In this chapter, we discussed a few advanced deep learning applications to solve a few complex image processing problems. We started with basic concepts in image classification with localization and object detection. Then we demonstrated how a popular YOLO v2 FCN pre-trained model can be used to detect objects in images and draw boxes around them. Next, we discussed the basic concepts in semantic segmentation and then demonstrated how to use DeepLab v3+ (along with a summary on its architecture) to perform semantic segmentation of an image. Then we defined transfer learning and explained how and when it is useful in deep learning, along with a demonstration on transfer learning in Keras to classify flowers with a pre-trained VGG16 model. Finally, we discussed how to generate novel artistic images with deep neural style transfer, and demonstrated this with Python and OpenCV and a pre-trained Torch model. You should be familiar with how to use pre-trained deep learning models to solve...
Hands-On Image Processing with Python
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Hands-On Image Processing with Python
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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)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Free Chapter
Getting Started with Image Processing
Sampling, Fourier Transform, and Convolution
Convolution and Frequency Domain Filtering
Image Enhancement
Image Enhancement Using Derivatives
Morphological Image Processing
Extracting Image Features and Descriptors
Image Segmentation
Classical Machine Learning Methods in Image Processing
Deep Learning in Image Processing - Image Classification
Deep Learning in Image Processing - Object Detection, and more
Additional Problems in Image Processing
Other Books You May Enjoy
Index
Customer Reviews