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OpenCV Introduction Hang Xiao Oct 26, 2012

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History 1999 Jan : lanched by Intel, real time machine vision library for UI, optimized code for intel 2000 Jun : OpenCV alpha 3 。 2000 Dec : OpenCV beta 1 for linux 2006 : the first 1.0 version supports Mac OS 2008 mid : obtain corporate support from Willow Garage 2009 Sep : OpenCV 1.2 （ beta2.0 2009 Oct : Version 2.0 released 。 2010 Dec : OpenCV 2.2 。 2011 Aug : OpenCV 2.3 。 2012 Apr : OpenCV 2.4.

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Overview Goals Develop a universal toolbox for research and development in the field of Computer Vision Algorithms More than 350 algorithms, 500 API Programming language C/C++, C#, Ch, Python, Ruby, Matlab, and Java (using JavaCV) OS support Windows, Android, Maemo, FreeBSD, OpenBSD, iOS, Linux and Mac OS. Licence BSDlisence, free for commercial and non-commmercial

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Overview - Applications 2D and 3D feature toolkits Egomotion estimation Facial recognition system Gesture recognition Human–computer interaction (HCI) Mobile robotics Motion understanding Object identification Segmentation and Recognition Stereopsis Stereo vision: depth perception from 2 cameras Structure from motion (SFM)Motion tracking

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Overview - A statistical machine learning library Boosting (meta-algorithm) Decision tree learning Gradient boosting trees Expectation-maximization algorithm k-nearest neighbor algorithm Naive Bayes classifier Artificial neural networks Random forest Support vector machine (SVM)

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Outline Image Analysis Structural Analysis Object Recognition Motion Analysis and Object Tracking 3D Reconstruction

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Outline Image Analysis Structural Analysis Object Recognition Motion Analysis and Object Tracking 3D Reconstruction

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Image Analysis Thresholds Statistics Pyramids Morphology Distance transform Flood fill Feature detection Contours retrieving

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Image Thresholding Fixed threshold; Adaptive threshold;

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Image Thresholding Examples Source picture Fixed threshold Adaptive threshold

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Statistics min, max, mean value, standard deviation over the image Norms C, L1, L2 Multidimensional histograms Spatial moments up to order 3 (central, normalized, Hu)

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Multidimensional Histograms Histogram operations calculation, normalization, comparison, back project Histograms types: Dense histograms Signatures (balanced tree) EMD algorithm The EMD computes the distance between two distributions, which are represented by signatures. The signatures are sets of weighted features that capture the distributions. The features can be of any type and in any number of dimensions, and are defined by the user. The EMD is defined as the minimum amount of work needed to change one signature into the other

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EMD – a method for the histograms comparison

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Image Pyramids Gaussian and Laplacian pyramids Image segmentation by pyramids

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Image Pyramids Gaussian and Laplacian

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Pyramid-based color segmentation On still pictures And on movies

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Morphological Operations Two basic morphology operations using structuring element: erosion dilation More complex morphology operations: opening : erosion + dilation closing : dilation + erosion morphological gradient : the difference between the dilation and the erosion of an image top hat : the difference between an input image and its opening black hat : the difference between the closing and its input image

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Morphological Operations Examples Morphology - applying Min-Max. Filters and its combinations Opening IoB= (I B) B Dilatation I B Erosion I B Image I Closing IB= (I B) BTopHat(I)= I - (I B)BlackHat(I)= (I B) - I Grad(I)= (I B)-(I B)

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Distance 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 Filling Simple Gradient

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Feature Detection Fixed filters (Sobel operator, Laplacian); Optimal filter kernels with floating point coefficients (first, second derivatives, Laplacian) Special feature detection (corners) Canny operator Hough transform (find lines and line segments) Gradient runs

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Canny Edge Detector

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Hough Transform Detects lines in a binary image Probabilistic Hough TransformProbabilistic Hough Transform Standard Hough TransformStandard Hough Transform

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Another Sample of the Hough Transform Using Source picture Result

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Contour 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 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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Outline Image Analysis Structural Analysis Object Recognition Motion Analysis and Object Tracking 3D Reconstruction

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Structural Analysis Contours processing Approximation Hierarchical representation Shape characteristics Matching Geometry Contour properties Fitting with primitives PGH: pair-wise geometrical histogram for the contour.

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Contour Processing Approximation: RLE algorithm (chain code) Teh-Chin approximation (polygonal) Douglas-Peucker approximation (polygonal); Contour moments (central and normalized up to order 3) Hierarchical representation of contours Matching of contours

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Hierarchical Representation of Contours A contour is represented with a binary tree Given the binary tree, the contour can be retrieved with arbitrary precision The binary tree is quasi invariant to translations, rotations and scaling

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Contours matching Matching based on hierarchical representation of contours

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Geometry Properties of contours: (perimeter, area, convex hull, convexity defects, rectangle of minimum area) Fitting: (2D line, 3D line, circle, ellipse) Pair-wise geometrical histogram

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Pair-wise geometrical histogram (PGH)

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Outline Image Analysis Structural Analysis Object Recognition Motion Analysis and Object Tracking 3D Reconstruction

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Object Recognition Eigen objects Hidden Markov Models

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Eigen Objects

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Eigen objects (continued)

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Hidden Markov Model Definitions - The set of states - The set of measurements - The state at time t - The transition probability matrix - The conditional probability matrix - The starting states distribution

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Embedded HMM for Face Recognition Model- - Face ROI partition

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Face recognition using Hidden Markov Models One person – one HMM Stage 1 – Train every HMM Stage 2 – Recognition P i - probability Choose max(P i ) … 1 n i

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Outline Image Analysis Structural Analysis Object Recognition Motion Analysis and Object Tracking 3D Reconstruction

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Motion Analysis and Object Tracking Background subtraction Motion templates Optical flow Active contours Estimators

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Background Subtraction Background model (normal distribution) Background statistics functions: Average Standard deviation Running average

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Motion Templates Object silhouette Motion history images Motion history gradients Motion segmentation algorithm silhouetteMHI MHG

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Motion Segmentation Algorithm Two-pass algorithm labeling all motion segments

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Motion Templates Example Motion templates allow to retrieve the dynamic characteristics of the moving object

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Optical Flow Block matching technique Horn & Schunck technique Lucas & Kanade technique Pyramidal LK algorithm 6DOF (6 degree of freedom) algorithm Optical flow equations:

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Pyramidal Implementation of the optical flow algorithm J imageI image Image Pyramid Representation Iterative Lucas – Kanade Scheme Generic Image (L-1)-th Level L-th Level Location of point u on image u L =u/2 L Spatial gradient matrix Standard Lucas – Kanade scheme for optical flow computation at level L d L Guess for next pyramid level L – 1 Finally, Image pyramid building Optical flow computation

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6DOF Algorithm Parametrical optical flow equations:

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Active Contours Snake energy: Internal energy: External energy: Two external energy types:

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Estimators Kalman filter ConDensation filter

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Kalman object tracker

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Outline Image Analysis Structural Analysis Object Recognition Motion Analysis and Object Tracking 3D Reconstruction

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3D reconstruction Camera Calibration View Morphing POSIT

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Camera Calibration Define intrinsic and extrinsic camera parameters. Define Distortion parameters

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Camera Calibration Now, camera calibration can be done by holding checkerboard in front of the camera for a few seconds. And after that you ’ ll get: 3D view of etalon Un-distorted image

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View Morphing

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POSIT Algorithm Perspective projection: Weak-perspective projection:

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OpenCV Websites http://opencv.org OpenCV official webpage. http://opencvlibrary.sourceforge.net/ http://opencvlibrary.sourceforge.net/ OpenCV documentation and FAQs.

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OpenCV Examples adaptiveskindetector : detect skin area fback_c : dense Franeback optical flow contours : calculate contours on different levels delaunay : delaunay triangle find_obj : SURF Detector and Descriptor using either FLANN or brute force matching on planar objects morphology : open/close, erode/dilate motempl ： motion templates mser_sample : Maximal Extremal Region interest point detector polar_transforms : illustrates Linear-Polar and Log-Polar image transforms pyramid_segmentation : color pyramid segmentation

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Thanks !!!

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