ANNA UNIVERSITY, CHENNAI
REGULATIONS - 2013
M.E. COMPUTER SCIENCE AND ENGINEERING
CP7004 IMAGE PROCESSING AND ANALYSIS
OBJECTIVES: To understand the basics of digital images
To understand noise models
To understand spatial domain filters
To understand frequency domain filters
To learn basic image analysis --- segmentation, edge detection, and corner detection
To learn morphological operations and texture analysis
To understand processing of color images
To understand image compression techniques
UNIT I SPATIAL DOMAIN PROCESSING
Introduction to image processing – imaging modalities – image file formats – image sensing and acquisition – image sampling and quantization – noise models – spatial filtering operations – histograms – smoothing filters – sharpening filters – fuzzy techniques for spatial filtering – spatial filters for noise removal
UNIT II FREQUENCY DOMAIN PROCESSING
Frequency domain – Review of Fourier Transform (FT), Discrete Fourier Transform (DFT), and
Fast Fourier Transform (FFT) – filtering in frequency domain – image smoothing – image
sharpening – selective filtering – frequency domain noise filters – wavelets – Haar Transform – multiresolution expansions – wavelet transforms – wavelets based image processing
UNIT III SEGMENTATION AND EDGE DETECTION
Thresholding techniques – region growing methods – region splitting and merging – adaptive
thresholding – threshold selection – global valley – histogram concavity – edge detection –
template matching – gradient operators – circular operators – differential edge operators –
hysteresis thresholding – Canny operator – Laplacian operator – active contours – object
segmentation
UNIT IV INTEREST POINTS, MORPHOLOGY, AND TEXTURE
Corner and interest point detection – template matching – second order derivatives – median filter based detection – Harris interest point operator – corner orientation – local invariant feature detectors and descriptors – morphology – dilation and erosion – morphological operators – grayscale morphology – noise and morphology – texture – texture analysis – co-occurrence matrices – Laws' texture energy approach – Ade's eigen filter approach.
UNIT V COLOR IMAGES AND IMAGE COMPRESSION
Color models – pseudo colors – full-color image processing – color transformations – smoothing
and sharpening of color images – image segmentation based on color – noise in color images.
Image Compression – redundancy in images – coding redundancy – irrelevant information in
images – image compression models – basic compression methods – digital image watermarking.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain image modalities, sensing, acquisition, sampling, and quantization
Explain image noise models
Implement spatial filter operations
Explain frequency domain transformations
Implement frequency domain filters
Apply segmentation algorithms
Apply edge detection techniques
Apply corner and interest point detection algorithms
Apply morphological operations
Perform texture analysis
Analyze color images
Implement image compression algorithms
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. W. Burger and M. Burge, “Digital Image Processing: An Algorithmic Introduction using
Java”, Springer, 2008.
3. John C. Russ, “The Image Processing Handbook”, Sixth Edition, CRC Press, 2011.
4. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Third Edition, Pearson,
2008.
5. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for Computer
Vision”, Third Edition, Academic Press, 2012.
6. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”, Packt
Publishing, 2012.
7. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms for
analyzing images”, O'Reilly Media, 2012.
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