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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

  • 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

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
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
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


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
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
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)
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 segmentation algorithms
Image Segmentation Algorithms
  • Otsu (1979)
  • Fisher (1936)
  • Kittler and Illingworth (1986)
  • Vincent and Soille (1991)
  • Besag, Chen and Dubes (1986, 1991)
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.

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 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
some applications
Some Applications
  • Fingerprint and Face Recognition
  • Watermarking and Steganography
  • Image Pattern Recognition
  • Microarray Image Data Analysis
  • 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 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) Original image (b) Enhanced image

(c) Binarization image (d) Smoothed image

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.
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)




Tree leaves

Other quantitative measurements

2. Decision rules: Classifiers

3. Performance Evaluation

4. Classification

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.
difference between watermarking and steganography

Insert a logo, pattern, a

message, and etc. into

an image, audio, video

to claim the ownership.


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 analysis1
Microarray Image Data Analysis

Each gene expression

is a feature which is

measured as average

spot brightness

Top: Tumor Tissues

Bottom: Normal Tissues

fort san domingo
Fort San Domingo (淡水紅毛城)

Entrance Gate

Dutch Clogs


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thank you for your attention
Thank You For Your Attention

Questions and Comments