Advance Concepts of Image Processing and Pattern Recognition. Effective Solution for Global Challenges

Preface
Contents
About the Editors
1 Hybrid Evolutionary Technique for Contrast Enhancement of Color Images
	1.1 Introduction
	1.2 Evolutionary Techniques Background
		1.2.1 Artificial Bee Colony (ABC) Technique
		1.2.2 Cuckoo Search Algorithm (CSA)
	1.3 Proposed Hybrid Image Contrast Enhancement Technique
	1.4 Results and Discussion
	1.5 Image Quality Measurement
	1.6 Image Error Measurement
	1.7 Conclusion
	References
2 Computer Vision for Agro-Foods: Investigating a Method for Grading Rice Grain Quality in Sri Lanka
	2.1 Introduction
	2.2 Literature Review
	2.3 Materials and Methods
	2.4 Results
	2.5 Conclusion
	References
3 A Study on Image Restoration and Analysis
	3.1 Introduction
	3.2 Image Restorations and Analysis
		3.2.1 Basic Requirement for Image Restoration and Analysis
		3.2.2 Noise Models in Image
		3.2.3 Spatial and Frequency Properties
	3.3 Operation of Probability Density Function
		3.3.1 Gaussian Noise Model
		3.3.2 Rayleigh Noise Model
		3.3.3 Erlang (Gamma) Noise Model
		3.3.4 Exponential Noise Distribution
		3.3.5 Impulse (Salt and Pepper) Noise Model
	3.4 Problems in Image Enhancement
	3.5 Methods Used in Image Restoration
		3.5.1 Inverse Filtering
		3.5.2 Weiner Filter
		3.5.3 Algorithm for Using Weiner Filter
		3.5.4 Lucy–Richardson Algorithm
		3.5.5 Lucy–Richardson Algorithm
		3.5.6 Regularized Filter Used in Image Restoration
		3.5.7 Algorithm for Regularized Filter Implement on Sample Image
	3.6 Blind Deconvolution for Blur Image
		3.6.1 Blind Deconvolution Algorithms for Sample Image
		3.6.2 Methodologies Implemented on Blur and Noisy Sample Image
		3.6.3 Wavelet Transformation in 2-D
		3.6.4 Estimation Method of Signal-to-Noise Ratio (SNR)
		3.6.5 Estimation of the Gaussian Point Spread Function (PSF)
		3.6.6 Comparison of Mean Square Error (MSE)
	3.7 Analysis of the Image Restoration Methods
		3.7.1 Mean Square Error (MSE) Comparison
		3.7.2 Peak-Signal-to-Noise Ratio (Peak-SNR) Comparison
		3.7.3 Signal-to-Noise Ratio (SNR) Comparison
	3.8 Conclusion and Feature Work
	References
4 Application of Deep Learning and Machine Learning in Pattern Recognition
	4.1 Introduction
	4.2 Literature Review
	4.3 Pattern Recognition (PR) Problem
		4.3.1 Pattern Recognition (PR) Process
		4.3.2 Loop-Back Routes Between Stages
		4.3.3 Training Data, Testing Data, and Algorithms
	4.4 Artificial Intelligence Techniques for Pattern Recognition
		4.4.1 Machine Learning (ML) Techniques
		4.4.2 Deep Learning (DL) Techniques
	4.5 Component of Pattern Recognition (PR) System in Real World
	4.6 Scope and Applications of PR in Different Domains
	4.7 Important PR Tools Used in Recent Times
	4.8 Summary and Conclusion
	References
5 Brain Tumor Classification Using Hybrid Artificial Neural Network with Chicken Swarm Optimization Algorithm in Digital Image Processing Application
	5.1 Introduction
	5.2 Literature Review
	5.3 System Design
		5.3.1 Preprocessing
		5.3.2 Segmentation
		5.3.3 Feature Extraction
		5.3.4 Classification
	5.4 Result and Discussion
	5.5 Conclusion
	5.6 Research Scope
	References
6 Detection and Classification of Breast Cancer Using CNN
	6.1 Introduction
	6.2 Literature Survey
	6.3 Methodology
		6.3.1 Dataset Description
		6.3.2 System Architecture
		6.3.3 Data Collection
		6.3.4 Preprocessing
		6.3.5 CNN Model Design
		6.3.6 Training and Testing
	6.4 Result and Discussion
	6.5 Conclusion
	References
7 De-Noising of Poisson Noise Corrupted CT Images by Using Modified Anisotropic Diffusion-Based PDE Filter
	7.1 Introduction
	7.2 General Frame for CT Image Restoration
		7.2.1 MAP Methodology
		7.2.2 Minimization Framework
		7.2.3 Methods and Models
		7.2.4 Anisotropic Diffusion (Yu and Acton 2002)-Based Method
		7.2.5 Digitization of the Proposed Model
	7.3 Results and Discussions
	7.4 Conclusion
	References
8 Computer-Aided Diabetic Retinopathy Diagnosis Using Conventional and Deep Learning Techniques—A Comparison
	8.1 Introduction
	8.2 Deep Learning
		8.2.1 Deep Learning Applications
		8.2.2 Deep Learning in Medical Image Processing
		8.2.3 Convolutional Neural Network (CNN)
	8.3 Diabetic Eye Diseases
		8.3.1 Diabetic Retinopathy
		8.3.2 Diabetic Macular Edema (DME)
		8.3.3 Glaucoma
		8.3.4 Cataracts
	8.4 A Review on Retinal Image Databases
	8.5 Diagnosing Diabetic Eye Diseases
		8.5.1 Diagnosing Without Deep Learning Techniques
		8.5.2 Diagnosing with Deep Learning Techniques
	8.6 Statistical Comparisons on with and Without Using Deep Learning Techniques
	8.7 Future Directions
	8.8 Conclusion
	References
9 Speckle Reduction in Ultrasound Images Using Hybridization of Wavelet-Based Novel Thresholding Approach with Guided Filter
	9.1 Introduction
	9.2 Literature Survey
	9.3 Wavelet Thresholding
	9.4 Guided Filter
	9.5 Proposed Hybrid Method
	9.6 Experimental Setup
		9.6.1 Synthetic Images (Test Image-1)
		9.6.2 Kidney Phantom (Test Image-2) and Cyst Phantom (Test Image-3)
		9.6.3 Real Ultrasound Images (Test Image-4)
	9.7 Image Quality Metrics
	9.8 Experiment Results and Discussions for Synthetic Images (Test Image-1)
	9.9 Experiment Results for Kidney Phantom (Test Image-2)
	9.10 Experiment Results and Discussions for Cyst Phantom (Test Image-4)
	9.11 Experiment Results and Discussions for Real Ultrasound Images (Test Image-4)
	9.12 Conclusion
	References
10 Poisson Noise-Adapted Total Variation-Based Filter for Restoration and Enhancement of Mammogram Images
	10.1 Introduction
	10.2 Numerical Result
	10.3 Results
	10.4 Conclusion
	References
11 Implementation of Mathematical Morphology Technique in Binary and Grayscale Image
	11.1 Introduction
	11.2 System Model
		11.2.1 Dilation
		11.2.2 Erosion
		11.2.3 Opening and Closing
	11.3 Simulation Results
	11.4 Conclusion
	References
12 Design of Advanced Security System Using Vein Pattern Recognition and Image Segmentation Techniques
	12.1 Introduction
	12.2 Related Works
	12.3 Methodology
	12.4 Proposed System
	12.5 Results and Discussion
	12.6 Conclusion
	References