OpenCV with Python blueprints design and develop advanced computer vision projects using OpenCV with Python
Design and develop advanced computer vision projects using OpenCV with Python About This Book Program advanced computer vision applications in Python using different features of the OpenCV library Practical end-to-end project covering an important computer vision problem All projects in the book inc...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2015.
|
| Edición: | 1st edition |
| Colección: | Community experience distilled.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629565506719 |
Tabla de Contenidos:
- Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Fun with Filters; Planning the app; Creating a black-and-white pencil sketch; Implementing dodging and burning in OpenCV; Pencil sketch transformation; Generating a warming/cooling filter; Color manipulation via curve shifting; Implementing a curve filter by using lookup tables; Designing the warming/cooling effect; Cartoonizing an image; Using a bilateral filter for edge-aware smoothing; Detecting and emphasizing prominent edges
- Combining colors and outlines to produce a cartoonPutting it all together; Running the app; The GUI base class; The GUI constructor; Handling video streams; A basic GUI layout; A custom filter layout; Summary; Chapter 2: Hand Gesture Recognition Using a Kinect Depth Sensor; Planning the app; Setting up the app; Accessing the Kinect 3D sensor; Running the app; The Kinect GUI; Tracking hand gestures in real time; Hand region segmentation; Finding the most prominent depth of the image center region; Applying morphological closing to smoothen the segmentation mask
- Finding connected components in a segmentation maskHand shape analysis; Determining the contour of the segmented hand region; Finding the convex hull of a contour area; Finding the convexity defects of a convex hull; Hand gesture recognition; Distinguishing between different causes of convexity defects; Classifying hand gestures based on the number of extended fingers; Summary; Chapter 3: Finding Objects via Feature Matching and Perspective Transforms; Tasks performed by the app; Planning the app; Setting up the app; Running the app; The FeatureMatching GUI; The process flow
- Feature extractionFeature detection; Detecting features in an image with SURF; Feature matching; Matching features across images with FLANN; The ratio test for outlier removal; Visualizing feature matches; Homography estimation; Warping the image; Feature tracking; Early outlier detection and rejection; Seeing the algorithm in action; Summary; Chapter 4: 3D Scene Reconstruction Using Structure from Motion; Planning the app; Camera calibration; The pinhole camera model; Estimating the intrinsic camera parameters; The camera calibration GUI; Initializing the algorithm
- Collecting image and object pointsFinding the camera matrix; Setting up the app; The main function routine; The SceneReconstruction3D class; Estimating the camera motion from a pair of images; Point matching using rich feature descriptors; Point matching using optic flow; Finding the camera matrices; Image rectification; Reconstructing the scene; 3D point cloud visualization; Summary; Chapter 5: Tracking Visually Salient Objects; Planning the app; Setting up the app; The main function routine; The Saliency class; The MultiObjectTracker class; Visual saliency; Fourier analysis
- Natural scene statistics
