AP4011 Advanced Digital Image Processing Syllabus:
AP4011 Advanced Digital Image Processing Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the image fundamentals and mathematical transforms necessary for image processing and to study the image enhancement techniques.
To understand the image segmentation and representation techniques.
To understand how image are analyzed to extract features of interest.
To introduce the concepts of image registration and image fusion.
To analyze the constraints in image processing when dealing with 3D data sets.
UNIT I FUNDAMENTALS OF DIGITAL IMAGE PROCESSING
Elements of visual perception, brightness, contrast, hue, saturation, mach band effect, 2D image transforms-DFT, DCT, KLT, and SVD. Image enhancement in spatial and frequency domain, Morphological image processing.
UNIT II SEGMENTATION
Edge detection, Thresholding, Region growing, Fuzzy clustering, Watershed algorithm, Active contour methods, Texture feature-based segmentation, Model based segmentation, Atlas based segmentation, Wavelet based Segmentation methods.
UNIT III FEATURE EXTRACTION
First and second order edge detection operators, Phase congruency, Localized feature extraction detecting image curvature, shape features Hough transform, shape skeletonization, Boundary descriptors, Moments, Texture descriptors- Autocorrelation, Co-occurrence features, Run length features, Fractal model-based features, Gabor filter, wavelet features.
UNIT IV REGISTRATION AND IMAGE FUSION
Registration- Pre-processing, Feature selection-points, lines, regions and templates Feature Correspondence-Point pattern matching, Line matching, region matching Template matching. Transformation functions-Similarity transformation and Affine Transformation. Resampling- Nearest Neighbour and Cubic Splines Image Fusion-Overview of image fusion, pixel fusion, Multiresolution based fusion discrete wavelet transforms, Curvelet transform. Region based fusion.
UNIT V 3D IMAGE VISUALIZATION
Sources of 3D Data sets, Slicing the Data set, Arbitrary section planes, The use of color, Volumetric display, Stereo Viewing, Ray tracing, Reflection, Surfaces, multiply connected surfaces, Image processing in 3D, Measurements on 3D images.
PRACTICALS:
1. Wavelet and DCT based Image Compression
2. Geometrical transformations and Interpolation of Images
3. Edge Detection using Canny edge detector
4. Region based , threshold based and Watershed Segmentation
5. Image filtering using DFT
6. Texture, Gabor and Wavelet Feature Extraction
7. Image fusion using Wavelets
8. Segmenting 3D Image volume using K-means clustering.
9. Segmentation of Lungs from 3D- Chest Scan.
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
CO1:To understand image formation and the role of human visual system plays in perception of gray and color image data.
CO2:To apply image processing techniques in both the spatial and frequency (Fourier) domains.
CO3:To design image analysis techniques in the form of image segmentation and to evaluate the methodologies for segmentation.
CO4:To conduct independent study and analysis of feature extraction techniques.
CO5:To understand the concepts of image registration and image fusion.
CO6:To analyze the constraints in image processing when dealing with 3D data sets and to apply image processing algorithms in practical applications.
TOTAL: 45+30=75 PERIODS
REFERENCES
1. John C.Russ, “The Image Processing Handbook”, CRC Press, 2007.
2. Mark Nixon, Alberto Aguado, “Feature Extraction and Image Processing”, Academic Press, 2008.
3. Ardeshir Goshtasby, “2D and 3D Image registration for Medical, Remote Sensing and Industrial Applications”, John Wiley and Sons, 2005.
4. Rafael C. Gonzalez, Richard E. Woods, , Digital Image Processing’, Pearson, Education, Inc., Second Edition, 2004.
5. Anil K. Jain, , Fundamentals of Digital Image Processing’, Pearson Education, Inc., 2002.
6. Rick S.Blum, Zheng Liu,“ Multisensor image fusion and its Applications“, Taylor & Francis, 2006.