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Hands-On Image Processing with Python

You're reading from  Hands-On Image Processing with Python

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Pages 492 pages
Edition 1st Edition
Languages
Author (1):
Sandipan Dey Sandipan Dey
Profile icon Sandipan Dey

Table of Contents (20) Chapters

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
1. Getting Started with Image Processing 2. Sampling, Fourier Transform, and Convolution 3. Convolution and Frequency Domain Filtering 4. Image Enhancement 5. Image Enhancement Using Derivatives 6. Morphological Image Processing 7. Extracting Image Features and Descriptors 8. Image Segmentation 9. Classical Machine Learning Methods in Image Processing 10. Deep Learning in Image Processing - Image Classification 11. Deep Learning in Image Processing - Object Detection, and more 12. Additional Problems in Image Processing 1. Other Books You May Enjoy Index

Introducing YOLO v2 


YOLO, is a very popular and fully conventional algorithm that is used for detecting images. It gives a very high accuracy rate compared to other algorithms, and also runs in real time. As the name suggests, this algorithm looks only once at an image. This means that this algorithm requires only one forward propagation pass to make accurate predictions. 

In this section, we will detect objects in images with a fully convolutional network (FCN) deep learning model. Given an image with some objects (for example, animals, cars, and so on), the goal is to detect objects in those images using a pre-trained YOLO model, with bounding boxes.

Many of the ideas are from the two original YOLO papers, available at https://arxiv.org/abs/1506.02640 and https://arxiv.org/abs/1612.08242. But before diving into the YOLO model, let's first understand some prerequisite fundamental concepts.

Classifying and localizing images and detecting objects

Let's first understand the concepts regarding...

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