IT6005 DIGITAL IMAGE PROCESSING L T P C
3 0 0 3
OBJECTIVES:
The student should be made to:
- Learn digital image fundamentals
- Be exposed to simple image processing techniques
- Be familiar with image compression and segmentation techniques
- Learn to represent image in form of features
UNIT I DIGITAL IMAGE FUNDAMENTALS 8
Introduction – Origin – Steps in Digital Image Processing – Components – Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels - color models
UNIT II IMAGE ENHANCEMENT 10
Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–
Smoothing and Sharpening Spatial Filtering – Frequency Domain: Introduction to Fourier Transform
– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters
UNIT III IMAGE RESTORATION AND SEGMENTATION 9
Noise models – Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering Segmentation: Detection of Discontinuities–Edge Linking and Boundary detection – Region based segmentation- Morphological processing- erosion and dilation
UNIT IV WAVELETS AND IMAGE COMPRESSION 9
Wavelets – Subband coding - Multiresolution expansions - Compression: Fundamentals – Image
Compression models – Error Free Compression – Variable Length Coding – Bit-Plane Coding – Lossless Predictive Coding – Lossy Compression – Lossy Predictive Coding – Compression Standards
UNIT V IMAGE REPRESENTATION AND RECOGNITION 9
Boundary representation – Chain Code – Polygonal approximation, signature, boundary segments – Boundary description – Shape number – Fourier Descriptor, moments- Regional Descriptors – Topological feature, Texture - Patterns and Pattern classes - Recognition based on matching.
TOTAL: 45 PERIODS
OUTCOMES:
Upon successful completion of this course, students will be able to:
- Discuss digital image fundamentals
- Apply image enhancement and restoration techniques
- Use image compression and segmentation Techniques
- Represent features of images
TEXT BOOK:
- Rafael C. Gonzales, Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson
REFERENCES:
- Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using
- Anil Jain K. “Fundamentals of Digital Image Processing”, PHI Learning Pvt. Ltd., 2011.
- Willliam K Pratt, “Digital Image Processing”, John Willey, 2002.
- Malay K. Pakhira, “Digital Image Processing and Pattern Recognition”, First Edition, PHI
- http://eeweb.poly.edu/~onur/lectures/lectures.html
- http://www.caen.uiowa.edu/~dip/LECTURE/lecture.html
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