What this book covers

Chapter 1, Applying Effects to Images, includes some of the basic preprocessing algorithms used in various computer vision applications. This chapter also explains how you can integrate OpenCV to your existing projects.

Chapter 2, Detecting Basic Features in Images, covers the detection of primary features such as edges, corners, lines, and circles in images.

Chapter 3, Detecting Objects, dives deep into feature detection, using more advanced algorithms to detect and describe features in order to uniquely match them to features in other objects.

Chapter 4, Drilling Deeper into Object Detection – Using Cascade Classifiers, explains the detection of general objects, such as faces/eyes in images and videos.

Chapter 5, Tracking Objects in Videos, covers the concepts of optical flow as a motion detector and implements the Lucas-Kanade-Tomasi tracker to track objects in a video.

Chapter 6, Working with Image Alignment and Stitching, covers the basic concepts of image alignment and image stitching to create a panoramic scene image.

Chapter 7, Bringing Your Apps to Life with OpenCV Machine Learning, explains how machine learning can be used in computer vision applications. In this chapter, we take a look at some common machine learning algorithms and their implementation in Android.

Chapter 8, Troubleshooting and Best Practices, covers some of the common errors and issues that developers face while building their applications. It also unfolds some good practices that can make the application more efficient.

Chapter 9, Developing a Document Scanning App, uses various algorithms that have been explained across various chapters to build a complete system to scan documents, regardless of what angle you click the image at.