CP7004 IMAGE PROCESSING AND ANALYSIS - ANNA UNIV 1ST SEM REG 2013 ME CSE SYLLABUS - Anna University Internal marks 2018

CP7004 IMAGE PROCESSING AND ANALYSIS - ANNA UNIV 1ST SEM REG 2013 ME CSE SYLLABUS

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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