Computer and Machine Vision

Transcription

1 Computer and Machine Vision Lecture Week 7 Part-1 (Convolution Transform Speed-up and Hough Linear Transform) February 26, 2014 Sam Siewert

2 Outline of Week 7 Basic Convolution Transform Speed-Up Concepts for Computer Vision Hough Linear Transform Review for Exam #1 Exam #1 Next Week Friday/Saturday Friday In Person 9am to 12noon Or Via Between 9am Friday and 9pm Saturday Sam Siewert 2

3 Methods to Speed-Up Convolution Transform Multi-Core Speed-up Sam Siewert 3

4 Threading Review Recall Threads from Operating Systems (Review if you Need to) CPU Core Methods POSIX Threads GPU Co-Processor Methods CUDA (Not Required Here Covered in Digital Media, Tricky) If Interested, See Udacity Intro to Parallel Programming SIMD Generate Vector Instructions on x86 with SSE (- mssse3 switch for gcc), See Paper Sam Siewert 4

5 Transform Thread Grids Recall Flynn s Taxonomy of Architectures for Processing Linux POSIX Threading Map Threads to Frame Grid Divide Up Frame Or Map Thread to Each Frame in Sequence # Threads is at Least 2x # of CPU Cores or Co-processor cores POSIX thread creation, parameters, join Thread safety Stack variables only Thread indexed global data Mutual Exclusion Semaphore protection of shared global data SIMD Vector Instructions Sam Siewert 5

6 Multi-Core Speedup Recall Speed-up Limited by Amdahl s Law (MS Excel Example) P = Parallel Portion (1-P) = Sequential Portion S = # of Cores to Provide Speedup # Threads on Multi-Core Should be at Least 2 x S, Up to 4 x S (To Account of I/O blocking) Sam Siewert 6

7 Hough Linear Transform Introduction to Shape Recognition Sam Siewert 7

8 Line Recognition Patent by P.V.C. Hough in 1962 Further Refined by Richard Duda and Peter Hart working at SRI on Shakey Robot Use of the Hough Transformation to Detect Lines and Curves in Pictures, Richard Duda & Peter Hart How the Hough Transform was Invented, P.E. Hart Both Available on Blackboard Transformation to R, Theta Space for All Points in Image on Edges in PBM Sam Siewert 8

9 Hough Transform Every Point on an Edge in the Image Has a Range of Lines it Can Lie On, Each with Unique Perpendicular Distance R to Origin and Angle Theta Off X axis From How the Hough Transform Was Invented, Peter E. Hart, IEEE Signal and Processing Magazine, November Center in Image: -R to R and Theta=0 Pi R, Theta Intersections in Transformed Space are Co-linear Sam Siewert 9

10 From Duda and Hart Paper Example of 120x120 PGM Converted to PBM with Thresholiding R, Theta at 20 degree increments Counts for Image FG Pixels with R, Theta Intersection Accumulated Counts > 35 Considered a Significant Edge Line in Shape of Interest From Use of Hough Transformation to Detect Lines and Curves in Pictures, Richard Duda and Peter Hart, Communications of the ACM, January 1972, Volume 15, Number 1. Sam Siewert 10

11 Hough Transform General Recognition Similar Approach in Parametric Space Used to Find Edge Pixels on a Circle or Ellipse in Parametric Transform Generalized Based on Parametric Transform for a Pattern Model Sam Siewert 11

12 RANSAC Alternative Start with Any 2 Edge Points to Form Line Hypothesis Find Number of Additional Edge Points that Are Co-linear Within Tolerance to Support Hypothesis Final Line has Greatest Support Disadvantage is Run-time Compared to Hough Transform Hough is Based on R and Theta Resolution and Number of Edge Points O(n x R x Theta) RANSAC Can Test all Edge Pairs Potentially O(n 3 ) From Computer and Machine Vision, E.R. Davies, page 292. Sam Siewert 12

13 OpenCV Hough Transform hough_line.cpp From OpenCV Examples See Manual for OpenCV for HoughLines Sam Siewert 13

14 Feature Detectors Keypoint features Edges Correspondences CV pp Used to Stitch Together Images in a Mosaic Used for 3D Image Stereopsis (Registration Points for Right and Left Eye for Similar Triangles) Used for Image Stabilization (Without Gimbal) Sam Siewert 14

15 Feature Correspondence 1. Correlation (Least Squares) and Track with Localized Search Track Identified Feature Frame to Frame Similar to Lab #3, but More Complex than Simple Center of Mass for Threshold 2. Detect all Features and Match Find Locations That Will Match Well Compact Invariant Descriptor Search New Images for Matching Candidates David Lowe SIFT Sam Siewert 15

16 Summary Segmenting Shapes in an Image is More Powerful Than Edge Finding Step toward Recognition Beyond Segmentation Step toward Feature Registration (Common Feature Seen in Two Images of Same Scene) More on SIFT Scale Invariant Feature Transform, After Exam #1 Sam Siewert 16