Image Processing Lab detailed syllabus for Cloud Computing & Big Data (CC) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Cloud Computing & Big Data students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Cloud Computing & Big Data 6th Sem scheme and its subjects, do visit Cloud Computing & Big Data (CC) 6th Sem 2021 revision scheme. The detailed syllabus of image processing lab is as follows.
Course Objectives:
- To implement basic and advanced image processing algorithms
- Able to implement various image enhancement methods
- Able to implement different image augmentation techniques
- An exposure of Convolutional neural network based feature extraction for classification problems
Course Outcomes:
On completion of the course student will be able to:
- Practice various image processing libraries and demonstrate basic operations on image
- Use different data augmentations and denoising techniques for improve the image dataset
- Implement image enhancement methods and image gradients 1
- Apply Convolutional neural networks for image feature extraction and classification
Module 1:
Illustrations of python programming using Anaconda platform/google colaboratory/jupyter lab/jupyter notebook , Familiarization of OpenCV, Pillow, Matplotlib, and/or PIL libraries. Reading, displaying operations of image, RGB to gray conversion, Applying statistical operations on images.
Module 2:
Implementing image augmentations techniques like blurring, flipping, translations. Resizing on images. Denoising of images
Module 3:
Performing Fourier transform on image. Implementing contrast and brightness enhancement operations to enhance image. Implementing various operators like Sobel, Prewit, canny on images.
Module 4:
Implement convolutional filtering methods. Feature extraction with kernel size 3×3, 5×5. Implementation Image classification algorithm on iris dataset. Handwriting recognition algorithm on MNIST dataset. ** – Sample Open Ended Experiments (Not evaluated for End Semester Examination but to be included in Continuous Internal Evaluation.) Students can-do open-ended real time projects/ experiments as a group of 2-3. There is no duplication in experiments between groups. Consider the following sample scenarios: 1. Fingerprint recognition 2. Face recognition Students should gather the required information, develop algorithms, and implement it in Python languages.
Text Books:
- Rafael C.Gonzalez & Richard E.Woods – Digital Image Processing – Pearson Education- 2/e – 2004.
- Anil.K.Jain – Fundamentals of Digital Image Processing- Pearson Education-2003.
Reference Books:
- William K. Pratt – Digital Image Processing – John Wiley & Sons-2/e, 2004
Online Resources
- https://www.geeksforgeeks.org/image-processing/
- https://www.analyticsvidhya.com/blog/2021/08/image-processing-in-python-the-computer-vision-techniques/
- https://www.datacamp.com/courses/image-processing-in-python
For detailed syllabus of all other subjects of Cloud Computing & Big Data (CC), 2021 revision curriculum do visit Cloud Computing & Big Data 6th Sem subject syllabuses for 2021 revision.
To see the syllabus of all other branches of diploma 2021 revision curriculum do visit SITTTR diploma all branches syllabus..
To see the results of Cloud Computing & Big Data (CC) of diploma 2021 revision curriculum do visit SITTTR diploma Cloud Computing & Big Data (CC) results..
For all Cloud Computing & Big Data academic calendars, visit Cloud Computing & Big Data all semesters academic calendar direct link.