OpenCV 2 computer vision application programming cookbook : over 50 recipes to master this library of programming functions for real-time computer vision / Robert Laganière.

Includes bibliographical references and index.

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This is a cookbook that shows results obtained on real images with detailed explanations and the relevant screenshots. The recipes contain code accompanied with suitable explanations that will facilitate your learning. If you are a novice C++ programmer who wants to learn how to use the OpenCV library to build computer vision applications, then this cookbook is appropriate for you. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can be used as a companion book in university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision. The book provides a good combination of basic to advanced recipes. Basic knowledge of C++ is required.

1. Playing with Images -- 2. Manipulating the Pixels -- 3. Processing Images with Classes -- 4. Counting the Pixels with Histograms -- 5. Transforming Images with Morphological Operations -- 6. Filtering the Images -- 7. Extracting Lines, Contours and Components -- 8. Detecting and Matching Interest Points -- 9. Estimating Projective Relations in Images -- 10. Processing Video Sequences.

1. Playing with Images -- Introduction -- Installing the OpenCV library -- Creating an OpenCV project with MS Visual C++ -- Creating an OpenCV project with Qt -- Loading, displaying, and saving images -- Creating a GUI application using Qt -- 2. Manipulating the Pixels -- Introduction -- Accessing pixel values -- Scanning an image with pointers -- Scanning an image with iterators -- Writing efficient image scanning loops -- Scanning an image with neighbor access -- Performing simple image arithmetic -- Defining regions of interest -- 3. Processing Images with Classes -- Introduction -- Using the Strategy pattern in algorithm design -- Using a Controller to communicate with processing modules -- Using the Singleton design pattern -- Using the Model-View-Controller architecture to design an application -- Converting color spaces -- 4. Counting the Pixels with Histograms -- Introduction -- Computing the image histogram -- Applying look-up tables to modify image appearance -- Equalizing the image histogram -- Backprojecting a histogram to detect specific image content -- Using the mean shift algorithm to find an object -- Retrieving similar images using histogram comparison -- 5. Transforming Images with Morphological Operations -- Introduction -- Eroding and dilating images using morphological filters -- Opening and closing images using morphological filters -- Detecting edges and corners using morphological filters -- Segmenting images using watersheds -- Extracting foreground objects with the GrabCut algorithm -- 6. Filtering the Images -- Introduction -- Filtering images using low-pass filters -- Filtering images using a median filter -- Applying directional filters to detect edges -- Computing the Laplacian of an image -- 7. Extracting Lines, Contours and Components -- Introduction -- Detecting image contours with the Canny operator -- Detecting lines in images with the Hough transform -- Fitting a line to a set of points -- Extracting the components' contours -- Computing components' shape descriptors -- 8. Detecting and Matching Interest Points -- Introduction -- Detecting Harris corners -- Detecting FAST features -- Detecting the scale-invariant SURF features -- Describing SURF features -- 9. Estimating Projective Relations in Images -- Introduction -- Calibrating a camera -- Computing the fundamental matrix of an image pair -- Matching images using random sample consensus -- Computing a homography between two images -- 10. Processing Video Sequences -- Introduction -- Reading video sequences -- Processing the video frames -- Writing video sequences -- Tracking feature points in video -- Extracting the foreground objects in video.

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