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TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING

TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING. MACHINE VISION Euripides G.M. Petrakis Michalis Zervakis http://www.intelligence.tuc/~petrakis http://courses.ece.tuc.gr Chania 2010. Machine Vision.

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TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING

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  1. TECHNICAL UNIVERSITY OF CRETEDEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING MACHINE VISION Euripides G.M. Petrakis Michalis Zervakis http://www.intelligence.tuc/~petrakis http://courses.ece.tuc.gr Chania 2010 Machine Vision (Introduction)

  2. Machine Vision • The goal of Machine Vision is to create a model of the real world from images • A machine vision system recovers useful information about a scene from its two dimensional projections • The world is three dimensional • Two dimensional digitized images Machine Vision (Introduction)

  3. Machine Vision (2) • Knowledge about the objects (regions) in a scene and projection geometry is required. • The information which is recovered differs depending on the application • Satellite, medical images etc. • Processing takes place in stages: • Enhancement, segmentation, image analysis and matching (pattern recognition). Machine Vision (Introduction)

  4. Illumination Image Acquisition Machine Vision System Scene 2D Digital Image Image Description Feedback The goal of a machine vision system is to compute a meaningful description of the scene (e.g., object)

  5. Machine Vision Stages Image Acquisition (by cameras, scanners etc) • Analog to digital conversion • Remove noise/patterns, improve contrast • Find regions (objects) in the image • Take measurements of objects/relationships • Match the above description with similar description of known objects (models) Image Processing Image Enhancement Image Restoration Image Segmentation Image Analysis (Binary Image Processing) Model Matching Pattern Recognition Machine Vision (Introduction)

  6. Image Processing Image Processing Input Image Output Image • Image transformation • image enhancement (filtering, edge detection, surface detection, computation of depth). • Image restoration (remove point/pattern degradation: there exist a mathematical expression of the type of degradation like e.g. Added multiplicative noise, sin/cos pattern degradation etc). Machine Vision (Introduction)

  7. Image Segmentation Image Segmentation Input Image Regions/Objects • Classify pixels into groups (regions/objects of interest) sharing common characteristics. • Intensity/Color, texture, motion etc. • Two types of techniques: • Region segmentation: find the pixels of a region. • Edge segmentation: find the pixels of its outline contour. Machine Vision (Introduction)

  8. Image Analysis Image Analysis Input Image Segmented Image (regions, objects) Measurements • Take useful measurements from pixels, regions, spatial relationships, motion etc. • Grey scale / color intensity values; • Size, distance; • Velocity; Machine Vision (Introduction)

  9. Pattern Recognition Model Matching Pattern Recognition • Image/regions  • Measurements, or • Structural description Class identifier • Classify an image (region) into one of a number of known classes • Statistical pattern recognition (the measurements form vectors which are classified into classes); • Structural pattern recognition (decompose the image into primitive structures). Machine Vision (Introduction)

  10. Digital Image Representation • Image: 2D array of gray level or color values • Pixel: array element; • Pixel value: arithmetic value of gray level or color intensity. • Gray level image: f = f(x,y) - 3D image f=f(x,y,z) • Color image (multi-spectral) f = {Rred(x,y), Ggreen(x,y), Bblue(x,y)} Machine Vision (Introduction)

  11. What a computer “sees” is very different from what a human sees. A computer sees pixels (arithmetic values) while a human sees shapes, structures etc. Machine Vision (Introduction)

  12. Relationships to other fields • Image Processing (IP) • Pattern Recognition (PR) • Computer Graphics (CG) • Artificial Intelligence (AI) • Neural Networks (NN) • Psychophysics Machine Vision (Introduction)

  13. Image Processing (IP) • IP transforms images to images • Image filtering, compression, restoration • IP is applied at the early stages of machine vision. • IP is usually used to enhance particular information and to suppress noise. Machine Vision (Introduction)

  14. Pattern Recognition (PR) • PR classifies numerical and symbolic data. • Statistical: classify feature vectors. • Structural: represent the composition of an object in terms of primitives and parse this description. • PR is usually used to classify objects but object recognition in machine vision usually requires many other techniques. Machine Vision (Introduction)

  15. Statistical Pattern Recognition • Pattern: the description of an an object • Feature vector • (size, roundness, color, texture) • Pattern class: set of patterns with similar characteristics. • Take measurements from a population of patterns. • Classification: Map each pattern to a class. Machine Vision (Introduction)

  16. Structure of PR Systems input Sensor Processing Measurements Classification class Machine Vision (Introduction)

  17. Example of Statistical PR • Two classes: • W1Basketball players • W2 jockeys • Description: X = (X1, X2) = (height, weight) X1 W1 .. …… . … .. … … + W2 . . . . . .. . .. .. D(X) = AX1 + BX2 + C = 0 Decision function - X2 Machine Vision (Introduction)

  18. Syntactic Pattern Recognition • The structure is important • Identify primitives • E.g., Shape primitives • Break down an image (shape) into a sequence of such primitives. • The way the primitives are related to each other to form a shape is unique. • Use a grammar/algorithm • Parse the shape Machine Vision (Introduction)

  19. Primitives • G1,L(G1) : submedian Grammar • G2,L(G2) : telocentric Grammar Machine Vision (Introduction)

  20. Each digit is represented by a waveform representing • black/white, white/black transitions (scan the image from • Left to right. Machine Vision (Introduction)

  21. Computer Graphics (CG) • Machine vision is the analysis of images while CG is the decomposition of images: • CG generates images from geometric primitives (lines, circles, surfaces). • Machine vision is the inverse: estimate the geometric primitives from an image. • Visualization and virtual reality bring these two fields closer. Machine Vision (Introduction)

  22. Artificial Intelligence (AI) • Machine vision is considered to be sub-field of AI. • AI studies the computational aspects of intelligence. • CV is used to analyze scenes and compute symbolic representations from them. • AI: perception, cognition, action • Perception translates signals to symbols; • Cognition manipulates symbols; • Action translates symbols to signals that effect the world. Machine Vision (Introduction)

  23. Psychophysics • Psychophysics and cognitive science have studied human vision for a long time. • Many techniques in machine vision are related to what is known about human vision. Machine Vision (Introduction)

  24. Neural Networks (NN) • NNs are being increasingly applied to solve many machine vision problems. • NN techniques are usually applied to solve PR tasks. • Image recognition/classification. • They have also applied to segmentation and other machine vision tasks. Machine Vision (Introduction)

  25. Machine Vision Applications • Robotics • Medicine • Remote Sensing • Cartography • Meteorology • Quality inspection • Reconnaissance Machine Vision (Introduction)

  26. Robot Vision • Machine vision can make a robot manipulator much more versatile. • Allow it to deal with variations in parts position and orientation. Machine Vision (Introduction)

  27. Remote Sensing • Take images from high altitudes (from aircrafts, satellites). • Find ships in the aerial image of the dock. • Find if new ships have arrived. • What kind of ships? Machine Vision (Introduction)

  28. Remote Sensing (2) • Analyze the image • Generate a description • Match this descriptions with the descriptions of empty docs • There are four ships • Marked by “+” Machine Vision (Introduction)

  29. Medical Applications • Assist a physician to reach a diagnosis. • Construct 2D, 3D anatomy models of the human body. • CG geometric models. • Analyze the image to extract useful features. Machine Vision (Introduction)

  30. Machine Vision Systems • There is no universal machine vision system • One system for each application • Assumptions: • Good lighting; • Low noise; • 2D images • Passive - Active environment • Changes in the environment call for different actions (e.g., turn left, push the break etc). Machine Vision (Introduction)

  31. Vision by Man and Machine • What is the mechanism of human vision? • Can a machine do the same thing? • There are many studies; • Most are empirical. • Humans and machines have different • Software • Hardware Machine Vision (Introduction)

  32. Human “Hardware” • Photoreceptors take measurements of light signals. • About 106 Photoreceptors. • Retinal ganglion cells transmit electric and chemical signals to the brain • Complex 3D interconnections; • What the neurons do? In what sequence? • Algorithms? • Heavy Parallelism. Machine Vision (Introduction)

  33. Machine Vision Hardware • PCs, workstations etc. • Signals: 2D image arrays gray level/color values. • Modules: low level processing, shape from texture, motion, contours etc. • Simple interconnections. • No parallelism. Machine Vision (Introduction)

  34. Course Outline • Introduction to machine vision, applications, Image formation, color, reflectance, depth, stereopsis. • Basic image processing techniques (filtering, digitization, restoration), Fourier transform. • Binary image processing and analysis, Distance transform, morphological operators. Machine Vision (Introduction)

  35. Course Outline (2) • Image segmentation (region segmentation, edge segmentation). • Edge detection, edge enhancement and linking.  Thresholding, region growing, region merging/splitting. • Relaxation labeling, Hough transform. • Image analysis, shape analysis. Polygonal approximation, splines, skeletons. Shape features, multi-resolution representations. Machine Vision (Introduction)

  36. Course Outline (3) • Image representation, image - shape recognition and classification. Attributed relational graphs, semantic nets.  • Image - shape matching (Fourier descriptors, moments, matching in scale space). • Texture representation and recognition, statistical and structural methods. • Motion, motion detection, optical flow. • Video Machine Vision (Introduction)

  37. Bibliography • “Machine Vision”, Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, Mc Graw-Hill, 1995 (highly recommended!). • "Image Processing, Analysis and Machine Vision", Milan Sonka, Vaclav Hlavac, Roger Boyle, PWS Publishing, Second Edition. • "Machine Vision, Theory, Algorithms, Practicalities'', E. R. Davies, Academic Press, 1997. Machine Vision (Introduction)

  38. "Practical Computer Vision Using C'', J. R. Parker, John Wiley & Sons Inc., 1994. • Selected articles from the literature. • Lecture notes (http://www.intelligence.tuc/~petrakis) • Webcourses (http://courses.ece.tuc.gr) Machine Vision (Introduction)

  39. GradingScheme • Final Exam (F): 40%, min 5  • Assignments (Α): 40%  • Two assignments  • Obligatory Machine Vision (Introduction)

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