ME (CSE) -SYLLABUS
CP7004 - IMAGE PROCESSING AND ANALYSIS L T P C
3 0 0 3
UNIT I SPATIAL DOMAIN PROCESSING 9
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 9
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 9
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 9
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 9
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
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.
Download link:
CP7004-IMAGE PROCESSING AND ANALYSIS
No comments :
Post a Comment