Intelligent Fractal-Based Image Analysis
Applications in Pattern Recognition and Machine Vision
- 1st Edition - May 27, 2024
- Imprint: Academic Press
- Editors: Soumya Ranjan Nayak, Janmenjoy Nayak, Khan Muhammad, Yeliz Karaca
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 6 8 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 6 9 - 7
Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applicati… Read more
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Request a sales quoteIntelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlights the relevance of related application areas for advanced as well as novice-user application. The book presents core concepts, methodological aspects, and advanced feature opportunities, focusing on major, real-time applications in engineering and health science. It will appeal to researchers, data scientists, industry professionals, and graduate students.
Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis.
- Investigates advanced fractal theories spanning neural networks, fuzzy logic, machine learning, deep learning, and hybrid intelligent systems in solving pattern recognition problems
- Explores the application of fractal theories to a wide range of medical image processing modalities
- Presents case studies that illustrate the application and integration of fractal theories into intelligent computing in the resolution of important pattern recognition and machine vision problems
Researcher data scientists, industry professionals, and graduate students in the field of fractal graphics and its related applications
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Foreword
- Preface
- Acknowledgments
- Part I: Intelligent fractal-based image analysis
- Chapter 1: A deep insight into intelligent fractal-based image analysis with pattern recognition
- Abstract
- 1.1. Introduction
- 1.2. Approaches and applications of pattern recognition
- 1.3. Various approaches of image analysis
- 1.4. Fractal based image analysis
- 1.5. Critical analysis
- 1.6. Conclusion
- References
- Chapter 2: Analysis of Mandelbrot set fractal images using a machine learning based approach
- Abstract
- 2.1. Introduction
- 2.2. Related works
- 2.3. Methodology
- 2.4. Results and discussion
- 2.5. Conclusions
- References
- Chapter 3: Chaos-based image encryption
- Abstract
- 3.1. Chaos-based image encryption studies and encryption/decryption processes
- 3.2. The evaluation methods
- 3.3. The security evaluation of chaos-based image encryption
- 3.4. A case study: chaos-based medical image encryption
- References
- Chapter 4: Fractal feature based image classification
- Abstract
- 4.1. Introduction
- 4.2. Related works
- 4.3. Materials and method
- 4.4. Experimental analysis
- 4.5. Conclusions
- References
- Part II: Recognition model using fractal features
- Chapter 5: The study of source image and its futuristic quantum applications: an insight from fractal analysis
- Abstract
- Acknowledgements
- 5.1. Introduction
- 5.2. Source description and correlation functions
- 5.3. Methods explanation for coherent droplets
- 5.4. Formulation within partially chaos emission
- 5.5. Model results and discussion
- 5.6. Summary and conclusions
- References
- Chapter 6: Deep CNNS and fractal-based sequence learning for violence detection in surveillance videos
- Abstract
- 6.1. Introduction
- 6.2. Violence detection methods
- 6.3. Discussion on experimental results
- 6.4. Challenges and future research guidelines
- 6.5. Conclusion
- References
- Chapter 7: Wavelets for anisotropic oscillations in nanomaterials
- Abstract
- Acknowledgement
- 7.1. Introduction
- 7.2. On the regularity of functions
- 7.3. Some formal calculus of oscillating singularities
- 7.4. Wavelet characterization of the regularity
- 7.5. An application on nanomaterials
- 7.6. Conclusions
- 7.7. Appendix – Wavelet toolkit review
- References
- Chapter 8: Comparative analysis of approaches to optimize fractal image compression
- Abstract
- 8.1. Introduction
- 8.2. Theoretical framework
- 8.3. Factor affecting FIC
- 8.4. Approaches for accelerate FIC
- 8.5. Summary and analysis of review
- 8.6. Conclusions
- References
- Part III: Fractals in disease identification and control
- Chapter 9: Alzheimer disease (AD) medical image analysis with convolutional neural networks
- Abstract
- 9.1. Introduction
- 9.2. Brain amyloidosis and medical imaging
- 9.3. CNNs in the field of medicine and their basic architecture
- 9.4. Brain medical imaging and CNN
- References
- Chapter 10: An intelligent fractal-dimension-based model for brain-tumor MRI analysis
- Abstract
- 10.1. Introduction
- 10.2. Background
- 10.3. Dataset
- 10.4. Proposed algorithm
- 10.5. Results and experiment
- 10.6. Conclusions
- References
- Chapter 11: Fractal dimension analysis using hybrid RDBC and IDBC for gray scale images
- Abstract
- 11.1. Introduction
- 11.2. Literature review
- 11.3. Proposed methodology
- 11.4. Experimental results
- 11.5. Conclusion
- References
- Chapter 12: Preliminary analysis and survey of retinal disease diagnosis through identification and segmentation of bright and dark lesions
- Abstract
- 12.1. Introduction
- 12.2. Retinal imaging modalities
- 12.3. Fundus imaging
- 12.4. Eye anatomy and retinal diseases
- 12.5. Current challenges and needs in computer aided retinal disease analysis
- 12.6. State-of-the-art for retinal disease diagnosis
- 12.7. Conclusions
- References
- Index
- Edition: 1
- Published: May 27, 2024
- No. of pages (Paperback): 318
- No. of pages (eBook): 350
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443184680
- eBook ISBN: 9780443184697
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Soumya Ranjan Nayak
Dr. Soumya Ranjan Nayak now holds the position of Assistant Professor in the School of Computer Engineering at Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, located in Odisha, India. He obtained a Doctor of Philosophy (Ph.D) and Master of Technology (M.Tech) in Computer Science and Engineering under a scholarship provided by the Ministry of Human Resource Development (MHRD) of the Government of India. These degrees were earned at CET, BPUT Rourkela, India. Prior to this, he completed a Bachelor of Technology (B. Tech) and a Diploma in Computer Science and Engineering. He has authored over 150 articles that have been published in reputable international journals and conferences such as Elsevier, Springer, World Scientific, IOS Press, Taylor & Francis, Hindawi, Inderscience, IGI Global, and others. These publications have undergone a rigorous peer-review process. In addition to the aforementioned accomplishments, the individual has authored 16 book chapters, published 6 books, and obtained 7 Indian patents (with 4 patents being granted). Furthermore, they have secured 4 International patents, all of which have been granted. The researcher's current areas of focus encompass medical picture analysis and classification, machine learning, deep learning, pattern recognition, fractal graphics, and computer vision. The author's writings have garnered over 1500 citations, with an h-index of 24 and an i10-index of 63, as reported by Google Scholar. Dr. Nayak holds the position of an associate editor for several esteemed academic journals, including the Journal of Electronic Imaging (SPIE), Mathematical Problems in Engineering (Hindawi), Journal of Biomedical Imaging (Hindawi), Applied Computational Intelligence and Soft Computing (Hindawi), and PLOS One. He is currently fulfilling the role of a guest editor for special issues of renowned academic journals such as Springer Nature, Elsevier, and Taylor & Franchise. He has been affiliated as a reviewer for numerous esteemed peer-reviewed journals, including Applied Mathematics and Computation, Journal of Applied Remote Sensing, Mathematical Problems in Engineering, International Journal of Light and Electron Optics, Journal of Intelligent and Fuzzy Systems, Future Generation Computer Systems, Pattern Recognition Letters, and others. He has additionally held the Technical Program Committee Member position for several conferences of significant worldwide recognition.
Affiliations and expertise
Assistant Professor, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, IndiaJN
Janmenjoy Nayak
Janmenjoy Nayak is an Assistant Professor, P.G. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha, India. He has been a Gold Medallist in Computer Science twice in his career, and has been awarded the “Innovation in Science Pursuit for Inspired Research” (INSPIRE) Research Fellowship from the Department of Science & Technology, Government of India (at both Junior Research Fellow and Senior Research Fellow level) and Best Researcher Award from Jawaharlal Nehru University of Technology, Kakinada, Andhra Pradesh for the academic year 2018–19. He has received many other awards from national and international academic agencies. Dr. Nayak has edited 19 books and 8 special issues on the applications of computational intelligence, soft computing, and pattern recognition, which have been published by Springer and Inderscience. He has published more than 190 refereed articles in various book chapters, conferences, and peer-reviewed journals of Elsevier, Inderscience, Springer, the Institute of Electrical and Electronics Engineers (IEEE), and others. He has also served as a reviewer for more than 100 journals and conferences produced by the IEEE, the Association for Computing Machinery (ACM), Springer, Elsevier, Wiley, and Inderscience. He has 11 years of experience in both teaching and research. Dr. Nayak is a senior member of the IEEE and a life member of societies such as the Soft Computing Research Society (SCRS), the Computer Society of India (CSI India), the Orissa Information Technology Society (OITS), the Orissa Mathematical Society (OMS), and the International Association of Engineers (IAENG), Hong Kong. He has successfully conducted and is associated with 14 internationally renowned series conferences such as ICCIDM, HIS, ARIAM, CIPR, and SCDA. His areas of interest include data mining, nature-inspired algorithms, and soft computing.
Affiliations and expertise
Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCBD) University, Mayurbhanj, IndiaKM
Khan Muhammad
Khan Muhammad received his PhD in Digital Contents from Sejong University, South Korea in February 2019. He was an Assistant Professor in the Department of Software, Sejong University from March 2019 to February 2022. He is currently the director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) and an Assistant Professor (Tenure-Track) in the Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, South Korea. His research interests include intelligent video surveillance, medical image analysis, information security, video summarization, multimedia data analysis, computer vision, and smart cities. He has registered 10 patents and contributed more than 220 papers in peer-reviewed journals and conference proceedings in his research areas. He is an Associate Editor/Editorial Board Member for more than 15 journals. He was among the most highly cited researchers in 2021 and 2022, according to the Web of Science (Clarivate).
Affiliations and expertise
Assistant Professor, Department of Interaction Science and Director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Sungkyunkwan University, Seoul, South KoreaYK
Yeliz Karaca
Yeliz Karaca is an Assistant Professor of Applied Mathematics, and a researcher at the University of Massachusetts (UMass) Chan Medical School, Worcester, USA. She received her Ph.D. degree in Mathematics in 2012. Along with the other awards she has been conferred, she was granted the Cooperation in Neurological Sciences and Support Award by Turkish Neurology Association as the first mathematician in Turkey. She also holds a medical card as the only mathematician entitled for it. Furthermore, she received the Outstanding Young Scientist Award in 2012 and Best Paper Awards in her specialized discipline, among the other national and international awards in different categories as well as grants. Another award of hers is Outstanding Reviewer Award (Mathematics Journal, MDPI) in 2021. She is the Editor-in-Chief of the book series named Systems Science & Nonlinear Intelligence Dynamics by World Scientific. Dr. Karaca has been acting as the lead editor, editor and associate editor in many different SCI indexed journals. She also has active involvement with diverse projects, some of which are Institute of Electrical and Electronics Engineers (IEEE, as senior member), Organization for Women in Science for the Developing World (OWSD); Complex Human Adaptive Organizations and Systems (CHAOS)- University of Perugia, Italy; International Engineering and Technology Institute (IETI, as the member of Board of Director). Her research interests include complex systems sciences with applications in various terrains, applied mathematics, advanced computational methods, AI applications, computational complexity, fractional calculus, fractals and multifractals, stochastic processes, different kinds of differential and difference equations, discrete mathematics, algebraic complexity, complexity science, wavelet and entropy, solutions of advanced mathematical challenges, mathematical neuroscience and biology as well as advanced data analysis in medicine and other related theoretical, computational and applied domains.
Affiliations and expertise
Assistant Professor of Applied Mathematics and Researcher, University of Massachusetts (UMass) Medical School, Worcester, Massachusetts, USA
Affiliations and expertise
Assistant Professor of Applied Mathematics and Researcher, University of Massachusetts Medical School, Worcester, Massachusetts, USARead Intelligent Fractal-Based Image Analysis on ScienceDirect