MACHINE VISION Euripides G.M. Petrakis http://www.ced.tuc/~petrakis - PowerPoint PPT Presentation

TECHNICAL UNIVERSITY OF CRETE DEPARTMENT OF ELECTRONIC AND COMPUTER ENGINEERING MACHINE VISION Euripides G.M. Petrakis http://www.ced.tuc/~petrakis Chania 2001 E.G.M. Petrakis Machine Vision (Introduction) 1

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 E.G.M. Petrakis Machine Vision (Introduction) 2

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). E.G.M. Petrakis Machine Vision (Introduction) 3

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

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

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). E.G.M. Petrakis Machine Vision (Introduction) 6

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. E.G.M. Petrakis Machine Vision (Introduction) 7

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

Pattern Recognition Model Matching Pattern Recognition Image/regions � •Measurements, or Class identifier •Structural description • 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). E.G.M. Petrakis Machine Vision (Introduction) 9

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)} E.G.M. Petrakis Machine Vision (Introduction) 10

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. E.G.M. Petrakis Machine Vision (Introduction) 11

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

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. E.G.M. Petrakis Machine Vision (Introduction) 13

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. E.G.M. Petrakis Machine Vision (Introduction) 14

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. E.G.M. Petrakis Machine Vision (Introduction) 15

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

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

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 E.G.M. Petrakis Machine Vision (Introduction) 18

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

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

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. E.G.M. Petrakis Machine Vision (Introduction) 21

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. E.G.M. Petrakis Machine Vision (Introduction) 22

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. E.G.M. Petrakis Machine Vision (Introduction) 23

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. E.G.M. Petrakis Machine Vision (Introduction) 24

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

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

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? E.G.M. Petrakis Machine Vision (Introduction) 27

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

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. E.G.M. Petrakis Machine Vision (Introduction) 29

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). E.G.M. Petrakis Machine Vision (Introduction) 30