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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

By : Millán Escrivá, Vinícius G. Mendonça, Joshi
2.5 (2)
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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

2.5 (2)
By: Millán Escrivá, Vinícius G. Mendonça, Joshi

Overview of this book

OpenCV is one of the best open source libraries available, and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module. By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Table of Contents (14 chapters)
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Face detection with SSD

Single Shot Detection (SSD) is another fast and accurate deep learning object-detection method with a similar concept to YOLO, in which the object and bounding box are predicted in the same architecture.

SSD model architecture

The SSD algorithm is called single shot because it predicts the bounding box and the class simultaneously as it processes the image in the same deep learning model. Basically, the architecture is summarized in the following steps:

  1. A 300 x 300 image is input into the architecture.
  2. The input image is passed through multiple convolutional layers, obtaining different features at different scales.
  3. For each feature map obtained in 2, we use a 3 x 3 convolutional filter to evaluate...

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