Advanced Image Processing with Python and OpenCV, Chesterfield G., 2024

Advanced Image Processing with Python and OpenCV, Chesterfield G., 2024.
     
    Image processing is a powerful technology that has evolved significantly over the past few decades. It involves the manipulation of digital images through various algorithms to enhance or extract information from them. At its core, image processing can be defined as the process of converting an image into a form that is suitable for analysis. This transformation is often necessary because raw images captured by cameras can be noisy, distorted, or lack the clarity needed for various applications.




Understanding Pixels and Color Spaces.
At the core of image processing lies the concept of pixels. A pixel, short for "picture element," is the smallest unit of a digital image and represents a single point in the image. Each pixel holds specific information regarding its color and intensity. In a grayscale image, the pixel value ranges from 0 (black) to 255 (white), where intermediate values represent varying shades of gray.

Color images, on the other hand, are typically represented using the RGB color model, which consists of three color channels: red, green, and blue. Each pixel is defined by a triplet of values, with each value ranging from 0 to 255. For example, the color white is represented as (255, 255, 255), while black is (0, 0, 0). By manipulating the intensity of these channels, a wide spectrum of colors can be created.

CONTENTS.
Chapter 1: Introduction to Image Processing and Computer Vision.
Overview of Image Processing.
The Role of Computer Vision.
Applications in Various Fields.
Introduction to OpenCV and Python.
Chapter 2: Setting Up Your Environment.
Installing Python and OpenCV.
Configuring IDEs and Libraries.
Understanding OpenCV Basics.
Chapter 3: Image Basics and Fundamentals.
Understanding Pixels and Color Spaces.
Image Formats and File Types.
Image Acquisition Techniques.
Image Processing Operations.
The Importance of Preprocessing.
Chapter 4: Image Filtering and Enhancement.
Understanding Image Filtering Techniques.
Image Enhancement Techniques.
Implementing Filters and Enhancements with OpenCV.
Chapter 5: Geometric Transformations.
Introduction to Geometric Transformations.
Types of Geometric Transformations.
Applications of Geometric Transformations.
Implementing Geometric Transformations with OpenCV.
Chapter 6: Image Segmentation Techniques.
Introduction to Image Segmentation.
Thresholding Techniques.
Clustering Techniques.
Edge-Based Segmentation.
Region-Based Segmentation.
Deep Learning Approaches.
Applications of Image Segmentation.
Implementing Image Segmentation with OpenCV.
Chapter 7: Feature Detection and Description.
Introduction to Feature Detection and Description.
Importance of Feature Detection.
Common Feature Detection Algorithms.
Feature Description.
Matching Features.
Applications of Feature Detection and Description.
Implementing Feature Detection and Description with OpenCV.
Chapter 8: Object Detection and Recognition.
Introduction to Object Detection and Recognition.
Key Concepts in Object Detection.
Traditional Object Detection Techniques.
Deep Learning for Object Detection.
Evaluation Metrics for Object Detection.
Applications of Object Detection and Recognition.
Implementing Object Detection with OpenCV and Deep Learning.
Chapter 9: Image Segmentation Techniques.
Introduction to Image Segmentation.
Key Concepts in Image Segmentation.
Traditional Image Segmentation Techniques.
Deep Learning-Based Image Segmentation.
Evaluation Metrics for Image Segmentation.
Applications of Image Segmentation.
Implementing Image Segmentation with OpenCV and Deep Learning.
Chapter 10: Feature Extraction and Representation.
Introduction to Feature Extraction.
Importance of Feature Extraction.
Types of Features in Image Processing.
Feature Extraction Techniques.
Dimensionality Reduction Techniques.
Applications of Feature Extraction.
Chapter 11: Image Classification Techniques.
Introduction to Image Classification.
Traditional Image Classification Methods.
Deep Learning for Image Classification.
Image Classification Applications.
Model Optimization Techniques.
Challenges in Image Classification.
Chapter 12: Object Detection Techniques.
Introduction to Object Detection.
Traditional Object Detection Methods.
Deep Learning Approaches to Object Detection.
Object Detection Frameworks.
Applications of Object Detection.
Challenges in Object Detection.
Future Trends in Object Detection.
Chapter 13: Image Segmentation Techniques.
Introduction to Image Segmentation.
Traditional Image Segmentation Techniques.
Deep Learning Approaches to Image Segmentation.
Applications of Image Segmentation.
Challenges in Image Segmentation.
Future Trends in Image Segmentation.
Chapter 14: Image Recognition and Classification.
Introduction to Image Recognition and Classification.
Traditional Image Recognition and Classification Techniques.
Deep Learning Approaches to Image Recognition and Classification.
Applications of Image Recognition and Classification.
Challenges in Image Recognition and Classification.
Future Trends in Image Recognition and Classification.
Chapter 15: Object Detection Techniques in Computer Vision.
Introduction to Object Detection.
Traditional Object Detection Techniques.
Deep Learning Approaches to Object Detection.
Applications of Object Detection.
Challenges in Object Detection.
Future Trends in Object Detection.
Conclusion.
Chapter 16: Image Segmentation Techniques in Computer Vision.
Introduction to Image Segmentation.
Traditional Image Segmentation Techniques.
Deep Learning Approaches to Image Segmentation.
Applications of Image Segmentation.
Challenges in Image Segmentation.
Future Trends in Image Segmentation.
Chapter 17: Future Trends in Image Processing and Computer Vision.
Introduction.
Advances in Deep Learning Techniques.
Real-Time Processing and Edge Computing.
Multi-Modal Learning.
Explainability and Interpretability.
Advancements in Image Processing Applications.
The Role of Open Source and Collaborative Development.



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