1. E.G.M. PetrakisMachine Vision (Introduction)1 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

2. E.G.M. PetrakisMachine 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

3. E.G.M. PetrakisMachine 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).

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

5. E.G.M. PetrakisMachine Vision (Introduction)5 Machine Vision Stages 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 Acquisition (by cameras, scanners etc) Model Matching Pattern Recognition Image Analysis (Binary Image Processing) Image Segmentation Image Processing Image Enhancement Image Restoration

6. E.G.M. PetrakisMachine Vision (Introduction)6 Image Processing 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). Image Processing Input ImageOutput Image

7. E.G.M. PetrakisMachine Vision (Introduction)7 Image Segmentation 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. Image Segmentation Input ImageRegions/Objects

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

9. E.G.M. PetrakisMachine Vision (Introduction)9 Pattern Recognition 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). Model Matching Pattern Recognition Image/regions  Measurements, or Structural description Class identifier

10. E.G.M. PetrakisMachine 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 = {R red (x,y), G green (x,y), B blue (x,y)}

11. E.G.M. PetrakisMachine 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.

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

13. E.G.M. PetrakisMachine 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.

14. E.G.M. PetrakisMachine 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.

15. E.G.M. PetrakisMachine 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.

16. E.G.M. PetrakisMachine Vision (Introduction)16 Structure of PR Systems input Sensor Measurements Processing Classification class

17. E.G.M. PetrakisMachine Vision (Introduction)17 Example of Statistical PR Two classes: I.W 1 Basketball players II.W 2 jockeys Description: X = (X 1, X 2 ) = (height, weight) X1X1 X2X2......... ….. ….... …… W2W2 W1W1 D(X) = AX 1 + BX 2 + C = 0 Decision function - +

18. E.G.M. PetrakisMachine 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

19. E.G.M. PetrakisMachine Vision (Introduction)19 Primitives G 1,L(G 1 ) : submedian Grammar G 2,L(G 2 ) : telocentric Grammar

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

21. E.G.M. PetrakisMachine 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.

22. E.G.M. PetrakisMachine 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.

23. E.G.M. PetrakisMachine 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.

24. E.G.M. PetrakisMachine 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.

25. E.G.M. PetrakisMachine Vision (Introduction)25 Machine Vision Applications Robotics Medicine Remote Sensing Cartography Meteorology Quality inspection Reconnaissance

26. E.G.M. PetrakisMachine 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.

27. E.G.M. PetrakisMachine 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?

28. E.G.M. PetrakisMachine 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 “+”

29. E.G.M. PetrakisMachine 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.

30. E.G.M. PetrakisMachine 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).

31. E.G.M. PetrakisMachine 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

32. E.G.M. PetrakisMachine Vision (Introduction)32 Human “Hardware” Photoreceptors take measurements of light signals. –About 10 6 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.

33. E.G.M. PetrakisMachine 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.

34. E.G.M. PetrakisMachine 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.

35. E.G.M. PetrakisMachine 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.

36. E.G.M. PetrakisMachine 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

37. E.G.M. PetrakisMachine 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.

38. E.G.M. PetrakisMachine 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)http://www.intelligence.tuc/~petrakis Webcourses (http://courses.ece.tuc.gr)http://courses.ece.tuc.gr

39. E.G.M. PetrakisMachine Vision (Introduction)39 Grading Scheme Final Exam (F): 40%, min 5 Assignments (Α): 40% Two assignments –Obligatory