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Deep Learning for Computer Vision

You're reading from   Deep Learning for Computer Vision Expert techniques to train advanced neural networks using TensorFlow and Keras

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788295628
Length 310 pages
Edition 1st Edition
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Author (1):
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Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
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Table of Contents (12) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Image Classification 3. Image Retrieval 4. Object Detection 5. Semantic Segmentation 6. Similarity Learning 7. Image Captioning 8. Generative Models 9. Video Classification 10. Deployment 11. Other Books You May Enjoy

Algorithms for semantic segmentation


There are several deep learning-based algorithms that were proposed to solve image segmentation tasks. A sliding window approach can be applied at a pixel level for segmentation. A sliding window approach takes an image and breaks the image into smaller crops. Every crop of the image is classified for a label. This approach is expensive and inefficient because it doesn't reuse the shared features between the overlapping patches. In the following sections, we will discuss a few algorithms that can overcome this problem.

The Fully Convolutional Network

The Fully Convolutional Network (FCN) introduced the idea of an end-to-end convolutional network. Any standard CNN architecture can be used for FCN by removing the fully connected layers, and the implementation of the same was shown in Chapter 4, Object Detection. The fully connected layers are replaced by a convolution layer. The depth is higher in the final layers and the size is smaller. Hence, 1D convolution...

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