BD4004 Image Processing and Analysis Syllabus:
BD4004 Image Processing and Analysis Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the basics of digital images and noise models
To understand spatial domain filters and frequency domain filters
To understand the image processing techniques
To familiarize the image processing environment and their applications
To appreciate the use of image processing in various applications
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 – multi resolution 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
COURSE OUTCOMES:
CO1: Design and implement algorithms for image processing applications that incorporates different concepts of medical Image Processing
CO2: Explain image modalities, sensing, acquisition, sampling, and quantization, noise models and implement spatial filter operations
CO3: Familiar with the use of MATLAB and its equivalent open source tools
CO4: Critically analyze different approaches to image processing applications
CO5: Explore the possibility of applying Image processing concepts in various applications
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. S. Sridhar, “Digital Image Processing”, 2nd Edition, Oxford University Press, 2016.
3. W. Burger and M. Burge, “Digital Image Processing: An Algorithmic Introduction using Java”, Springer,2nd edition, 2016.
4. John C. Russ, “The Image Processing Handbook”, Sixth Edition, CRC Press, 2011.
5. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Third Edition, Pearson,2008.
6. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for Computer Vision”, Third Edition, Academic Press, 2013.
7. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”, Packt Publishing, 2012.
8. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms for analyzing images”, O’Reilly Media, 2012.