Mastering OpenCV with Practical Computer Vision ProjectsPackt Publishing Ltd, 2012 M12 3 - 340 páginas Each chapter in the book is an individual project and each project is constructed with step-by-step instructions, clearly explained code, and includes the necessary screenshots. You should have basic OpenCV and C/C++ programming experience before reading this book, as it is aimed at Computer Science graduates, researchers, and computer vision experts widening their expertise. |
Contenido
Summary | |
Application architecture | |
Grayscale conversion | |
Markerless Augmented Reality | |
Exploring Structure from Motion Using OpenCV | |
Number Plate Recognition Using SVM and Neural Networks | |
3D Head Pose Estimation Using AAM and POSIT | |
Face Recognition using Eigenfaces or Fisherfaces | |
Eye detection | |
Collecting faces and learning from them | |
Face recognition | |
Collection mode | |
Checking and handling mouse clicks | |
Otras ediciones - Ver todas
Mastering OpenCV with Practical Computer Vision Projects Shervin Emami,Khvedchenia Ievgen,Daniel Lélis Baggio,Naureen Mahmood Sin vista previa disponible - 2012 |
Términos y frases comunes
3D point Active Appearance Models Active Shape Models algorithm Android annotation application Augmented Reality BGRA bool camera calibration camera frame Cartoonifier chapter clicks CMake collected faces color format components computer vision const float const int contours coordinate create cv::Mat dataset desktop draw edges Eigenfaces eigenvectors face detection face recognition face recognition algorithm face tracking facial feature feature detectors Feature extraction feature points filter Fisherfaces following code following screenshot function grayscale histogram equalization homography implementation input image keypoints marker code marker-less mask matching matrix method mode object OpenCV OpenGL optical flow output parameters patch models pattern perform pixels point cloud position preprocessed face reconstruction Rect rectangle region result rotation scale screenshot shows segmentation shape model step subspace threshold training data training images training set transformation triangulation uchar variable vector vector<Point2f visualization void webcam width window XCode