Hands-On Image Processing with Python

Explore mathematical computations and algorithms for image processing using popular Python tools and frameworksKey FeaturesGain practical knowledge of every image processing task with popular Python librariesExplore topics such as pseudo-coloring, noise smoothing, and computing image descriptorsCover popular machine learning and deep learning techniques for complex image processing tasksBook DescriptionImage processing plays an important role in our daily lives with various applications in social media (face detection), medical imaging (X-rays and CT scans), and security (fingerprint recognition). This book is designed to help you learn the core aspects of image processing, from essential concepts to code using the Python programming language.The book starts by covering classical image processing techniques. You'll then go on to explore the evolution of image processing algorithms, right up to the recent advancements in image processing and computer vision with deep learning. As you progress, you'll learn how to use image processing libraries such as PIL, scikit-image, and scipy ndimage in Python. The book will further enable you to write code snippets in Python 3 and implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. You'll gradually be able to implement machine learning models using the Python library, scikit-learn. In addition to this, you'll explore deep convolutional neural networks (CNNs), such as VGG-19 with Keras, before progressing to use an end-to-end deep learning model called YOLO for object detection. Later chapters will take you through a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.By the end of this book, you'll have learned how to implement various algorithms for efficient image processing.What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency Domain Filters such as Weiner in PythonPerform morphological image processing and segment images with different algorithmsGet to grips with techniques for extracting features from images and matching imagesWrite Python code to implement supervised machine learning and unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is forThis image processing handbook is for computer vision engineers and machine learning developers who are well-versed in Python programming and want to delve into the various aspects and complexities of image processing. No prior knowledge of image processing techniques is required.Table of ContentsGetting started with Image Processing Sampling Fourier TransformConvolution and Frequency domain FilteringImage EnhancementImage Enhancement using DerivativesMorphological Image ProcessingExtracting Image Features and DescriptorsImage SegmentationClassical Machine Learning Methods Learning in Image Processing - Image Classification with CNNObject Detection, Deep Segmentation and Transfer Learning Additional Problems in Image Processing