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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 Filtering and Transformations in OpenCV

In this chapter, you will learn the basic building blocks for computer vision applications. We are already familiar with digital cameras and smartphone devices with auto image enhancement or color adjustments to make our photographs more pleasing. The techniques behind these originated long ago and have come through several iterations to become better and faster. Many of the techniques explained in this chapter also become major preprocessing techniques for object detection and object classification tasks introduced later. Hence, it is very important to study these techniques and understand their applications.

You will study the basis for these applications with several techniques for filtering an image linearly as well as non-linearly.

Later in the chapter, you will also study transformation techniques and downsampling techniques...

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