opencv training course by theerayod wiangtong. goals develop a universal toolbox for research and...
TRANSCRIPT
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OpenCV Training courseBy Theerayod Wiangtong
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GoalsGoals Develop a universal toolbox for research
and development in the field of Computer Vision
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Why use OpenCV? Fast development time, more than 500
algorithms in OpenCV libraries C/C++ based programming Both Windows and Linux supported Open and free, BSD license Loads of developers using OpenCV Loads of information and documents Etc
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History of OpenCV Originally developed by Intel, currently
maintained by Willow Garage
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OpenCV - Features
Table Courtesy Learning OpenCV: Computer Vision with the OpenCV Library
5
Cross-platform and extremely portable Free! for both research and commercial use Targeted for real-time applications
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OpenCV – Architecture & Modules
6
CvAux Area for experimental algorithms: e.g. HMM, Stereo
vision, 3D tracking, Bg/fg segmentation, camera calibration, Shape matching, Gesture recognition, ..
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OpenCV Comparisons
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Examples of Using OpenCV functions Click here
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OpenCV: Algorithmic Content
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OpenCV FunctionalityOpenCV FunctionalityBasic structures and operations Image AnalysisStructural AnalysisObject RecognitionMotion Analysis and Object Tracking3D Reconstruction
(more than 500 (more than 500 algorithms!!)algorithms!!)
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Image ThresholdingImage Thresholding Fixed threshold; Adaptive
threshold;
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StatisticsStatistics min, max, mean value, standard
deviation over the image Multidimensional histograms Norms C, L1, L2
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Multidimensional HistogramsMultidimensional Histograms Histogram operations : calculation,
normalization, comparison, back project
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Histogram Equalization
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Histograms comparisonHistograms comparison
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Image PyramidsImage Pyramids
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Convolution in image The source pixel and its surrounding
pixels are all mathematically merged to produce a single destination pixel. The matrix slides across the surface of the source image, producing pixels for the destination image
http://beej.us/blog/data/convolution-image-processing/
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Image PyramidsImage Pyramids
Gaussian and Laplacian
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Morphological OperationsMorphological OperationsTwo basic morphology operations
using structuring element: erosion dilation
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Distance TransformDistance Transform Calculate the distance for all non-feature points
to the closest feature point Two-pass algorithm, 3x3 and 5x5 masks, various
metrics predefined
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Flood FillingFlood Filling
•grayscale image, floating range •grayscale image, fixed range
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Feature DetectionFeature Detection
Fixed filters (Sobel operator, Canny operator, Laplacian, Scharr filter)
Hough transform (find lines and circles)
http://www.stevens-tech.edu/wireless/klin/EdgeDetection/EdgeDetectionInfo.htm
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Edge detection operators Simple
Cross
2 -1
-1 0
This means: pixel(i,j) = 2*pixel(i,j) - pixel(i,j+1) - pixel(i+1,j).
1 0
0 -10 1
-1 0
Template 1: Template 2: pixel(i,j) = maximum(template 1, template 2)
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Edge detection operators Prewitt
Sobel
1 0 -1
1 0 -1
1 0 -1
1 1 1
0 0 0
-1 -1 -1
X-axis Template: Y-axis Template:pixel(i,j) = sqrt((x-axis template)^2 + (y-axis template)^2)
1 0 -1
2 0 -2
1 0 -1
1 2 1
0 0 0
-1 -2 -1
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Canny Edge DetectorCanny Edge Detector
http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html
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Hough TransformHough Transform
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Contour RetrievingContour Retrieving The contour representation:
Chain code (Freeman code) Polygonal representation
Initial Point
Chain code for the curve: 34445670007654443
Contour representation
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Hierarchical representation of contours
Image Boundary
(W1) (W2) (W3)
(B2) (B3) (B4)
(W5) (W6)
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Contours ExamplesContours Examples
Source Picture(300x600 = 180000 pts total)
Retrieved Contours (<1800 pts total)
After Approximation(<180 pts total)
And it is rather fast: ~70 FPS for 640x480 on complex scenes
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Contour ProcessingContour Processing
Approximation: RLE algorithm (chain code) Teh-Chin approximation (polygonal) Douglas-Peucker approximation (polygonal);
Contour moments (central and normalized up to order 3) Matching of contours
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Contours matchingContours matchingMatching based on hierarchical
representation of contours
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Object Recognition: Eigen Object Recognition: Eigen ImageImage
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One person – one HMMStage 1 – Train every HMM
Stage 2 – Recognition
Pi - probability
Choose max(Pi)
…1
n
i
Object Recognition: HMMObject Recognition: HMM
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Motion Analysis and Object Motion Analysis and Object TrackingTracking
Background subtraction Motion templates Optical flow Active contours Estimators
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Background SubtractionBackground Subtraction Background: any static or periodically moving
parts of a scene that remain static or periodic over the period of interest. How about waving trees, light on/off..?!?
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Background statistics functions
Average Standard deviation Connect component
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Background Subtraction Background Subtraction ExampleExample
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Motion TemplatesMotion TemplatesObject silhouetteMotion history imagesMotion history gradientsMotion segmentation algorithm
silhouette MHI
MHG
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Motion Templates ExampleMotion Templates Example
•Motion templates allow to retrieve the dynamic characteristics of the moving object
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ObjectObject trackingtracking Mean-shift
Choose a search window (width and location) Compute the mean of the data in the search window Center the search window at the new mean location Repeat until convergence
Cam-shift: Continuously Adaptive Mean SHIFT
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Region ofinterest
Center ofmass
Mean Shiftvector
Mean shift
Slide by Y. Ukrainitz & B. Sarel
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Region ofinterest
Center ofmass
Mean Shiftvector
Mean shift
Slide by Y. Ukrainitz & B. Sarel
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Region ofinterest
Center ofmass
Mean Shiftvector
Mean shift
Slide by Y. Ukrainitz & B. Sarel
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Region ofinterest
Center ofmass
Mean Shiftvector
Mean shift
Slide by Y. Ukrainitz & B. Sarel
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Region ofinterest
Center ofmass
Mean Shiftvector
Mean shift
Slide by Y. Ukrainitz & B. Sarel
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Region ofinterest
Center ofmass
Mean Shiftvector
Mean shift
Slide by Y. Ukrainitz & B. Sarel
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Region ofinterest
Center ofmassMean shift
Slide by Y. Ukrainitz & B. Sarel
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Particle filter
ObjectObject trackingtracking
Optical flow is the relation of the motion field. It is a 2D projection of the physical movement of points relative to the observer
Optical flow, LK
)1( tI
Optical FlowOptical Flow
}{),( iptI
1p
2p
3p
4p
1v
2v
3v
4v
}{ iv
Velocity vectorsVelocity vectors
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OpenCV shape classification OpenCV shape classification capabilitiescapabilitiesContour approximationMoments (image&contour)Convexity analysisPair-wise geometrical
histogramFitting functions (line, ellipse)
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Using contours and geometry Using contours and geometry to classify shapesto classify shapesGiven the contour
classify the geometrical figure shape (triangle, circle, etc)
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MomentsMoments
Contour moments (faster)Not applicable for different sizes,
orientationHu invariants
Here p is the x-order and q is the y-order, whereby order means the power to which the corresponding component is taken in the sum just displayed. E.g. m00 moment is actually just thelength in pixels of the contour.
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Image segmentationImage segmentation Separate image into coherent “objects”
image human segmentation
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Segmentation MethodsSegmentation MethodsEdge-based approach
Color segmentation: histogramCalculate the histogram. Find the objects of the selected histogram in the image.
Apply edge detector (sobel, laplace, canny, gradient strokes).Find connected components in an inverted image
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OpenCV: Getting started
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Getting StartedGetting Started
56
Download OpenCV http://opencv.willowgarage.com/wiki/
There exists a short walkthrough video on YouTube at http://www.youtube.com/watch?v=9nPpa_WiArI
Learning OpenCV: Computer Vision with the OpenCV Library by Gary Bradski and Adrian Kaehler http://proquest.safaribooksonline.com/9780596516130
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OpenCV 2.1 with Visual Studio OpenCV 2.1 with Visual Studio 20082008
Download the OpenCV 2.1.0 Windows installer from SourceForge - "OpenCV-2.1.0-win32-vs2008.exe".
Install it to a folder (without any spaces in it), say "C:\OpenCV2.1\". This article will refer to this path as $openCVDir
During installation, enable the option "Add OpenCV to the system PATH for all users".
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Configure Visual Studio Configure Visual Studio 20082008 Open VC++ Directories configuration: Tools >
Options > Projects and Solutions > VC++ Directories
Choose "Show directories for: Include files" • Add "$openCVDir\include\opencv"
Choose "Show directories for: Library files" • Add "$openCVDir\lib"
Choose "Show directories for: Source files" • Add "$openCVDir\src\cv" • Add "$openCVDir\src\cvaux" • Add "$openCVDir\src\cxcore" • Add "$openCVDir\src\highgui"
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Configure your ProjectConfigure your Project Open Project Properties: Project > %projectName
% Properties... Open Linker Input properties: Configuration
Properties > Linker > Input Open the "..." window to edit "Additional
Dependencies" and on each line put: • "cv210.lib" • "cxcore210.lib" • "highgui210.lib" • And any other lib file, e.g, cvaux.lib, necessary for
your project Your project should now build. If you get any errors
try restarting Visual Studio and then doing a clean Rebuild.
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More info
http://opencv.willowgarage.com/documentation/c/index.html http://dasl.mem.drexel.edu/~noahKuntz/openCVTut1.html http://sapachan.blogspot.com/search/label/Learning%20OpenCV http://www.shervinemami.co.cc/introToOpenCV.html http://note.sonots.com/OpenCV/Install.html
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Questions