Outline For Image Processing A Digital Image Processing
- Slides: 35
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
Image Processing System
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
Pixels in a Gray Level Image
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 (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 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
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
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 -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 • 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 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: 8 x 8 DCT matrix as shown above Y=AXAt
DCT on a 8 x 8 Block
Quantized DCT Coefficients
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 with ci = 1/√ 2 • Y=P*W*(X*Wt*Q), where • P and Q are permutation matrices
A Block and Its Daub 4 Transform
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
Image and Its Histogram
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 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
Binarization by Thresholding
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 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 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
- Histogram processing in digital image processing
- Neighborhood averaging in image processing
- Neighborhood processing in digital image processing
- پردازش تصویر
- Point processing in digital image processing
- Gonzalez
- Translate
- Optimum notch filter in digital image processing
- Image compression models in digital image processing
- Key stage in digital image processing
- Lossless image compression matlab source code
- Image sharpening in digital image processing
- Image geometry in digital image processing
- False contouring
- Image transforms in digital image processing
- Maketform matlab
- Noise
- Publik sektor
- How to shrink a rubber band
- Representation and description in digital image processing
- Threshold image matlab
- Oerdigital
- Explain basic relationship between pixels
- Intensity transformation functions in image processing
- For coordinates p(2,3)the 4 neighbors of pixel p are
- Imadjust
- Consider an image p, the distance d is also called as
- Coordinate conventions in digital image processing
- Dam construction in image processing
- Digital image processing java
- Thresholding in digital image processing
- Filteration
- In digital image processing
- Representation and description in digital image processing
- Thresholding in digital image processing
- Boundary representation in digital image processing