OpenCV 4 computer vision application programming cookbook build complex computer vision applications with OpenCV and C++
Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detection Key Features Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms Develop effective, robust, and fail-safe vision for your applications Build...
| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | Inglés |
| Published: |
Birmingham ; Mumbai :
Packt Publishing Ltd
2019.
|
| Edition: | Fourth edition |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630733506719 |
Table of Contents:
- Cover
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Table of Contents
- Preface
- Chapter 1: Playing with Images
- Installing the OpenCV library
- Getting ready
- How to do it...
- How it works...
- There's more...
- Using Qt for OpenCV developments
- The OpenCV developer site
- See also
- Loading, displaying, and saving images
- Getting ready
- How to do it...
- How it works...
- There's more...
- Clicking on images
- Drawing on images
- Running the example with Qt
- See also
- Exploring the cv::Mat data structure
- How to do it...
- How it works...
- There's more...
- The input and output arrays
- See also
- Defining regions of interest
- Getting ready
- How to do it...
- How it works...
- There's more...
- Using image masks
- See also
- Chapter 2: Manipulating the Pixels
- Accessing pixel values
- Getting ready
- How to do it...
- How it works...
- There's more...
- The cv::Mat_ template class
- See also
- Scanning an image with pointers
- Getting ready
- How to do it...
- How it works...
- There's more...
- Other color reduction formulas
- Having input and output arguments
- Efficient scanning of continuous images
- Low-level pointer arithmetics
- See also
- Scanning an image with iterators
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Writing efficient image-scanning loops
- How to do it...
- How it works...
- There's more...
- See also
- Scanning an image with neighbor access
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Performing simple image arithmetic
- Getting ready
- How to do it...
- How it works...
- There's more...
- Overloaded image operators
- Splitting the image channels
- Remapping an image
- How to do it...
- How it works...
- See also.
- Chapter 3: Processing Color Images with Classes
- Comparing colors using the strategy design pattern
- How to do it...
- How it works...
- There's more...
- Computing the distance between two color vectors
- Using OpenCV functions
- The functor or function object
- The OpenCV base class for algorithms
- See also
- Segmenting an image with the GrabCut algorithm
- How to do it...
- How it works...
- See also
- Converting color representations
- Getting ready
- How to do it...
- How it works...
- See also
- Representing colors with hue, saturation, and brightness
- How to do it...
- How it works...
- There's more...
- Using colors for detection - skin tone detection
- Chapter 4: Counting the Pixels with Histograms
- Computing the image histogram
- Getting started
- How to do it...
- How it works...
- There's more...
- Computing histograms of color images
- See also
- Applying lookup tables to modify the image's appearance
- How to do it...
- How it works...
- There's more...
- Stretching a histogram to improve the image contrast
- Applying a lookup table on color images
- Equalizing the image histogram
- How to do it...
- How it works...
- Backprojecting a histogram to detect specific image content
- How to do it...
- How it works...
- There's more...
- Backprojecting color histograms
- Using the mean shift algorithm to find an object
- How to do it...
- How it works...
- See also
- Retrieving similar images using histogram comparison
- How to do it...
- How it works...
- See also
- Counting pixels with integral images
- How to do it...
- How it works...
- There's more...
- Adaptive thresholding
- Visual tracking using histograms
- See also
- Chapter 5: Transforming Images with Morphological Operations
- Eroding and dilating images using morphological filters
- Getting ready.
- How to do it...
- How it works...
- There's more...
- See also
- Opening and closing images using morphological filters
- How to do it...
- How it works...
- See also
- Detecting edges and corners using morphological filters
- Getting ready
- How to do it...
- How it works...
- See also
- Segmenting images using watersheds
- How to do it...
- How it works...
- There's more...
- See also
- Extracting distinctive regions using MSER
- How to do it...
- How it works...
- See also
- Extracting foreground objects with the GrabCut algorithm
- How to do it...
- How it works...
- See also
- Chapter 6: Filtering the Images
- Filtering images using low-pass filters
- How to do it...
- How it works...
- See also
- Downsampling an image
- How to do it...
- How it works...
- There's more...
- Interpolating pixel values
- See also
- Filtering images using a median filter
- How to do it...
- How it works...
- Applying directional filters to detect edges
- How to do it...
- How it works...
- There's more...
- Gradient operators
- Gaussian derivatives
- See also
- Computing the Laplacian of an image
- How to do it...
- How it works...
- There's more...
- Enhancing the contrast of an image using the Laplacian
- Difference of Gaussians
- See also
- Chapter 7: Extracting Lines, Contours, and Components
- Detecting image contours with the Canny operator
- How to do it...
- How it works...
- See also
- Detecting lines in images with the Hough transform
- Getting ready
- How to do it...
- How it works...
- There's more...
- Detecting circles
- See also
- Fitting a line to a set of points
- How to do it...
- How it works...
- There's more...
- Extracting the components' contours
- How to do it...
- How it works...
- There's more...
- Computing components' shape descriptors
- How to do it...
- How it works...
- There's more...
- Quadrilateral detection
- Chapter 8: Detecting Interest Points
- Detecting corners in an image
- How to do it...
- How it works...
- There's more...
- Good features to track
- The feature detector's common interface
- See also
- Detecting features quickly
- How to do it...
- How it works...
- There's more...
- Adapted feature detection
- See also
- Detecting scale-invariant features
- How to do it...
- How it works...
- There's more...
- The SIFT feature-detection algorithm
- See also
- Detecting FAST features at multiple scales
- How to do it...
- How it works...
- There's more...
- The ORB feature-detection algorithm
- See also
- Chapter 9: Describing and Matching Interest Points
- Matching local templates
- How to do it...
- How it works...
- There's more...
- Template matching
- See also
- Describing local intensity patterns
- How to do it...
- How it works...
- There's more...
- Cross-checking matches
- The ratio test
- Distance thresholding
- See also
- Describing keypoints with binary features
- How to do it...
- How it works...
- There's more...
- FREAK
- See also
- Chapter 10: Estimating Projective Relations in Images
- Computing the fundamental matrix of an image pair
- Getting ready
- How to do it...
- How it works...
- See also
- Matching images using a random sample consensus
- How to do it...
- How it works...
- There's more...
- Refining the fundamental matrix
- Refining the matches
- Computing a homography between two images
- Getting ready
- How to do it...
- How it works...
- There's more...
- Detecting planar targets in an image
- How to do it...
- See also
- Chapter 11: Reconstructing 3D Scenes
- Digital image formation
- Calibrating a camera
- Getting ready
- How to do it...
- How it works...
- There's more...
- Calibration with known intrinsic parameters
- Using a grid of circles for calibration
- See also
- Recovering the camera pose
- How to do it...
- How it works...
- There's more...
- cv::Viz - a 3D visualizer module
- See also
- Reconstructing a 3D scene from calibrated cameras
- How to do it...
- How it works...
- There's more...
- Decomposing a homography
- Bundle adjustment
- See also
- Computing depth from a stereo image
- Getting ready
- How to do it...
- How it works...
- See also
- Chapter 12: Processing Video Sequences
- Reading video sequences
- How to do it...
- How it works...
- There's more...
- See also
- Processing video frames
- How to do it...
- How it works...
- There's more...
- Processing a sequence of images
- Using a frame processor class
- See also
- Writing video sequences
- How to do it...
- How it works...
- There's more...
- The codec four-character code
- See also
- Extracting the foreground objects in a video
- How to do it...
- How it works...
- There's more...
- The mixture of Gaussian method
- See also
- Chapter 13: Tracking Visual Motion
- Tracing feature points in a video
- How to do it...
- How it works...
- See also
- Estimating the optical flow
- Getting ready
- How to do it...
- How it works...
- See also
- Tracking an object in a video
- How to do it...
- How it works...
- See also
- Chapter 14: Learning from Examples
- Recognizing faces using the nearest neighbors of local binary patterns
- How to do it...
- How it works...
- See also
- Finding objects and faces with a cascade of Haar features
- Getting ready
- How to do it...
- How it works...
- There's more...
- Face detection with a Haar cascade
- See also
- Detecting objects and people using SVMs and histograms of oriented gradients
- Getting ready
- How to do it...
- How it works...
- There's more...
