Digital Image Processing detail syllabus for Information Technology, effective from 2019-2020, is collected from BTEUP 2017 Syllabus official website and presented for diploma students. PDF download is possible from official site but you can download the istudy mobile app for syllabus on mobile. The course details such as exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.
For all other bteup syllabus 6th sem information tech 2019-2020 you can visit BTEUP Syllabus 6th Sem Information Tech 2019-2020 Subjects. For all other elective subjects do refer to Electives. The detail syllabus for digital image processing is as follows.
Rationale:
Digital Image processing becomes an emerging and growing field in various technological areas and currently used in various Industries such process industry, medical industry etc. This course will help in understanding the fundamentals of image processing. Student will also understand how to apply various algorithms on images for better image understanding. The course also covers the design aspects and realization of image processing. It largely covers the theory and algorithms related to image acquisition and formation, enhancement, segmentation and representation.
Learning Outcomes:
By the end of this course, students will be able to:
- Explain how digital images are represented and manipulated in a computer, including reading and writing from storage, and displaying.
- Write a program which implements fundamental image processing algorithms.
- Be conversant with the mathematical description of image processing techniques and know how to go from the equations to code.
- An ability to apply knowledge of computing and mathematics appropriate to the discipline.
- An ability to analyze a problem and identify the computing requirements appropriate for its solution; an ability to design, implement and evaluate a computer-based system, process, component or program to meet desired needs.
- An ability to apply mathematical foundations, algorithmic principles and computer science theory to the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices.
1. Introduction and Digital Image Fundamentals : (12 Lectures)
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2. Image enhancement in Spatial domain: (12 Lectures)
Basic gray level Transformations Histogram Processing Techniques Basics of Spatial Filtering, Smoothing (Low Pass) Spatial Filters Sharpening(High Pass) Spatial Filters.
3. Image enhancement in Spatial domain: (12 Lectures)
Basic gray level Transformations Histogram Processing Techniques Basics of Spatial Filtering, Smoothing (Low Pass) Spatial Filters Sharpening(High Pass) Spatial Filters.
4. Filtering in the Frequency Domain: (12 Lectures)
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5. Image Restoration and Reconstruction: (08 Lectures)
- Noise Models
- Noise Reduction
- Inverse Filtering
- MMSE (Wiener) Filtering
6. Color Image Processing: (10 Lectures)
- Color Fundamentals
- Color Models
- Pseudo color image processing
- Basics of Full-Color Image Processing.
- Color Transformations.
- Smoothing and Sharpening.
- Color Segmentation.
7. Image Segmentation: (08 Lectures)
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8. Morphological Image Processing: (10 Lectures)
- Erosion and Dilation
- Opening and Closing
- Basic Morphological Algorithms:
- Hole filling
- Connected components
- Thinning
- Skeletons
List of Experiments:
- Implement the Mirroring (Inversion) spatial image enhancement functions on a bitmap image.
- Implement the Rotation (Clockwise) spatial image enhancement functions on a bitmap image.
- Implement the Enlargement (Double Size)spatial image enhancement functions on a bitmap image.
- Implement
- Low Pass Filter
- High Pass Filter for image enhancement on an image.
- Implement
- Arithmetic Mean Filter
- Geometric Mean Filter for image enhancement on an image.
- Implement Smoothing and Sharpening function on an eight bit color image.
- Implement
- Boundary Extraction Algorithm
- Graham’s Scan Algorithm on an image.
- Implement
- Edge Detection
- Line Detection on an image.
- Display an image and its corresponding histogram
- Perform shrinking, zooming and cropping functions on an image
- Perform the experiment for histogram equalization.
- Perform blurring and de-blurring functions on an image.
- Removal of salt and pepper noise from a distorted image.
- Implement a function in MATLAB for image morphology that analyze the form and shape detail of image structures. Implement a function in MATLAB for Image Restoration.
Reference Books:
- Gonzalez & Woods, —Digital Image Processing^, 3rd ed., Pearson education, 2008
- Jain Anil K., —Fundamentals Digital Image Processing^, Prentice Hall India, 2010
- Pratt W.K, —Digital Image Processing^, 3rd ed., John Wiley & Sons, 2007 e-books/e-tools/relevant software to be used as recommended by AICTE/UPBTE/NITTTR, Chandigarh.
Instructional Strategy:
Since this subject is practical oriented, the teacher should demonstrate the capabilities of MATLAB programming to students while doing practical exercises.
Means of Assessment:
- Assignments and quiz/class tests, mid-term and end-term written tests
- Actual laboratory and practical work, exercises and viva-voce
- Software installationand viva-voce
Reference Books:
- http://swayam.gov.in
- http://spoken-tutorial.org
For detail syllabus of all other subjects of BE Information Tech, 2019-2020 scheme do visit Information Tech 6 syllabus for 2019-2020 Scheme.
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