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HANDS-ON IMAGE PROCESSING WITH PYTHON

By: Material type: TextTextPublication details: UK PACK PUBLISHING LTD 2018Description: ix, 474ISBN:
  • 978-1-78934-373-1
Subject(s): DDC classification:
  • 006.7/DEY
Summary: 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.
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Holdings
Item type Current library Call number Status Notes Date due Barcode
Books Books Symbiosis Institute of Computer Studies and Research Python 006.7/DEY (Browse shelf(Opens below)) Available COMPUTERS VISION & PATTERN RECOGNITION, PROGRAMMING LANGUAGES -PYTHON, PATTERN RECOGNITION, COMPUTERS - IMAGE PROCESSING SICSR-B-19552
Books Books Symbiosis Institute of Computer Studies and Research Python 006.7/DEY (Browse shelf(Opens below)) Available COMPUTERS VISION & PATTERN RECOGNITION, PROGRAMMING LANGUAGES -PYTHON, PATTERN RECOGNITION, COMPUTERS - IMAGE PROCESSING SICSR-B-19553

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.

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