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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