Outline For Image Processing A Digital Image Processing

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Outline For Image Processing • • A Digital Image Processing System Image Representation and

Outline For 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, (JP 2) Image Transform and Filtering Histogram, Enhancement and Restoration Segmentation, Edge Detection, Thinning Image Data Compression Image Pattern Analysis (Recognition and Interpretation) [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+

Examples of Digital Images

Examples of Digital Images

Image Processing System

Image Processing System

Digital Image Analysis System • A 2 D image is nothing but a mapping

Digital Image Analysis System • A 2 D 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 (120 GB), CD (700 MB), DVD (4. 7 GB), Flash memory (512 MB~4 GB), 3. 5” floppy diskettes, i-pod, … 3. Processing Unit – PC, Workstation, PC-cluster 4. Communication – telephone lines, cable, wireless, … 5. Display – LCD monitor, laser printer, laser-jet printer

Gray Level and Color Images

Gray Level and Color Images

Pixels in a Gray Level Image

Pixels in a Gray Level Image

A Gray Level Image is a Matrix f(0, 0) f(0, 1) f(0, 2) ….

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]

Gray and Color Image Data • 0, 64, 144, 196, 225, 169, 100, 36

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) Magenta – (255, 0, 255) Yellow – (255, 0) Gray – (128, 128)

Image Representation (Gray/Color) • A gray level image is usually represented by an M

Image Representation (Gray/Color) • A gray level image is usually represented by an M by 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

Red, Green, Blue, Color Images

Red, Green, Blue, Color Images

Sensing, Sampling, Quantization • A 2 D digital image is formed by a sensor

Sensing, Sampling, Quantization • A 2 D 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

Gray Level and Color Images

Gray Level and Color Images

Image File Formats (1/2) The American National Standards Institute (ANSI) sets standards for voluntary

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 Compu. Serve 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

Some Image File Formats (2/2) • Raw – Raw image format uses a 8

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 8 RC 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 Post. Script language format from Adulus Systems uses Metafile of 1~24 -bit colors with compression • JPEG 2000

Image Transforms and Filtering • Feature Extraction – find all ellipses in an image

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 Partition an image into nonoverlapping 8 by 8 blocks, and apply

Discrete Cosine Transform Partition an image into nonoverlapping 8 by 8 blocks, and apply a 2 d 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) X: a block of 8 x 8 pixels A=Q 8:

Discrete Cosine Transform (DCT) X: a block of 8 x 8 pixels A=Q 8: 8 x 8 DCT matrix as shown above Y=AXAt

DCT on a 8 x 8 Block

DCT on a 8 x 8 Block

Quantized DCT Coefficients

Quantized DCT Coefficients

Wavelet Transform • Haar, Daubechie’s Four, 9/7, 5/3 transforms • 9/7, 5/3 transforms was

Wavelet Transform • Haar, Daubechie’s Four, 9/7, 5/3 transforms • 9/7, 5/3 transforms was selected as the lossy and lossless coding standards for JPEG 2000 • A Comparison of JPEG and JPEG 2000 shows that the latter is slightly better than the former, however, to replace the current image. jpg by image. jp 2 needs

Daubechies’ 4 Wavelet Transform • X: an image • W: Haar transform shown above

Daubechies’ 4 Wavelet Transform • X: an image • W: Haar transform shown above with ci = 1/√ 2 • Y=P*W*(X*Wt*Q), where • P and Q are permutation matrices

A Block and Its Daub 4 Transform

A Block and Its Daub 4 Transform

Mean and Median Filtering • X 1 X 2 X 3 • X 4

Mean and Median Filtering • X 1 X 2 X 3 • X 4 X 0 X 5 • X 6 X 7 X 8 Replace the X 0 by the mean of X 0~X 8 is called “mean filtering” Replace the X 0 by the median of X 0~X 8 is called “median filtering”

Example of Median Filtering

Example of Median Filtering

Image and Its Histogram

Image and Its Histogram

Enhancement and Restoration • The goal of enhancement is to accentuate certain features for

Enhancement and Restoration • The goal of enhancement is 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

Segmentation and Edge Detection • Segmentation is basically a process of pixel classification: the

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, Histogram, Thresholding

Image, Histogram, Thresholding

Binarization by Thresholding

Binarization by Thresholding

Edge Detection -1 -2 -1 0 0 0 1 2 1 X -1 0

Edge Detection -1 -2 -1 0 0 0 1 2 1 X -1 0 1 -2 0 2 Y -1 0 1 Large (|X|+|Y|) Edge

Thinning and Contour Tracing • Thinning is to find the skeleton of an image

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 • The purpose is to save storage space and to reduce

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

Lenna Image vs. Compressed Lenna

Lenna Image vs. Compressed Lenna