Short description:

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.

Long description:

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.

Table of Contents:

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