Feature Extraction for Classification : Hough Transform and Gabor Filtering Heikki Kälviäinen

H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany

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LAPPEENRANTA UNIVERSITY OF TECHNOLOGYTHE DEPARTMENT OF INFORMATION TECHNOLOGY

Intensive Program on Computer VisionIPCV 2002

July 22 – August 2, 2002 Koblenz, Germany

http://www.uni-koblenz.de/~lb/lb_activities/ipcv02/ipcv02.html


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Feature Extraction for Classification: Hough Transform and Gabor Filtering

Heikki KälviäinenProfessor

Computer Science

Laboratory of Information Processing

[email protected]

http/www.it.lut.fi/~kalviai


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Lappeenranta University of Lappeenranta University of TechnologTechnology, Finlandy, Finland


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L ondonBer lin

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S t.Petersburg

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Lappeenranta

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S tockholm

Hels ink i


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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


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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


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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

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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


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Fundamental Steps in Image Processing

• Image acquisition• Preprocessing• Segmentation• Representation and description• Recognition and interpretation

• Image processing system


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Robot Vision: Handling of Sheets in a Workshop


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Robotized Handling of Objects


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Automatic Cheese Factory (RTS, Ltd.) Video


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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!


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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


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• 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.


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• 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.


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Applications in Finland

• TEKES technology programs– Machine Vision (1992-1996) & Intelligent and Adaptive

Systems Applications (1995-2000) & Intelligent Automation Systems (2001-2004)

• Applications of – process control– robot vision – quality control

in electronics, metal, forest, food manufacturing, etc., industry & applications in business


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Visual Quality Control in Steel Manufacturing


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Robot Positioning: Deflection Compensation


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Visual Inspection on Wooden Surfaces


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Visual Inspection on Wooden Surfaces


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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, http://www.cis.hut.fi/research/demos.shtml)


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References

• R. C. Gonzalez & R.E. Woods, Digital Image Processing, Addison-Wesley, 1993.

• See more references for example at http://www.it.lut.fi/opetus/99-00/010588000/refs.html

• Applications: – Finland: Machine Vision 1992-1996. TEKES Technology

Programme Report 15/96. Final Report, 1996.

– LUT: http://www.it.lut.fi/research/ip/appl.html

– Systems: for example, RTS Group (www.rts-group.com)


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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.

sincos yx

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LAPPEENRANTA UNIVERSITY OF TECHNOLOGYTHE DEPARTMENT OF INFORMATION TECHNOLOGY Hough

Transform(SHT)


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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.


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Randomized Hough Transform (RHT)• Developed in Lappeenranta Universityof 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. 331-338.


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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.


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1. Infinite scope parameter space.

2. Arbitrarily high parameter resolution.

3. High computational speed.

4. Small storage.

Advances of RHT over SHT

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RHT Extensions

Kälviäinen, H.,Hirvonen, P.,

Xu, L.,Oja, E.,

”Probabilistic and Non-probabilistic

Hough Transforms:Overview andComparisons,”

Image and VisionComputing,

Vol. 13, No. 4, 1995,pp. 239-251.


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Feature extraction using Hough Transform

End-pointdetection

Encoding

Input Image

Feature Image

Hough Transform

FEATURE EXTRACTION

Line parameters

Reconstruction

Feature File

July 17, 2000 H. Kälviäinen, IPCV 2002, July 22 - August 2, 2002, Koblenz, Germany


Applications of Hough Transform

• Randomized Hough Transform (RHT)• Curve detection• Motion detection• Mixed pixel classification• Image compression• Vanishing point detection• Image databases• etc.



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


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Compression, Similarity, Matching, Object Recognition



Query images



Test database


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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.

Documents

Feature Extraction for Classification : Hough Transform and Gabor Filtering Heikki Kälviäinen