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

Segmentation is often referred to as the clustering of pixels of a similar category. An example is as shown in the following screenshot. Here, we see that inputs are on the left and the segmentation results are on the right. The colors of an object are according to pre-defined object categories. These examples are taken from the Pascal VOC dataset:

In the top picture on the left, there are several small aeroplanes in the background and, therefore, we see small pixels colored accordingly in the corresponding image on the right. In the bottom-left picture, there are two pets laying together, therefore, their segmented image on the right has different colors for the pixels belonging to the cat and dog respectively. In this figure, the boundary is differently colored for convenience and does not imply a different category.

In traditional segmentation techniques, the...