Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data

Sandipan Dey

商品描述

Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.

Key Features

  • Practical coverage of every image processing task with popular Python libraries
  • Includes topics such as pseudo-coloring, noise smoothing, computing image descriptors
  • Covers popular machine learning and deep learning techniques for complex image processing tasks

Book Description

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.

The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.

By the end of this book, we will have learned to implement various algorithms for efficient image processing.

What you will learn

  • Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python
  • Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python
  • Do morphological image processing and segment images with different algorithms
  • Learn techniques to extract features from images and match images
  • Write Python code to implement supervised / unsupervised machine learning algorithms for image processing
  • Use deep learning models for image classification, segmentation, object detection and style transfer

Who this book is for

This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.

Table of Contents

  1. Getting started with Image Processing
  2. Sampling Fourier Transform
  3. Convolution and Frequency domain Filtering
  4. Image Enhancement
  5. Image Enhancement using Derivatives
  6. Morphological Image Processing
  7. Extracting Image Features and Descriptors
  8. Image Segmentation
  9. Classical Machine Learning Methods
  10. Learning in Image Processing - Image Classification with CNN
  11. Object Detection, Deep Segmentation and Transfer Learning
  12. Additional Problems in Image Processing

商品描述(中文翻譯)

探索使用流行的Python工具和框架進行圖像處理的數學計算和算法。

主要特點



  • 使用流行的Python庫實踐每個圖像處理任務

  • 包括偽彩色、噪聲平滑、計算圖像描述符等主題

  • 涵蓋複雜圖像處理任務的流行機器學習和深度學習技術

書籍描述


圖像處理在我們的日常生活中扮演著重要角色,應用廣泛,包括社交媒體(人臉檢測)、醫學影像(X射線、CT掃描)、安全(指紋識別)以及機器人和太空等領域。本書將使用Python從概念到代碼深入探討圖像處理的核心。

本書將從傳統的圖像處理技術開始,探索圖像處理算法的演進,直到最近的深度學習或計算機視覺的進展。我們將學習如何在Python中使用圖像處理庫,如PIL、scikit-image和scipy ndimage。本書將使我們能夠使用Python 3編寫代碼片段,快速實現複雜的圖像處理算法,如圖像增強、濾波、分割、目標檢測和分類。我們將能夠使用scikit-learn庫使用機器學習模型,並進一步探索使用Keras的深度卷積神經網絡(如VGG-19),還將使用一個名為YOLO的端到端深度學習模型進行目標檢測。我們還將涵蓋一些高級問題,如圖像修補、梯度混合、變分降噪、縫合、拼貼和變形。

通過閱讀本書,我們將學習實現各種高效圖像處理算法。

你將學到什麼



  • 在Python中執行基本的數據預處理任務,如圖像去噪和空間濾波

  • 在Python中實現快速傅立葉變換(FFT)和頻域濾波器(如Weiner)

  • 進行形態學圖像處理,並使用不同的算法對圖像進行分割

  • 學習從圖像中提取特徵並進行圖像匹配的技術

  • 使用Python編寫代碼實現監督/非監督機器學習算法進行圖像處理

  • 使用深度學習模型進行圖像分類、分割、目標檢測和風格轉換

本書適合對象


本書適合計算機視覺工程師和機器學習開發人員,他們擅長Python編程,並希望探索圖像處理的細節和複雜性。不需要先備的圖像處理技術知識。

目錄



  1. 開始圖像處理

  2. 取樣傅立葉變換

  3. 卷積和頻域濾波

  4. 圖像增強

  5. 使用導數進行圖像增強

  6. 形態學圖像處理

  7. 提取圖像特徵和描述符

  8. 圖像分割

  9. 經典機器學習方法

  10. 圖像處理中的學習-使用CNN進行圖像分類

  11. 目標檢測、深度分割和遷移學習

  12. 圖像處理中的其他問題