ME 6406 MACHINE VISION. Georgia Institute of Technology
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1 ME 6406 MACHINE VISION Georgia Institute of Technology
2 Class Information Instructor Professor Kok-Meng Lee MARC 474 Office hours: Tues/Thurs 1:00-2:00 pm (404) Class Website Notes, Homework submissions, links, etc
3 Prerequisites Graduate Standing MATLAB proficiency Required for homework submission Image Processing Toolbox Available in computer labs MRDC 2104, 2105 Library commons
4 Recommended Texts Digital Image Processing using Matlab, R. C. Gonzalez, R. E. Woods and S. Eddins, Prentice Hall, 2004 Digital Image Processing, R. C. Gonzalez and R. E. Woods, Prentice Hall, 3 rd Edition, 2008
5 Evaluation Policy 4 Homeworks (20% each) Electronic submission to T-square Individual submission 2 In-class Midterms (10% each)
6 Course Description Design of algorithms for vision systems for manufacturing, farming, construction and the service industries. Image processing, optics, illumination and feature representation.
7 What is Machine Vision? The process of acquiring one or more images, using an optical sensing device, and subsequent processing and analysis so that decisions can be made Machine vision encompasses computer science, optics, mechanical engineering, and industrial automation
8 What is Machine Vision? Machine Vision Context of industrial applications Integration with sensing & control theory Illumination regulation Computer Vision Field in general Image Processing Operation that produces images from images
9 What is Machine Vision? Human (Biological) vs Machine Vision Qualitative vs Quantitative People can rely on inference systems and assumptions for comprehension Computers do not 'see' in the same way that human beings are able to Examine individual pixels of images, processing them and attempting to develop conclusions in a consistent manner Spectral limitations of human vision (only visible spectrum) Passive vs Active (we rely on external illumination to see)
10 A & B are of different gray shades? Human Perception
11 A Machine Vision System Sensor Image Acquisition Pre-processing & Enhancement Decision Making Image Analysis Feature Extraction A B
12 Image Acquisition Vision properties Physics (Optics) Illumination Sensors Image representation Binary Grayscale Color Binary/ Grayscale IP Binarization Thresholding Geometry Area Position Orientation Edge/line detection Spatial filters /masks Color IP RGB, HSI, CMY Principal component analysis Feature Extraction Model-based Vision Hough Transform Circle Line Generalized Matching Similar Triangles Templates Scale-space filtering Curvature Morphology Algorithms/ operations Translation Reflection Complement Difference Dilation Erosion Pre- Processing & Enhancement Histogram Equalization Segmentation Labeling Camera Model/ Calibration 2D Image 3D world correspondence Intrinsic para Focal length,etc Extrinsic para Coordinate transformation matrix,etc Hands-eye calibration Geometrical Methods Geometry of multiple views & reconstruction Point Estimation Pose Estimation Image Analysis Neural Network Classifier Basics Sigmoid function Training algorithms Learning rules Weights updates Applications Motion Motion field Visual servoing
13 Image Acquisition (Hardware) Video Camera TV-based RS-170 standard (30 fps) Decision Frame Grabber Computer Program (processing) Video Buffer Computer Memory (RAM) Smart Cameras External Controller
14 Image Formation Image sensor CCD, CID, CMOS, vidicon tube (visible spectrum) X-ray, MRI, sonar, thermal, etc Sensor arrangement single/array/area Optics Pin-hole camera Thin lens equation Illumination & lighting Surface texture & reflectance Objects Background Light source sensor Optics reflectance OBJECT Captured image
15 Illumination Surfaces Diffuse Appear equally bright from all viewing directions Absorbs no light Paper & matte paints Specular Incident angle = reflected angle mirrors Retroreflective Returns most of incident radiation in the direction close to where it originated 3M s Scotchlite
16 Retroreflective Characteristics 3M Scotchlite Relative reflectance compared to flat white surface
17 Retroreflective illumination Object-Diffuse Background-Retroreflective
18 Retroreflective illumination Object-Retroreflective Background-Diffuse
19 Sensor Device Arrangement Single pixel Line of pixels 2-Dimension area of pixels
20 Sensor Device Arrangement Image acquisition using single, line sensor configurations
21 Image Sensors Charge-Coupled Device (CCD) Complementary metal oxide semiconductor (CMOS) or Active pixel sensor (APS) Charge Injection Device (CID) Similar to CCD except in the way the electric charge is transferred before it is recorded CMOS Vidicon tube Cathode ray tube technology CCD Photonics Spectra 2001
22 CCD Architecture Full-frame transfer Row-by-row Non-uniform exposure brighter Uniform exposure Pixel-by-pixel Mechanical shutter Simultaneous acquisition and reading Frame-transfer output Register darker clock Electronic shutter Duplicate array so that image can be read slowly from the storage region while a new image is exposing in active area output STORAGE Register clock Interline transfer 1 pixel transfer from image to storage Imaging area is reduced 50% output Register clock
23 Optics Pinhole camera The most basic/elementary imaging device Projection of image without lens Hole size Smaller: better focus, darker image Larger: blurred image, Brighter image Issues: Low-light accumulation capability Light diffraction Bending of light as it passes an aperture DeMenthon, 2000
24 The Optical Lens Optical lens allow creation of a brighter image by converging light DeMenthon, 2000
25 Thin Lens Equation f o =f i ideal lens, By similar triangles, do zo fi d f z i o i z z f f f i o i o 2 Focal Length, f z i Z z f i o z z f i o Z f z f f f o i i o Z z f z o Z Ideal thin lens equation fo fi z
26 Lens Imaging Points at other distances are imaged as little circles (red) By ideal lens equation: Blur circle definition z z f & similar triangles, d f f d z z z z f z f d z z z d Blur size decreases with focal length f=0, blur is a point z z z ' To attain a clear image, diameter of blur circle is less than resolution of imaging device
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