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Practical Computer Vision

Practical Computer Vision

By : Abhinav Dadhich
1.5 (2)
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Practical Computer Vision

Practical Computer Vision

1.5 (2)
By: Abhinav Dadhich

Overview of this book

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects. With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.
Table of Contents (12 chapters)
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Image pyramids

Pyramids refer to rescaling a photograph either increasing the resolution or decreasing it. These are often used to increase the computation efficiency of computer vision algorithms such as image matching in a huge database. In such cases, image matching is computed on a downsampled image and later on the search is iteratively refined for a higher resolution of the image.

The downsampling and upsampling often depend on the pixel selection process. One of the simplest processes is selecting alternative rows and column pixel values to create a downsampled version of the photo as follows:

However, if we try to upsample from the rightmost picture in the previous figure, the results look as follows:

It can easily be seen that the rightmost picture above is not the same as the original one that we started with. This is due to the fact that we lose information during...

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