Image Processing Computer Vision syllabus
PEC-321D-IT · Third Year Information Technology, SPPU 2024 pattern. Every unit, the marks scheme, course outcomes and books, copied from the official syllabus PDF.
Unit-wise syllabus
Introduction to Image Processing and Computer Vision
9 hoursDerived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.
- Digital Image fundamentals, Image Sensing and acquisition, Sampling and Quantization, Image formation models, Overview of Computer Vision, Applications of Image processing and Computer Vision.
- Fundamental concepts of digital image processing, sampling, and quantization, and identify relevant applications of computer vision.
- Case Study : Smart Toll Plaza Automated Number Plate Recognition (ANPR) System
Preserved official unit paragraph
Digital Image fundamentals, Image Sensing and acquisition, Sampling and Quantization, Image formation models, Overview of Computer Vision, Applications of Image processing and Computer Vision. Fundamental concepts of digital image processing, sampling, and quantization, and identify relevant applications of computer vision. Case Study : Smart Toll Plaza Automated Number Plate Recognition (ANPR) System
Image Enhancement
9 hoursDerived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.
- Image enhancement in spatial domain, Basic grey level Transformations, Histogram Processing Techniques, Spatial Filtering, Image smoothing and Image Sharpening, Image enhancement process in frequency domain, Low pass filtering, High pass filtering.
- Case Study : Medical X-Ray and MRI Contrast Optimization
Preserved official unit paragraph
Image enhancement in spatial domain, Basic grey level Transformations, Histogram Processing Techniques, Spatial Filtering, Image smoothing and Image Sharpening, Image enhancement process in frequency domain, Low pass filtering, High pass filtering. Case Study : Medical X-Ray and MRI Contrast Optimization
Image Segmentation and Compression
9 hoursDerived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.
- Point, line and edge detection, Thresholding, Regions Based segmentation, Edge linking and boundary detection.
- Fundamental of redundancies, Basic Compression Methods: Huffman Coding, Concept of Discrete cosine transform, JPEG,MPEG Compression Standard Case Study : Traffic Monitoring and Automated Enforcement System (Segmentation Domain)
Preserved official unit paragraph
Point, line and edge detection, Thresholding, Regions Based segmentation, Edge linking and boundary detection. Fundamental of redundancies, Basic Compression Methods: Huffman Coding, Concept of Discrete cosine transform, JPEG,MPEG Compression Standard Case Study : Traffic Monitoring and Automated Enforcement System (Segmentation Domain)
Object Recognition and Motion Estimation
9 hoursDerived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.
- Object Recognition techniques: Viola-Jones, Yolo, Deep learning algorithms for Object Recognition.
- Optical Flow, Gaussian Mixture Model (GMM), Structure of Motion, Motion Estimation.
- Case Study : Smart Traffic Intersection Monitoring and Speed Enforcement
Preserved official unit paragraph
Object Recognition techniques: Viola-Jones, Yolo, Deep learning algorithms for Object Recognition. Optical Flow, Gaussian Mixture Model (GMM), Structure of Motion, Motion Estimation. Case Study : Smart Traffic Intersection Monitoring and Speed Enforcement
Applications of Image Processing and Computer vision
9 hoursDerived reading outline. Source text split at semicolons, line breaks and sentence boundaries, not an official topic hierarchy.
- Review of Computer Vision applications
- Fuzzy-Neural algorithms for computer vision applications Face Recognition, Facial Expression Recognition, Optical Character Recognition, Automated Video Surveillance Case Study : Smart Airport Contactless Immigration Clearing Gate
Preserved official unit paragraph
Review of Computer Vision applications; Fuzzy-Neural algorithms for computer vision applications Face Recognition, Facial Expression Recognition, Optical Character Recognition, Automated Video Surveillance Case Study : Smart Airport Contactless Immigration Clearing Gate
Marks and credits
| Head | Marks | Credit |
|---|---|---|
| CCE (continuous comprehensive evaluation) | 30 | 3 |
| End-semester exam | 70 |
Course outcomes
- CO1Explain the fundamental concepts of digital image processing, sampling, and quantization, and identify relevant applications of computer vision.
- CO2Apply spatial and frequency domain filtering techniques to enhance image quality, remove noise, and sharpen structural details.
- CO3Apply segmentation algorithms to extract object boundaries and utilize lossy/lossless compression techniques to reduce image and video data storage.
- CO4Implement deep learning object detection models like YOLO and apply motion tracking algorithms to extract temporal information from video sequences.
- CO5Analyze and design intelligent vision systems for real-world applications including biometric recognition, OCR, and automated surveillance networks.
Books
Text books
- Gonzalez & Woods, “Digital Image Processing”, Pearson Education, 3 rd Edition, 2008
- S Sridhar, “Digital Image Processing”, Oxford University Press, 2nd Edition.
Reference books
- Jain Anil K., “Fundamentals Digital Image Processing”, Prentice Hall India, 4 th Edition.
- Milan Sonka, Vaclav Hlavav, Roger Boyle, “Image Processing, Analysis and Machine Vision”, Thomson Learning, 2 nd Edition., 2001.
- Pratt W.K, “Digital Image Processing”, John Wiley & Sons, 3 rd Edition, 2007.
- Jayaraman. S, Veerakumar. T, “Digital Image Processing”, McGraw Hill Education, 2nd Edition.
FAQ
How many units are in Image Processing Computer Vision?
Image Processing Computer Vision (PEC-321D-IT) has 5 units and 45 hours of theory: Unit I Introduction to Image Processing and Computer Vision (9 h); Unit II Image Enhancement (9 h); Unit III Image Segmentation and Compression (9 h); Unit IV Object Recognition and Motion Estimation (9 h); Unit V Applications of Image Processing and Computer vision (9 h).
What is the marks scheme for Image Processing Computer Vision?
The official Information Technology 2024 pattern syllabus lists continuous comprehensive evaluation (CCE) for 30 marks and the end-semester exam for 70 marks, for 3 credits.