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Python Image Processing Cookbook

Python Image Processing Cookbook

By : Sandipan Dey
2 (2)
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Python Image Processing Cookbook

Python Image Processing Cookbook

2 (2)
By: Sandipan Dey

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)
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Classifying images with scikit-learn (HOG and logistic regression)

In this recipe, you are going to implement a feature-based image classifier using the scikit-image and scikit-learn library functions. A multiclass logistic regression (softmax regression) classifier will be trained on the histogram of oriented gradients (HOG) descriptors extracted from the training images. The following equations show how the parameters for a K-class softmax regression classifier are estimated in the training phase (for example, with stochastic gradient descent) and then the model that is learned is used to predict the probability of a class label given an input image in the testing phase:

Getting ready

In this recipe, we will classify images...

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