EC8093-DIGITAL IMAGE PROCESSING Syllabus 2017 Regulation
DIGITAL IMAGE PROCESSING Syllabus 2017 Regulation,EC8093-DIGITAL IMAGE PROCESSING Syllabus 2017 Regulation
EC8093Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â DIGITAL IMAGE PROCESSINGÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â L P T CÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 3 0 0 3
OBJECTIVES:
- To become familiar with digital image fundamentals
- To get exposed to simple image enhancement techniques in Spatial and Frequency domain.
- To learn concepts of degradation function and restoration techniques.
- To study the image segmentation and representation techniques.
- To become familiar with image compression and recognition methods
UNIT I DIGITAL IMAGE FUNDAMENTALSÂ Â Â Â Â Â Â Â Â Â Â 9
Steps in Digital Image Processing – Components – Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels – Color image fundamentals – RGB, HSI models, Two-dimensional mathematical preliminaries, 2D transforms – DFT, DCT.
UNIT II IMAGE ENHANCEMENTÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 9
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, Homomorphic filtering, Color image enhancement.
UNIT III IMAGE RESTORATIONÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 9
Image Restoration – degradation model, Properties, Noise models – Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering
UNIT IV IMAGE SEGMENTATIONÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 9
Edge detection, Edge linking via Hough transform – Thresholding – Region based segmentation – Region growing – Region splitting and merging – Morphological processing- erosion and dilation, Segmentation by morphological watersheds – basic concepts – Dam construction – Watershed segmentation algorithm.
UNIT V IMAGE COMPRESSION AND RECOGNITIONÂ 9
Need for data compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding, JPEG standard, MPEG. Boundary representation, Boundary description, Fourier Descriptor, Regional Descriptors – Topological feature, Texture – Patterns and Pattern classes – Recognition based on matching.
                                                    TOTAL 45 PERIODS
OUTCOMES:
At the end of the course, the students should be able to:
- Know and understand the basics and fundamentals of digital image processing, such as digitization, sampling, quantization, and 2D-transforms.
- Operate on images using the techniques of smoothing, sharpening and enhancement.
- Understand the restoration concepts and filtering techniques.
- Learn the basics of segmentation, features extraction, compression and recognition methods for color models.
TEXT BOOKS:
1.
Rafael C. Gonzalez, Richard E. Woods,Digital Image Processing Pearson, Third Edition, 2010.
2.
Anil K. Jain,Fundamentals of Digital Image Processing Pearson, 2002.
REFERENCES:
- Kenneth R. Castleman,Digital Image Processing Pearson, 2006.
- Rafael C. Gonzalez, Richard E. Woods, Steven Eddins,Digital Image Processing using MATLAB Pearson Education, Inc., 2011.
- D,E. Dudgeon and RM. Mersereau,Multidimensional Digital Signal Processing Prentice Hall Professional Technical Reference, 1990.
- William K. Pratt,Digital Image Processing John Wiley, New York, 2002
- Milan Sonka et al Image processing, analysis and machine vision Brookes/Cole, Vikas Publishing House, 2nd edition, 1999
