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OpenCV with Python Blueprints

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Learn
  • Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning
  • Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
  • Learn feature extraction and feature matching for tracking arbitrary objects of interest
  • Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
  • Track visually salient objects by searching for and focusing on important regions of an image
  • Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)
  • Recognize street signs using a multi-class adaptation of support vector machines (SVMs)
  • Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features
About

OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions

This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.

By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.

Features
  • Program advanced computer vision applications in Python using different features of the OpenCV library
  • Practical end-to-end project covering an important computer vision problem
  • All projects in the book include a step-by-step guide to create computer vision applications
Page Count 230
Course Length 6 hours 54 minutes
ISBN9781785282690
Date Of Publication 18 Oct 2015

Authors

Michael Beyeler

Michael Beyeler is a Postdoctoral Fellow at the University of Washington in Seattle. His work lies at the intersection of neuroscience, computer vision, and machine learning. Michael is the author of two Packt books: OpenCV with Python Blueprints (2015) and Machine Learning for OpenCV (2017). He is an active contributor to several open-source software projects and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. His technical blog can be found at www.askaswiss.com.