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Hands-On Image Processing with Python

Hands-On Image Processing with Python

By : Sandipan Dey
3 (5)
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Hands-On Image Processing with Python

Hands-On Image Processing with Python

3 (5)
By: Sandipan Dey

Overview of this book

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.
Table of Contents (20 chapters)
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Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Transfer learning – what it is, and when to use it


Transfer learning is a deep learning strategy that reuses knowledge gained from solving one problem by applying it to a different, but related, problem. For example, let's say we have three types of flowers, namely, a rose, a sunflower, and a tulip. We can use the standard pre-trained models, such as VGG16/19, ResNet50, or InceptionV3 models (pre-trained on ImageNet with 1000 output classes, which can be found at https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) to classify the flower images, but our model wouldn't be able to correctly identify them because these flower categories were not learned by the models. In other words, they are classes that the model is not aware of.

The following image shows how the flower images are classified wrongly by the pre-trained VGG16 model (the code is left to the reader as an exercise):

Transfer learning with Keras

Training of pre-trained models is done on many comprehensive image classification problems...

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