Book Image

Practical Computer Vision

By : Abhinav Dadhich
Book Image

Practical Computer Vision

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)

Segmentation and Tracking

In the previous chapter, we studied different methods for feature extraction and image classification using Convolutional Neural Networks (CNNs) to detect objects in an image. Those methods work well in creating a bounding box around the target object. However, if our application requires a precise boundary, called an instance, around the object, we need to apply a different approach.

In this chapter, we will be focusing on object instance detection, which is also termed image segmentation. In the second part of the chapter, we will first see MOSSE tracker with OpenCV see various approaches to tracking objects in a sequence of image

Segmentation and tracking are, however, not quite interlinked problems, but they depend heavily on the previous approaches of feature extraction and object detection. The application's range is quite vast, including...