LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany1 Intensive Program on Computer Vision IPCV 2002 July 22 – August 2, 2002 Koblenz, Germany

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany2 Feature Extraction for Classification: Hough Transform and Gabor Filtering Heikki Kälviäinen Professor Computer Science Laboratory of Information Processing http/

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany3 Lappeenranta University of Technology, Finland

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany4

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany5 Contents Fundamentals of computer vision –Digital image processing –Pattern recognition & Machine vision –Fundamental steps in image processing –Applications Feature Extraction for Classification –Hough Transform –Gabor Filtering

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany6 Digital Image Processing R. C. Gonzalez & R.E. Woods, Digital Image Processing, Addison-Wesley, 1993 : “A digital image is an image f(x,y) that has been discretized both in spatial coordinates and brightness” f(x,y) is a 2D intensity function where x and y are spatial coordinates and the value of f at any point (x,y) is proportional to the brightness of the image at the point

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany7 Digital Image Processing A digital image consists of pixels (also called image elements, picture elements) For example: an image of a 256 x 256 array with 256 gray-levels (8 bits: 0 black, 255 white) –Binary images: only two values –Gray-level images: e.g. 256 values –Color images: three color components (e.g. RGB) –Spectral images: several components

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY 8

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany9 Pattern Recognition and Machine Vision A digital image is just a set of pixels ? Pattern recognition = measurements and observations from natural scenes and their automatic analysis and recognition Computer vision = image analysis using pattern recognition techniques Machine vision = application oriented image analysis

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany10 Fundamental Steps in Image Processing Image acquisition Preprocessing Segmentation Representation and description Recognition and interpretation Image processing system

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany11 Robot Vision: Handling of Sheets in a Workshop

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany12 Robotized Handling of Objects

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany13 Video Video Automatic Cheese Factory (RTS, Ltd.) VideoVideo

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany14 Requirements for Successful Applications Fast – No delays Accurate – Assist/replace human vision Not too expensive – Return on investment Easy to implement and to use – End users are experts in their own field only!

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany15 Applications (some areas) Recognition, classification, and tracking of objects –Face recognition, fingerprint detection –Speech recognition, motion detection –OCR, document processing, image databases Industrial applications –Visual quality control –Process automation –Robotics

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany16 Applications (some areas) Telecommunications – Image compression, video technology. Military applications –Tracking of objects, surveillance systems. Remote Sensing –Analysis of satellite images, classification of airplanes,spying, weather forecasts, forest fire detection, missile control.

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany17 Applications (some areas) Medical image processing – X-ray images, ultrasound images, images of cells, chromosomes, proteins. –Detection of tumors, cancer; assistance in operations. Chemistry, Biology, Physics, Astronomy – DNA, molecules, particles, planets.

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany18 Applications in Finland TEKES technology programs –Machine Vision ( ) & Intelligent and Adaptive Systems Applications ( ) & Intelligent Automation Systems ( ) Applications of –process control –robot vision –quality control in electronics, metal, forest, food manufacturing, etc., industry & applications in business

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany19 Visual Quality Control in Steel Manufacturing

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany20 Robot Positioning: Deflection Compensation

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany21 Visual Inspection on Wooden Surfaces

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany22 Visual Inspection on Wooden Surfaces

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany23 Other Applications Industrial Robot for Windscreen Grinding Quality Control in Printing Industry Punch Press Quality Assurance Classification of Parquet Pieces Controlled Wood Cutting Automatic Cheese Production Detection of Food Fatness Baking Better Biscuits Sorting Ceramic Tiles Multispectral Video Image databases (see, e.g. PICSOM, rch/demos.shtml)

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany24 References R. C. Gonzalez & R.E. Woods, Digital Image Processing, Addison-Wesley, See more references for example at Applications: –Finland: Machine Vision TEKES Technology Programme Report 15/96. Final Report, –LUT: –Systems: for example, RTS Group (

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany25 Hough Transform A method for global feature extraction: –y = a x + b => b = -x a + y. –For each pixel (x,y) compute a curve b = -x a + b into the parameter space. –Alternatively the normal presentation of a line: Hough Transform detects sets of pixels which represent geometric primitives in a binary image. Lines, circles, ellipses, arbitrary shapes, etc. Tolerant to noise and distortions in an image, but traditional versions suffer from problems with time and space complexities. New variants: probabilistic and deterministic Hough Transforms.

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY 26 Hough Transform (SHT)

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany27 The Kernel of the Hough Transform 1.Create the set D of all edge points in a binary picture. 2.Transform each point in the set D into a parameterized curve in the parameter space. 3.Increment the cells in the parameter space determined by the parametric curve. 4.Detect local maxima in the accumulator array. Each local maximum may correspond to a parametric curve in the image space. 5.Extract the curve segments using the knowledge of the maximum positions.

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany28 Randomized Hough Transform (RHT) Developed in Lappeenranta University of Technology (LUT), FINLAND. Xu, L., Oja, E., Kultanen, P, ”A New Curve Detection Method: Randomized Hough Transform (RHT), Pattern Recognition Letters, vol. 11, no. 5., 1990, pp

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany29 The Kernel of the Randomized Hough Transform (RHT) 1.Create the set D of all edge points in a binary edge picture. 2.Select a point pair (d_i, d_j) randomly from the set D. 3.If the points do not satisfy the predefined distance limits, go to Step 2; otherwise continue to Step 4. 4.Solve the parameter space point (a, b) using the curve equation with the points (d_i, d_j). 5.Accumulate the cell A(a, b) in the accumulator space. 6.If the A(a, b) is equal to the threshold t, the parameters a and b describe the parameters of the detected curve; otherwise continue to Step 2.

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany30 1.Infinite scope parameter space. 2.Arbitrarily high parameter resolution. 3.High computational speed. 4.Small storage. Advances of RHT over SHT

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY 31 RHT Extensions Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E., ”Probabilistic and Non-probabilistic Hough Transforms: Overview and Comparisons,” Image and Vision Computing, Vol. 13, No. 4, 1995, pp

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany32 Feature extraction using Hough Transform

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY Applications of Hough Transform Randomized Hough Transform (RHT) Curve detection Motion detection Mixed pixel classification Image compression Vanishing point detection Image databases etc.

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY Application of Hough Transform for image databases Content-based matching of line-drawing images using Hough Transform Similarity of images in image databases Hough Transform as a feature extractor Translation-, rotation-, and scale-invariant features from the accumulator matrix

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany35 Compression, Similarity, Matching, Object Recognition

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY Query images

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY Test database

LAPPEENRANTA UNIVERSITY OF TECHNOLOGY THE DEPARTMENT OF INFORMATION TECHNOLOGY H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany38 Image Processing Using Gabor Filtering For local and global feature extraction. Filtering in time (spatial) space and frequency space. For image processing and analysis two important parameters: frequency f and orientation theta. More information: –Gabor lecture notes 1: (IPCV2002_Gabor1.ps) Introduction to the theory of Gabor functions. –Gabor lecture notes 2: (IPCV2002_Gabor2.ps) Image analysis using Gabor filtering: practice and applications.