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Syllabus | B. Tech. Electronics & Communication Engineering | DIGITAL IMAGE PROCESSING

13040610 DIGITAL IMAGE PROCESSING L T P C
Version1.1 Date of Approval: Jun 06, 2013 3 1 0 3
Pre-requisites//Exposure                 Signal Processing
co-requisites  

Course Objectives

  1. To impart the basic concepts of image segmentation and shaping
  2. To apply different types signal processing techniques in image processing 

Course Outcomes

On completion of this course, the students will be able to

  1. Know Basics of Image formation and transformation using sampling and quantization
  2. Define different types of signal processing techniques used for image sharpening and smoothing
  3. Perform and demonstrate the compression and coding techniques used for image data

Catalog Description 

Digital image processing is a fascinating subject in several aspects. Human beings perceive most of the information about their environment through their visual sense. While for a long time images could only be captured by photography, we are now at the edge of another technological revolution which allows image data to be captured, manipulated, and evaluated electronically with computers. With breathtaking pace, computers are becoming more powerful and at the same time less expensive, so that widespread applications for digital image processing emerge. In this way, image processing is becoming a tremendous tool for analyzing image data in all areas of natural science. For more and more scientists digital image processing will be the key to study complex scientific problems they could not have dreamed of tackling only a few years ago. A door is opening for new interdisciplinary cooperation merging computer science with the corresponding research areas.

Text Books

  1. Ganzalez and Wood, “Digital Image Processing”, Addison Wesley, 1993
  2. Anil K.Jain, “Fundamental of Image Processing”, Prentice Hall of India

Reference Books

  1. Rosenfeld and Kak, “Digital Picture Processing” vol.I & vol.II, Academic,1982
  2. Ballard and Brown, “Computer Vision”, Prentice Hall, 1982.
  3. Wayne Niblack, “An Introduction to Digital Image Processing”, Prentice Hall, 1986
  4. Milan Sonka, Vaclav Hlavac, Roger Boyle, “Image Processing, Analysis and Machine Vision”, Vikas Publications 

Course Content

Unit I:Introduction to Image Processing                                       

6 lecture hours

Image formation, image geometry perspective and other transformation, sterio imaging elements of visual perception. Digital Image-sampling and quantization serial & parallel Image processing.

Unit II: Signal Processing                                                               

7 lecture hours

Signal Processing – Fourier, Walsh-Hadmard discrete cosine and Hotelling transforms and their properties, filters, correlators and convolvers. Image enhancement-Contrast modification. Histogram specification, smoothing, sharpening, frequency domain enhancement, pseudo-colour Enhancement. 

Unit III: Image Restoration                                             

9 lecture hours

Image Restoration-Constrained and unconstrained restoration Wiener filter , motion blur remover, geometric and radiometric correction Image data compression-Huffman and other codes transform compression, predictive compression two tone Image compression, block coding, run length coding, and contour coding.

Unit IV :  Segmentation Techniques                                              

8 lecture hours

Segmentation Techniques-thresholding approaches, region growing, relaxation, line and edge detection approaches, edge linking, supervised and unsupervised classification techniques, remotely sensed image analysis and applications.

Unit V: Shape Analysis                                                                   

9 lecture hours

Shape Analysis – Gestalt principles, shape number, moment Fourier and other shape descriptors, skelton detection, Hough transform, topological and texture analysis, shape matching. Practical Applications – Finger print classification, signature verification, text recognition, map understanding, bio-logical cell classification.

 Mode of Evaluation: The theory and lab performance of students are evaluated separately. 

  Theory
Components Internal SEE
Marks 50 50
Total Marks 100
Scaled Marks 75

Relationship between the Course Outcomes (COs) and Program Outcomes (POs)

Mapping between Cos and POs
Sl. No. Course Outcomes (COs) Mapped Programme Outcomes
1 Know Basics of Image formation and transformation using sampling and quantization

 

1
2 Define different types of signal processing techniques used for image sharpening and smoothing

 

4
3 Perform  and demonstrate the compression and coding techniques used for image data 7,10

  

    Engineering Knowledge Problem analysis Design/development of solutions Conduct investigations of complex problems Modern tool usage The engineer and society Environment and sustainability Ethics Individual or team work Communication Project management and finance Life-long Learning
    1 2 3 4 5 6 7 8 9 10 11 12
TEC361 Digital Image Processing 1     2     3     3    

1=addressed to small extent

2= addressed significantly

3=major part of course

 

 

Theory  The theory of this course is used to evaluate the program outcome PO(10)
ADMISSIONS 2021