OpenCV Blueprints by Joseph Howse
OpenCV Blueprints Joseph Howse ebook
Publisher: Packt Publishing
In this section, we'll look at some Android-specific tasks: one is rendering an overlay on top of the camera view and the second is reading media files on Android. Rounding up the unusual suspects This chapter's demo applications are tested with three cameras, which are described in the following table. Discover practical and interesting innovations in computer vision while building atop a mature open-source library, OpenCV 3. Classification Once you have extracted the features for all the samples in the dataset, it is time to start the classification process. We can separate the face and apply an eye detector using face detection, which can be done with a high-resolution camera. Search in book Toggle Font Controls. Further improvement In this section, we will show some improvements that you can consider while creating a fully featured panorama application. What we have right now is a very barebones implementation of video stabilization. Twitter · Facebook · Google Plus · Email · Prev. Calibration In the section that discusses the mathematical basis, we found several unknown camera parameters. System overview In this section, we will explain the process to apply the trained model in your application. Gyroscopic Video Stabilization Video stabilization is a classic problem in computer vision. What you need for this book As a basic setup, the complete book is based on the OpenCV 3 software. Further reading We have introduced a basic system for facial expression. In the previous section, we discussed the use of the first biometric, which is the face of the person trying to log in to the system. The process flow Features are extracted, matched, and tracked by the FeatureMatching class, especially by its public match method.