1st Edition
Intelligent Systems and Applications in Computer Vision
The book comprehensively covers a wide range of evolutionary computer vision methods and applications, feature selection and extraction for training and classification, and metaheuristic algorithms in image processing. It further discusses optimized image segmentation, its analysis, pattern recognition, and object detection.
Features:
- Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, and quantum computing
- Covers deep learning algorithms in computer vision
- Showcases novel solutions such as multi-resolution analysis in imaging processing, and metaheuristic algorithms for tackling challenges associated with image processing
- Highlight optimization problems such as image segmentation and minimized feature design vector
- Presents platform and simulation tools for image processing and segmentation
The book aims to get the readers familiar with the fundamentals of computational intelligence as well as the recent advancements in related technologies like smart applications of digital images, and other enabling technologies from the context of image processing and computer vision. It further covers important topics such as image watermarking, steganography, morphological processing, and optimized image segmentation. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in fields including electrical engineering, electronics, communications engineering, and computer engineering.
Chapter 1
A Review Approach on Deep Learning Algorithms in Computer Vision
Chapter 2
Object Extraction from Real Time Color Images Using Edge Based Approach
Chapter 3
Deep Learning Techniques for Image Captioning
Chapter 4
Deep Learning Based Object Detection for Computer Vision Tasks: A Survey of Methods & Applications
Chapter 5
Deep Learning Algorithms for Computer Vision: A Deep Insight into Principles and Applications
Chapter 6
Handwritten Equation Solver Using Convolutional Neural Network
Chapter 7
Agriware: Crop Suggester System by Estimating the Soil Nutrient Indicators
Chapter 8
A Machine Learning Based Expeditious Covid-19 Prediction Model Through Clinical Blood Investigations
Chapter 9
Comparison of Image Based and Audio Based Techniques for Bird-Species Identification
Chapter 10
Detection of Ichthyosis Vulgaris Using SVM
Chapter 11
Chest X-Ray Diagnosis and Report Generation: Deep Learning Approach
Chapter 12
Deep Learning Based Automatic Image Caption Generation for Visually Impaired People
Chapter 13
Empirical Analysis of Machine Learning Techniques Under Class Imbalance and Incomplete Datasets
Chapter 14
Gabor Filter As Feature Extractor in Anomaly Detection from Radiology Images
Chapter 15
Discriminative Features Selection from Zernike Moments for Shape Based Image Retrieval System
Chapter 16
Corrected Components of Zernike Moments for Improved Content Based Image Retrieval: A Comprehensive Study
Chapter 17
Translate And Recreate Text in An Image
Chapter 18
Multi-Label Indian Scene Text Language Identification: Benchmark Dataset and Deep Ensemble Baseline
Chapter 19
AI Based Wearables for Healthcare Applications: A Survey of Smart Watches
Chapter 20
Nature Inspired Computing for Optimization
Chapter 21
Automated Smart Billing Cart for Fruits
Biography
Dr. Amit Kant Pandit is working as an Associate Professor and is Ex-Hod, DECE, in Shri Mata Vaishno Devi University (SMVDU), Katra (India). He is a Senior member of IEEE and MIR labs member and has 19 years of academic experience.
Mohamed Abouhawwash received the BSc and MSc degrees in statistics and computer science from Mansoura University, Mansoura, Egypt, in 2005 and 2011, respectively. He finished his Ph.D. in Statistics and Computer Science, 2015, in a channel program between Michigan State University, USA, and Mansoura University, Egypt. He is at Computational Mathematics, Science, and Engineering (CMSE), Biomedical Engineering (BME) and Radiology, Institute for Quantitative Health Science & Engineering (IQ), Michigan State University, East Lansing, MI 48824, USA. He is an Assistant Professor with the Department of Mathematics, Faculty of Science, Mansoura University, Egypt. In 2018, Dr. Abouhawwash is a Visiting Scholar with the Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, Kamloops, BC, Canada. His current research interests include evolutionary algorithms, machine learning, image reconstruction, and mathematical optimization. Dr. Abouhawwash was a recipient of the best master’s and Ph.D. thesis awards from Mansoura University in 2012 and 2018, respectively.
Dr. Shubham Mahajan (Member, IEEE, ACM) received the B.Tech. degree from Baba Ghulam Shah Badshah University, the M.Tech. degree from Chandigarh University and Ph.D. degree with Shri Mata Vaishno Devi University (SMVDU), Katra, India. He is working as a Assistant Professor with Ajeenkya D Y Patil University, Pune. He has eight Indian, one Australian, one German Patent to his credit in the area of artificial intelligence and image processing. He has authored/coauthored more than 50 publications including peer-reviewed journals and conferences. His main research interests include image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods with applications in optimization, data mining, machine learning, robotics, and optical communication, and also received the ‘Best Research Paper Award’ from ICRIC 2019 (Springer, LNEE) and also received the Best Student Award-2019, IEEE Region-10 Travel Grant Award-2019, 2nd runner up prize in IEEE RAS HACKATHON-2019 (Bangladesh) and IEEE Student Early Researcher Conference Fund (SERCF-2020), Emerging Scientist Award-2021, and IEEE Signal Processing Society Professional Development Grant-2021. He was a Campus Ambassador for IEEE, IIT Bombay, Kanpur, Varanasi, Delhi and various MNC’s. He is constantly looking for collaboration opportunities with foreign professors and students.
