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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (12 chapters)
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Video Classification

In this chapter, we will see how to train deep learning models for video data. We will start classifying videos on a frame basis. Then, we will use the temporal information for better accuracy. Later, we will extend the applications of images to videos, including pose estimation, captioning, and generating videos.

We will cover the following topics in this chapter:

  • The datasets and the algorithms of video classification
  • Splitting a video into frames and classifying videos
  • Training a model for visual features on an individual frame level 0
  • Understanding 3D convolution and its use in videos
  • Incorporating motion vectors on video
  • Object tracking utilizing the temporal information
  • Applications such as human pose estimation and video captioning
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