#### Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Title Page
Contributors
Packt Upsell
Preface
Free Chapter
Applying Geometric Transformations to Images
Detecting Edges and Applying Image Filters
Cartoonizing an Image
Detecting and Tracking Different Body Parts
Extracting Features from an Image
Seam Carving
Detecting Shapes and Segmenting an Image
Object Tracking
Machine Learning by an Artificial Neural Network
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## What are support vector machines?

Support vector machines (SVM) are supervised learning models that are very popular in the realm of machine learning. SVMs are really good at analyzing labeled data and detecting patterns. Given a bunch of data points and the associated labels, SVMs will build the separating hyperplanes in the best possible way.

Wait a minute, what are hyperplanes? To understand that, let's consider the following figure:

As you can see, the points are being separated by line boundaries that are equidistant from the points. This is easy to visualize in two dimensions. If it were in three dimensions, the separators would be planes. When we build features for images, the length of the feature vectors is usually in the six-digit range. So, when we go to such a high dimensional space, the equivalent of lines would be hyperplanes. Once the hyperplanes are formulated, we use this mathematical model to classify unknown data, based on where it falls on this map.