july 27, 2002 image processing for k.r. precision1 image processing training lecture 1 by suthep...

July 27, 2002 Image Processing for K.R. Precision 1

Image Processing Training Image Processing Training Lecture 1Lecture 1

by Suthep Madarasmi, Ph.D.

Assistant Professor

Department of Computer Engineering

King Mongkut’s University of Technology Thonburi


Presentation Overview Presentation Overview

1.0 Image Formation1.1 Image Formats and Types1.2 Image Formation Models1.3 Coordinate Transformations 1.4 Camera Calibration

2.0 Binary Images 2.1 Histograms 2.2 Thresholding Techniques2.3 Example: Rubber Inspection2.4 Binary Image Features2.5 Measurement and Accuracy

3.0 Image Filtering3.1 Convolution Operation3.2 Common Filters3.3 Template Matching3.4 Edge Detection3.4 Example: Paper 3.6 Texture Features 3.7 Example: Food

Introduction


Input Image

Output Image

Image Precessing

Image Processing:

• Image Enhancement

• Edge Finding

• Image Segmentation

Machine Vision:

• Scene Description

• Shape Information

• Object Recognition

What is Image ProcessingWhat is Image Processing

Introduction


Image Formation ModelImage Formation Model

1.0 Image Formation


Image Projection ModelImage Projection Model

Z

Xf

xx = Xf/Z; y = Yf/Z

1.0 Image Formation


Digital Image RepresentationDigital Image Representation

1.0 Image Formation


Images Spatial ResolutionImages Spatial Resolution

1.0 Image Formation


Gray Level ResolutionsGray Level Resolutions

1.0 Image Formation


Digital Image FormatsDigital Image Formats

RGB Images CMYK Images 256 Indexed Color Images 16 Indexed Color Images Gray Scale Images (8 bit) Gray Scale Images (4 bit) Black and White Images Image Types:

GIF, JPG, BMP, TIF, Multi-Page TIF, PDF, PS, RTF, etc.

1.1 Image Formats and Types


Coordinate TransformationsCoordinate Transformations

3-D Transformations: Rotation & Translation Object Coordinates 3D World Coordinates 3D Camera Coordinates 3D Image Coordinates 2D, Continuous Cartesian Image Coordinates 2D, Discreet Cartesian (x, y) Image Coordinates 2D, Device Independent (r, c) Image Coordinates 2D, Device Coordinates (x, y)

1.3 Coordinate Transformations


Camera CalibrationCamera Calibration

1.4 Camera Calibration


Binary ImagesBinary Images

1.0 Binary Images


Binary Image Assumed from Binary Image Assumed from 2 Sources with Gaussian 2 Sources with Gaussian NoiseNoise

2.0 Binary Images


Image HistogramImage Histogram

A histogram is the count of each gray scale within the image

Histogray may represent P[i], where i is gray value between 0..255.

Histograms are used to look at gray scale distribution for thresholding to binary image

Examples of Histograms

2.1 Histograms


Histogram Equalization: Histogram Equalization: ScalingScaling

2.1 Histograms


Histogram EqualizationHistogram Equalization

2.1 Histograms


P-Tile Method for ThresholdP-Tile Method for Threshold

2.2 Thresholding Techniques


Region Segmentation for Region Segmentation for Multiple ObjectsMultiple Objects



Problem with Single Problem with Single ThresholdThreshold



Automatic Threshold MethodAutomatic Threshold Method



Adaptive Thresholding by Adaptive Thresholding by RegionsRegions



Rubber Sheet InspectionRubber Sheet Inspection

2.3 Example: Rubber Inspection


Rubber: Multiple ThresholdRubber: Multiple Threshold

Input Image ofBackIlluminatedRubber Sheet

Preprocessing1: NoiseRemoval viaGaussian Blur

Preprocessing2: BoundaryErosion

Partition imageinto rectangularregions

AutomaticThresholding fromHistogram PerRegion

Threshold

BinaryImagePerRegion

AssembledBinaryImage ofDefects



b.

c.

d.

e.

a.

b.

c.

d.

e.

a.

b.

Rubber: Example OutputRubber: Example Output



Size Filter to Noisy Binary Size Filter to Noisy Binary ImageImage

2.4 Binary Image Features


Computing Position Computing Position (Centroid)(Centroid)



Iterative Thinning OperationsIterative Thinning Operations



Expanding and ShrinkingExpanding and Shrinking



Horizontal and Vertical Horizontal and Vertical ProjectionsProjections



Projections for OCRProjections for OCR



Image Convolution: FilteringImage Convolution: Filtering

3.0 Image Filtering, Correlation Operations


Salt / Pepper and Gaussian Salt / Pepper and Gaussian NoiseNoise

3.2 Common Filters


Applying the Mean FilterApplying the Mean Filter

3.2 Common Filtes


Result of 3x3, 5x5, and 7x7 Result of 3x3, 5x5, and 7x7 mean filtermean filter

3.2 Common Filters


Applying the Median FilterApplying the Median Filter

3.2 Common Filters


The Gaussian Filter: The Gaussian Filter: Continuous Continuous

3.2 Common Filters


Template MatchingTemplate Matching

Template matching is the sum squared difference between image and template

Very similar to Convolution Used for Recognition: OCR and Others

3.3 Template Matching


A Discreet (Digital) Gaussian A Discreet (Digital) Gaussian FilterFilter

3.2 Common Filters


Edges: First & Second Edges: First & Second DerivativesDerivatives

3.4 Edge Detection


LOG (Laplacian of Gaussian) LOG (Laplacian of Gaussian) FilterFilter

3.4 Edge Detection


LOG MasksLOG Masks

3.4 Edge Detection


Paper InspectionPaper Inspection

3.4 Example: Paper Inspection


Paper Inspection: DefectsPaper Inspection: Defects

3.4 Example: Paper Inspection


Texture Feature ExtractionTexture Feature Extraction

Texture: Statistical Distribution of Gray Co-Occurence Matrix captures distribution Texture Measures from Co-Occurence:

Entropy Energy Homogeneity

3.6 Texture Features


Food Inspection: TextureFood Inspection: Texture

3.7 Example: Food Inspection


Food Texture: Method & Food Texture: Method & ResultsResults

หา Co-occurrence matrixที่�� d[3,3] และ d[-3,3]

กรองเฉล��ย

LoG

3.7 Example: Food Inspection

july 27, 2002 image processing for k.r. precision1 image processing training lecture 1 by suthep...

Documents

image filtering

image histogray

d image coordinates

y image coordinates

c image coordinates

image formation models

binary image features

precision13 binary image