Image pattern recognition and its applications
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Image Pattern Recognition and Its Applications. Chaur-Chin Chen ( 陳朝欽 ) Institute of Information Systems & Applications (Department of Computer Science) National Tsing Hua University HsinChu ( 新竹 ), Taiwan ( 台灣 ) [email protected] May 3, 2013. Outline. Fundamental Image Processing

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Image pattern recognition and its applications

Image Pattern Recognition and Its Applications

Chaur-Chin Chen (陳朝欽)

Institute of Information Systems & Applications

(Department of Computer Science)

National Tsing Hua University

HsinChu (新竹), Taiwan (台灣)

[email protected]

May 3, 2013


Outline

Outline

  • Fundamental Image Processing

  • Fingerprint and Face Verification

  • Supervised vs. Unsupervised Learning

  • Watermarking and Steganography

  • Microarray Image Analysis

  • Some Other Application


Outline continuation

Outline (Continuation)

  • Some Other Applications

  • Supervised vs. Unsupervised Learning

  • Data Description and Representation

  • 8OX and iris Data Sets

  • Dendrograms of Hierarchical Clustering

  • PCA vs. LDA

  • A Comparison of PCA and LDA


Fundamental image processing

Fundamental Image Processing

♪ A Digital Image Processing System

  • Image Representation and Formats

    1.Sensing, Sampling, Quantization

    2. Gray level and Color Images

    3. Raw, RGB, Tiff, BMP, JPG, GIF, (JP2)

  • Image Transform and Filtering

  • Histogram, Enhancement

  • Segmentation, Edge Detection, Thinning

  • Image Data Compression

  • Fingerprint and Face Recognition

  • Image Pattern Recognition

  • Watermarking and Steganography

  • Microarray Image Data Analysis

    [1] R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital Image Processing Using MATLAB, Pearson Prentice Hall, 2004

    [2] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Prentice-Hall, 2002+


Image processing system

Image Processing System

  • A 2D image is nothing but a mapping from a region to a matrix

  • A Digital Image Processing System consists of

    1. Acquisition – scanners, digital camera, ultrasound,

    X-ray, MRI, PMT

    2. Storage – HD (500GB, TeraBytes, PeraBytes, …), CD (700 MB),

    DVD (4.7 GB), Flash memory (2~32 GB)

    3. Processing Unit – PC, Workstation (Sun Microsystems), PC-cluster

    4. Communication – telephone lines, cable, wireless, Wi-Fi, LTE

    5. Display – LCD monitor, laser printer, smart phone, i-Pad


Illustration of image processing system

Illustration of Image Processing System


Gray level and c o l o r images

Gray Level and Color Images


Pixel s in a gray level image

Pixels in a Gray Level Image


A gray level image is a matrix

A Gray Level Image is a Matrix

f(0,0) f(0,1) f(0,2) …. …. f(0,n-1)

f(1,0) f(1,1) f(1,2) …. …. f(1,n-1)

. . .

. . .

. . .

f(m-1,0) f(m-1,1) f(m-1,2) … …. f(m-1,n-1)

An image of m rows, n columns, f(i,j) is in [0,255]


Image representation gray c o l o r

Image Representation (Gray/Color)

  • A gray level image is usually represented by an M x N matrix whose elements are all integers in {0,1, …, 255} corresponding to brightness scales

  • A color image is usually represented by 3 M x N matrices whose elements are all integers in {0,1, …, 255} corresponding to 3 primary primitives of colors such as Red, Green, Blue


Gray and color image data

Gray and Color Image Data

  • 0, 64, 144, 196,

    225, 169, 100, 36

    (R, G, B) for a color pixel

    Red – (255, 0, 0)

    Green – ( 0, 255, 0)

    Blue – ( 0, 0, 255)

    Cyan – ( 0,255, 255)

    Magenta – (255, 0, 255)

    Yellow – (255, 255, 0)

    Gray – (128, 128, 128)


Rgb hex triplet color chart

Red = FF0000

Green = 00FF00

Blue = 0000FF

Cyan = 00FFFF

Magenta= FF00FF

Yellow = FFFF00

RGB Hex Triplet Color Chart


Koala and its r g b components

Koala and Its RGB Components


R g b his to grams of koala

(R,G,B) Histograms of Koala


Sensing sampling quantization

Sensing, Sampling, Quantization

  • A 2D digital image is formed by a sensor which maps a region to a matrix

  • Digitization of the spatial coordinates (x,y) in an image function f(x,y) is called Sampling

  • Digitization of the amplitude of an image function f(x,y) is called Quantization


Sampling and quantization

Sampling and Quantization


Image file formats 1 2

The American National Standards Institute (ANSI) sets standards for voluntary use in US. One of the most popular computer standards set by ANSI is the American Standard Code for Information Interchange (ASCII) which guarantees all computers can exchange text in ASCII format

BMP – Bitmap format from Microsoft uses Raster-based 1~24-bit colors (RGB) without compression or allows a run-length compression for 1~8-bit color depths

GIF – Graphics Interchange Format from CompuServe Inc. is Raster-based which uses 1~8-bit colors with resolutions up to 64,000*64,000 LZW (Lempel-Ziv-Welch, 1984) lossless compression with the compression ratio up to 2:1

Image File Formats (1/2)


Some image file formats 2 2

Some Image File Formats (2/2)

  • Raw – Raw image format uses a 8-bit unsigned character to store a pixel value of 0~255 for a Raster-scanned gray image without compression. An R by C raw image occupies R*C bytes or 8RC bits of storage space

  • TIFF – Tagged Image File Format from Aldus and Microsoft was designed for importing image into desktop publishing programs and quickly became accepted by a variety of software developers as a standard. Its built-in flexibility is both a blessing and a curse, because it can be customized in a variety of ways to fit a programmer’s needs. However, the flexibility of the format resulted in many versions of TIFF, some of which are so different that they are incompatible with each other

  • JPEG – Joint Photographic Experts Group format is the most popular lossy method of compression, and the current standard whose file name ends with “.jpg” which allows Raster-based 8-bit grayscale or 24-bit color images with the compression ratio more than 16:1 and preserves the fidelity of the reconstructed image

  • EPS – Encapsulated PostScript language format from Adulus Systems uses Metafile of 1~24-bit colors with compression

  • JP2 - JPEG 2000 based on 5/3 and 9/7 wavelet transforms


Image transforms and filtering

Image Transforms and Filtering

  • Feature Extraction – find all ellipses in an image

  • Bandwidth Reduction – eliminate the low contrast “coefficients”

  • Data Reduction – eliminate insignificant coefficients of Discrete Cosine Transform (DCT), Wavelet Transform (WT)

  • Smooth filtering can get rid of noisy signals


Discrete cosine transform

Discrete Cosine Transform

Partition an image into nonoverlapping 8 by 8 blocks, and apply a 2d DCT on each block to get DC and AC coefficients.

Most of the high frequency coefficients become insignificant, only the DC term and some low frequency AC coefficients are significant.

Fundamental for JPEG Image Compression


Discrete cosine transform dct

Discrete Cosine Transform (DCT)

X: a block of 8x8 pixels

A=Q8: 8x8 DCT matrix as

shown above

Y=AXAt


Quantized dct coefficients on a 8x8 block

Quantized DCT Coefficients on a 8x8 Block


Lenna image vs compressed lenna

Lenna Image vs. Compressed Lenna


Wavelet transform

Wavelet Transform

  • Haar, Daubechies’ Four, 9/7, 5/3 transforms

  • 9/7, 5/3 transforms was selected as the lossy and lossless coding standards for JPEG2000, respectively

  • A Comparison of JPEG and JPEG2000 shows that the latter is slightly better than the former, however, to replace image.jpg by image.jp2 needs time


3 scale wavelet transforms

3-Scale Wavelet Transforms


Mean and median filtering

X1 X2 X3

X4 X0 X5

X6 X7 X8

Replace the X0 by the

mean of X0~X8 is

called “mean filtering”

X1 X2 X3

X4 X0 X5

X6 X7 X8

Replace the X0 by the

median of X0~X8 is

called “median filtering”

Mean andMedian Filtering


Example of median filtering

Example of Median Filtering


Image and its histogram

Image and Its Histogram


Enhancement and restoration

Enhancement and Restoration

  • The goal of enhancementis to accentuate certain features for subsequent analysis or image display. The enhancement process is usually done interactively

  • The restoration is a process that attempts to reconstruct or recover an image that has been degraded by using some unknown phenomenon


Example of image enhancement

Example of Image Enhancement

  • Support that A(i, j) is image gray level at pixel (i, j), μ and s2 are the mean and variance of gray levels of input image, and α=150, γ=95, γ must satisfy γ>s.

    The enhanced image B( i , j ) is obtained by a contrast stretching given below

  • B( i , j ) α + γ * ([A ( i , j ) – μ]/s)


Result of image enhancement

Result of Image Enhancement


Segmentation and edge detection

Segmentation and Edge Detection

  • Segmentation is basically a process of pixel classification: the picture is segmented into subsets by assigning the individual pixels into classes

  • Edge Detection is to find the pixels whose gray values or colors being abruptly changed


Image lenna and its histogram

Image Lenna and Its Histogram


Image segmentation algorithms

Image Segmentation Algorithms

  • Otsu (1979)

  • Fisher (1936)

  • Kittler and Illingworth (1986)

  • Vincent and Soille (1991)

  • Besag, Chen and Dubes (1986, 1991)


A simple thresholding algorithm 1

A Simple Thresholding Algorithm(1)


Image histogram thresholding

Image, Histogram, Thresholding


Binarization by thresholding

Binarization by Thresholding


Icm segmentation algorithm

ICM Segmentation Algorithm

1. Given an image Y, initialize a labeling X

2. For t=1:mxn

X(t)←g0 if

Pr(X(t)=g0|XN(t),Y) > Pr(X(t)=g|XN(t),Y) for g,g0

3. Repeat step 2 until “convergence” (6 runs)

4. X is the required labeling

Chaur-Chin Chen and Richard C. Dubes

Environmental Studies and ICM Segmentation Algorithm,

Journal of Information Science and Engineering,

Vol. 6, 325-337, 1990.


Image segmentation icm vs otsu

Image Segmentation: ICM vs. Otsu


Image segmentation icm vs otsu1

Image Segmentation: ICM vs. Otsu


Image segmentation icm vs otsu2

Image Segmentation: ICM vs. Otsu


Edge detection

Edge Detection

-1 -2 -1

0 0 0  X

1 2 1

-1 0 1

-2 0 2  Y

-1 0 1

Large (|X|+|Y|)  Edge


Thinning and contour tracing

Thinning and Contour Tracing

  • Thinning is to find the skeleton of an image which is commonly used for Optical Character Recognition (OCR) and Fingerprint matching

  • Contour tracing is usually used to locate the boundaries of an image which can be used in feature extraction for shape discrimination


Image edge skeleton contour

Image Edge, Skeleton, Contour


Image data compression

Image Data Compression

  • The purpose is to save storage space and to reduce the transmission time of information. Note that it requires 6 mega bits to store a 24-bit color image of size 512 by 512. It takes 6 seconds to download such an image via an ADSL (Asymmetric Digital Subscriber Line) with the rate 1 mega bits per second and more than 12 seconds to upload the same image

  • Note that 1 byte = 8 bits, 3 bytes = 24 bits


Training images for vq

Training Images for VQ


Lbg algorithm for codebook generation

LBG Algorithm for Codebook Generation


Codebook and decoded images

Codebook and Decoded Images


Some applications

Some Applications

  • Fingerprint and Face Recognition

  • Watermarking and Steganography

  • Image Pattern Recognition

  • Microarray Image Data Analysis


Image pattern recognition and its applications

美國啟用出入境指紋及人臉影像辨識系統

  • 美國國土安全部基於安全考慮,自(2004)元月五日起,啟用數位化出入境身分辨識系統(US-VISIT),大部分來美的14歲至79歲旅客,包括來自台灣、大陸、香港的留學生,於進入美國國際機場及港口時,都要接受拍照及留下指紋掃描紀錄以便辨識查核。(27個免簽證國公民之入境待遇略有不同,短期來美者,將受豁免。),亦將需接受指紋掃描查核。 


Us visit

US-VISIT

  • US-VISIT currently applies to all visitors (with limited exemptions) holding non-immigrant visas, regardless of country of origin.

  • 2004 – US$ 330 million

  • 2005 – US$ 340 million

  • 2006 – US$ 340 million

  • 2007 – US$ 362 million

  • 2009 – US$ ??? million


2007 11 20

入境按指紋 日本2007/11/20實施

  • 日本入境排隊長 指紋掃瞄會更長!(2007年9月27日)

  • 入境日本將按指紋 日官員赴台宣導新措施(2007年9月27日)

  • 日11月20日實施外國人入境須按指紋臉部照片(2007年9月25日)

  • 入境按指紋 日本11月將實施(2007年9月2日)


A typical fingerprint image

A Typical Fingerprint Image


Flowchart of an afis

Flowchart of An AFIS


Image pattern recognition and its applications

(a) Original image (b) Enhanced image

(c) Binarization image (d) Smoothed image


Thinning 9

Thinning[9]

  • The purpose of thinning stage is to gain the skeleton structure of a fingerprint image.

  • It reduces a binary image consisting of ridges and valleys into a ridge map of unit width.

    (d) Smoothed image (e) Thinned image


Minutiae definition

Minutiae Definition

♫From a thinned image, we can classify each ridge pixel into the following categories according to its 8-connected neighbors.

♫A ridge pixel is called :

  • an isolated point if it does not contain any 8-connected neighbor.

  • an ending if it contains exactly one 8-connected neighbor.

  • an edgepoint if it has two 8-connected neighbors.

  • a bifurcation if it has three 8-connected neighbors.

  • a crossing if it has four 8-connected neighbors.


Example of minutiae extraction

Example of Minutiae Extraction


Minutiae pattern matching

Minutiae Pattern Matching


Is this lady in your database

Is this Lady in your database?


Part of 5 40 training face images

Part of 5*40 Training Face Images


Missed face images and their wrongly best matched images

Missed Face Images and Their Wrongly-Best Matched Images


Are they the same person

Are They the Same Person?


Challenges and opportunities

Challenges and Opportunities

  • A perfect biometric recognition system did not exist and will never exists

  • An application based on biometrics usually requests a perfect verification/identification

  • A collection of biometric data is usually time consuming and more or less intrudes personal privacy

  • The mechanism of achieving the trade-off between privacy and security merits studies.


Supervised learning problems

Supervised Learning Problems

☺The problem of supervised learning can be

defined as to design a function which takes the

training data xi(k), i=1,2, …ni, k=1,2,…, C, as input

vectors with the output as either a single category

or a regression curve.

☺The unsupervised learning (Cluster Analysis) is

similar to that of the supervised learning problem

(Pattern Recognition) except that the categories

are unknown in the training data.


Distinguish eggplants from bananas

Distinguish EggplantsfromBananas

1. Features(characteristics)

Colors

Shapes

Size

Tree leaves

Other quantitative measurements

2. Decision rules: Classifiers

3. Performance Evaluation

4. Classification


Possum dingo fox wombat

Possum, Dingo, Fox, Wombat


Watermarking and steganography

Watermarking andSteganography

  • Watermarking is the practice of hiding a message about an image, audio clip, video clip, or other work of media within that work itself.

  • Steganography is the art of writing in cipher, or in character, which are not intelligible except to persons who have the key. In computer terms, steganography has evolved into the practice of hiding a message within a larger one in such a way that others cannot discern the presence or contents of the hidden message.


Examples of watermarking and steganography

Examples of Watermarking and Steganography


Difference between watermarking and steganography

Watermarking

Insert a logo, pattern, a

message, and etc. into

an image, audio, video

to claim the ownership.

Steganography

Put a cover image,

audio, video, and etc.

on a secret message to

protect the secrecy

during the transmission.

Differencebetween Watermarking andSteganography


An example of steganography

An Example of Steganography

  • The Precious Night

  • by Tsui Ping

  • The southern winds lightly kiss my face, with the heavy scent of blossms

  • The southern winds lightly kiss my? face, but the stars are sparse and the moon veiled

  • We lie against each other, exchanging endless words of love

  • We lie against each other, meaning everything we say

  • We don't care that tomorrow we may bid each other farewell

  • But remember tonight, and treasure it

  • On the eve of parting, we rue the sun's imminent rising

  • Lingering before parting, we promise to meet in a dream


Microarray image data analysis

Microarray Image Data Analysis


Microarray image data analysis1

Microarray Image Data Analysis

Each gene expression

is a feature which is

measured as average

spot brightness

Top: Tumor Tissues

Bottom: Normal Tissues


Bar code and qr code

Bar Code and QR code


Face and fingerprint images

Face and Fingerprint Images


License plate

License Plate


Fort san domingo

Fort San Domingo (淡水紅毛城)

Entrance Gate

Dutch Clogs


Image pattern recognition and its applications

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Thank you for your attention

Thank You For Your Attention

Questions and Comments


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