{{{credits}}}
| L | T | P | C |
| 3 | 0 | 0 | 3 |
- To understand the basics of digital images.
- To understand the spatial and frequency domain processing.
- To learn basic image analysis - segmentation and feature detection.
- To understand color image processing and image compression techniques.
- To appreciate the use of image processing in various applications.
{{{unit}}}
| Unit I | Fundamentals of Image Processing | 8 |
Introduction – Elements of visual perception – Steps in Image Processing Systems – Image Acquisition – Sampling and Quantization – Pixel Relationships – Image Modalities – File Formats – Image Operations: Arithmetic; Logical; Statistical and Spatial operations
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| Unit II | Image Enhancement and Restoration | 10 |
Spatial Domain processing: Filtering operations; Histograms; Smoothing filters; Sharpening filters; Fuzzy techniques; Noise models; Filters for noise removal Frequency Domain processing: Fourier Transform – DFT and FFT; Filtering operations; Smoothing and Sharpening – Selective filters; Filters for noise removal; Homomorphic filtering Restoration: Model of Image Degradation/Restoration Process, Noise Models
{{{unit}}}
| Unit III | Image Segmentation and Feature Analysis | 10 |
Thresholding techniques: Region growing; splitting and merging; Adaptive – Otsu method Edge detection: Template matching; Gradient operation; Hysterisis Thresholding – Canny operator – Laplacian operator; Image morphology – Binary and Gray Level morphology operations – erosion; dilation – opening– closing operations – Morphological watersheds; Features – Corner and interest point detection – boundary representation and detections – texture descriptors – regional descriptors and feature selection techniques
{{{unit}}}
| Unit IV | Multi Resolution Analysis, Color Images and Image Compressions | 9 |
Multi Resolution Analysis: Image Pyramids – Multi resolution expansion – Wavelet Transforms; Fast Wavelet transforms; Wavelet Packets Image Compression: Fundamentals – Models – Error Free Compression –Lossy Compression – Compression Standards – Watermarking Color Images: Color Models; Smoothing and Sharpening – Segmentation based on Color – Noise in Color Images
{{{unit}}}
| Unit V | Case Studies in Image Processing | 8 |
Image Recognition : Fingerprint Recognition – Image Classification : Tumor classification from Medical Image – Image Understanding: CBIR – Image Fusion: Statellite image enhancement – Object tracking: Surveillance applications – Image Steganography: Image hiding in Multimedia
\hfill Total: 45
After the completion of this course, students will be able to:
- Design and implement enhancement and segmentation algorithms for image processing application. (K4)
- Perform analysis using various image features. (K3)
- Analyze the multi resolution techniques and methods used for color images. (K3)
- Make a positive professional contribution in the field of Digital Image Processing. (K4)
Course Outcomes (Batch 2021-2023)
- Analyze the enhancement and segmentation algorithms for image processing application. (K4)
- Choose various image features for segmentation . (K3)
- Apply the multi resolution techniques and methods for color images. (K3)
- Examine applications to make a positive professional contribution in the field of Digital Image Processing. (K4)
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | ||
| K3 | K6 | K6 | K6 | K6 | ||||||||
| CO1 | K3 | 3 | 2 | 2 | ||||||||
| CO2 | K3 | 3 | 2 | 2 | ||||||||
| CO3 | K3 | 3 | 2 | 2 | ||||||||
| CO4 | K4 | 3 | 2 | 2 | ||||||||
| Score | 14 | 8 | 6 | 2 | ||||||||
| Course Mapping | 3 | 2 | 2 | 1 |
- Rafael C.Gonzalez, Richard E.Woods, “Digital Image Processing”, Third Edition, Pearson Education, 2008. (Units I, II, III, IV)
- Anil K.Jain, “Fundamentals of Digital Image Processing”, PHI, 2006.
- Rafael C.Gonzalez, Richard E.Woods, Eddins, “Digital Image Processing Using MATLAB”, Second Edition, Tata McGraw-Hill, 2009.
- Davis, E. R. “Machine Vision” Second Edition, 1997.