very useful in detecting cancer at an extremely early stage which paves ways for effective diagnosis and treatment. Image preprocessing techniques along with image segmentation and image processing algorithms are useful tools that pave the way for advancement in the medical field with wide applications such as cancer detection and fingerprint identification using pattern matching, feature extraction, and edge detection algorithms.

Keywords: Medical image processing, segmentation, hyperspectral imaging, feature extraction, unsupervised learning, image acquisition

1.1 Introduction

Medical imaging can reveal both the normal and pathological functioning of the human body. It has the ability to anticipate cancer symptoms up to 10 years in advance. It does not release radiation or come into touch with the human body. As a consequence, patients do not have to go through needless testing or surgeries. As a consequence, there are no risks and no discomfort.

The motivation behind writing this chapter:

There are few opportunities to learn about this topic. Despite the author’s extensive analysis, all the available information did not answer the author’s most pressing questions, which are as follows:

Why is it essential to perform image processing and preprocessing on medical images?

What exactly is it doing in the background?

Finally, how can anyone perform image processing and preprocessing on their own?

This chapter is a collection of all the missing puzzle pieces that have been put together to address the above questions and demonstrate the magic that occurs on images when different image preprocessing and processing techniques are applied.

The following are the objectives of this chapter:

Describe the various types of medical images used for screening.

State the different steps involved in medical image processing.

Discuss preprocessing techniques of medical image processing.

Implement OpenCV and scikit-learn python libraries and perform various operations on the image using these libraries.

Explain image acquisition, reconstruction, and feature extraction techniques.

Illustrate an application of medical image processing, which is a case study on detection of throat cancer.

1.2 Importance of Image Preprocessing

Preprocessing is done to enhance the picture’s quality so that we can analyze it very efficiently. We can eliminate extra distortions and strengthen some features that are crucial steps done by preprocessing technique.

Both the upstream and downstream pictures are resolved at the lowest level of abstraction during preprocessing. The essence of prior knowledge is significant if preprocessing is used to correct any image degradation. The type of degradation, image properties, image noise, and certain spectral characteristics are the main features of image preprocessing. The various techniques are discussed in this chapter.

As a result of different techniques, we can increase the efficiency of programs that use those images, resulting in a more accurate result.

1.3 Introduction to Digital Medical Imaging

Medical imaging is the technique of getting photographs of internal organs for medical reasons such as defining or analyzing diseases. The term “medical imaging processing” refers to the use of a computer to manipulate images.

Every week, thousands of imaging procedures are practiced around the globe. Due to advances in image processing, such as picture analysis, augmentation, and identification, and displaying it in digital form, medical imaging is continuously improving. Medical images range from the most general, such as a chest X-ray, to the most complicated, such as practical magnetic resonance imaging.

Medical imaging is becoming a vital part of the entire healthcare ecosystem from well-being and monitoring through early diagnosis and medication selection, and follow-up medical imaging is becoming a vital part of the entire healthcare ecosystem. Medical imaging helps to diagnose disease processes and also provides a basic understanding of anatomy and organ function.

It could be used to perform medical testing including organ delineation, lung tumor prediction, spinal disfigurement diagnosis, and artery stenosis detection. To boost the quality of medical imaging, image processing techniques are used. As the volume and dimensions of healthcare records expand, novel computer-aided techniques are required to balance medical data and design effective and reliable methods. As the number of healthcare institutions and patients continues to rise, the use of computer-aided medical prognostics and decision support systems in clinical practice is now becoming particularly important.

Computational intelligence has the potential to improve the efficiency of healthcare systems (such as detection, treatment, and monitoring). Combining computer analysis with appropriate care has the potential to aid clinicians in improving diagnostic accuracy. Moreover, by combining medical images and other forms of electronic health records, the accuracy of detection can be increased and the time it takes to detect can be reduced.

Reduced processing costs, simpler networking and storage, instant data quality, multiple duplications while preserving quality, fast and inexpensive replication, and versatile manipulation are just a few of the features of data medical images.

1.3.1 Types of Medical Images for Screening

Types of medical images for screening are as follows:

X-rays

Computed tomography (CT) scan

Ultrasound

Magnetic resonance imaging (MRI)

Positron emission tomography (PET)

Mammogram

Fluoroscopy

Infrared thermography

1.3.1.1 X-rays

It is the oldest, as well as the most widely used imaging type. X-ray generally works on frequency and wavelengths that are unable to see without the naked eye. X-rays are relatively quick, low-cost, non-invasive, and easy for patients to endure [11].

1.3.1.2 Computed Tomography (CT) Scan

In a CT scanner, a motored X-ray supply fires a tiny beam of X-rays that revolves around the patient. The X-rays are picked up as they pass through the patient by special digital X-ray detectors, which are positioned directly opposite the X-ray supply. Through CT scans we can get abnormal structures [16].

1.3.1.3 Ultrasound

In medical imaging, ultrasound is among the safest forms and has a wide range of applications. During an ultrasound, high-frequency sound waves are transferred from the probe to the body via the conducting gel, and after striking various structures within the body, the waves bounce back, resulting in an image that can be used for diagnosis. It can assist in the diagnosis of several body parts, including the bones, pelvis, blood vessels, belly, kidneys, muscles, breasts, and joints [11].

1.3.1.4 Magnetic Resonance Imaging (MRI)

MRI creates images that are not visible with CT scans or X-rays. These images are created using radio waves and high magnetic fields. MRI does not use ionizing radiation. Malignancies, brain function, spinal cord injury, strokes, and aneurysms are all diagnosed by examining internal physiological components. The spins of the nucleon are aligned using a strong magnetic flux, and then, the spins of the protons are flipped using radiofrequency before being aligned again. Protons in various bodily tissues return to their original spins at varying speeds, allowing the MRI to distinguish between different types of tissue and detect any abnormalities. The molecules, on the other hand, “flip” and return to their original spin orientation, which is captured and processed into an image [11].

1.3.1.5 Positron Emission Tomography (PET) Scan

PET scan contains radioactive tracer. This tracer is either injected, inhaled, or swallowed in veins, depending on which body part is to be examined. The scanner uses the gamma rays emitted by the tracer to show images of bones and organs. Unlike other imaging types, it can catch problems much earlier and can show how different parts of the body are working. PET scan is usually painless and can be used for diagnosing, treatment of various diseases. It can check how well treatment is working and how deep the disease has spread [16].

1.3.1.6 Mammogram

In the fight against breast cancer, there are two types of mammography available: diagnostic and screening mammograms. Diagnostic mammography is used to check for cancer when a tumor or thickening in the breast is discovered. Mammograms for screening are used to detect any abnormalities.

1.3.1.7 Fluoroscopy

A fluoroscopy is like a motion picture of body function because it shows moving body parts. The procedure is generally performed using contrast dyes, which show how they flow through the body whereas all of this being done, a signal is being sent by an X-ray to the monitor. Fluoroscopies are used to analyze both hard and soft tissue, including organs, joints, bones, and vessels.

1.3.1.8 Infrared Thermography

Thermography is a diagnostic technique that uses infrared light to map the body’s anatomical processes. It is based on skin surface temperature, which is determined by the blood circulation in the skin’s outer millimetres. Infrared medical thermography is sensitive enough to detect any changes in skin temperature. The recording of temperature to build an image distribution on the body’s surface is known as clinical thermography. It is used for determining inflammation in different areas of the body [11].

1.4 Preprocessing Techniques of Medical Imaging Using Python

The images obtained through various data acquisition methods are processed. However, due to the extreme variety of noise present in the data, the raw images captured from the scan centers are not appropriate for direct processing. As a result, it must be preprocessed before being examined. Preprocessing is a crucial process, which includes filtering, labeling, artifact elimination, enhancement, and segmentation. Python libraries like OpenCV and NumPy are used for the implementation of the various preprocessing techniques [22].

1.4.1 Medical Image Preprocessing

Image preprocessing steps are as follows:

Reading the image

Resizing the image

Removing the noise from the image or denoise

Image filtering or smoothing

Image segmentation

Figure 1.1 displays the steps involved in Image preprocessing along with the various techniques that can be used for performing those steps.

Figure 1.1 Medical image preprocessing.

1.4.1.1 Reading the Image

This is the first step in performing image preprocessing. This step reads the image which has been imported to the system for analysis.

1.4.1.2 Resizing the Image

In image processing resizing the image is a method to expand and minimize the size of a given image in the form of pixels. The image is partitioned into two types: image up-sampling and image down-sampling, both of which are required when resizing data to match a particular communication channel or output display. The clarity of an image increases with an increase in pixels. This is called up-sampling. The pixel intensities in an input image are lowered based on the sampling frequency in the down-sampling technique [3]. In Figure 1.2, the different steps of image preprocessing are implemented in python using the OpenCV library.

Figure 1.2 Python code using OpenCV library.

Figure 1.3 Reading and resizing the image.

In Figure 1.3, the original image on the left side is read using the image read and image show method in python. The right side of the image has the resized image resized using the resize method of OpenCV, which takes parameters such as source image, desired size scaling element along the horizontal axis, scaling element along the vertical axis, and interpolation method for resizing.

1.4.1.3 Noise Removal

Image noise is a spontaneous variation of color information or brightness in images created by medical scanners or devices. Noise is commonly described as the unpredictability of a signal caused by random fluctuations [22]. Visual noise can be seen in all medical photos. The presence of noise in a picture causes it to appear dotted, grainy, textured, or snowy. There are many forms of noise, and the most common types of noise seen in medical images are listed [8, 19].

1.4.1.3.1 Salt and Pepper

It appears as a mixture of white and black pixels on the screen. Salt and pepper noise appears in images when rapid fluctuations, such as erroneous changeover, occur [1]. It is also called spike noise or impulsive noise [19].

1.4.1.3.2 Gaussian

Noise that is random is called Gaussian noise. It is a probability density function of a normal distribution, frequently referred to as a Gaussian distribution [1]. Every individual pixel in the image is changed by a low number from its initial amount in Gaussian noise [19].

1.4.1.3.3 Shot or Poisson

The dominant noise in the lighter sections of a picture is shot noise, also known as Poisson noise [1]. Photon shot noise is the numerical quantum variations in the number of photons for a given level of exposure that are normal in imaging devices [19].

1.4.1.3.4 Speckle

It is a granular noise that generally occurs in synthetic aperture radar and active radar images and degrades their accuracy [1].

1.4.1.4 Filtering and Smoothing