IMAGE PROCESSING & DEEP LEARNING

Paper Code: 
DCAI 801A
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

This course will enable students to:

  1. Explore the concepts of image processing and its application
  2. Introduce students to basic applications, concepts, and techniques of deep learning.

Learning Outcome

Learning and Teaching Strategies

Assessment Strategies-

The students will:

CO121. Comprehend the  concepts of computer vision and image processing.

CO122. Apply the basic image processing operations on images.

CO123. Apply image recognition and object detection on images/objects.

CO124. Describe the concepts of deep learning and its applications.

CO125. Develop a deep learning model for a real life problem and evaluate its performance.

Approach in teaching.

Interactive Lectures, Group Discussion, Tutorials, Case Study

 

Learning activities for the students.

Self-learning assignments, Machine Learning exercises, presentations

Class test, Semester end examinations, Quiz, Practical Assignments, Presentation

 

 

9.00
Unit I: 

Introduction to computer vision. Image Processing VS Computer Vision, Problems in Computer Vision, Fundamentals Applications Image processing system components, phases in image processing. Introduction to images, Image formation, Image as a Matrix, Manipulating Pixels, Displaying and Saving an Image, Display Utility Functions, Color Image, Image Channels, Splitting and Merging Channels, Manipulating Color pixels, Images with Alpha Channel

 

9.00
Unit II: 

Basic image operations (creating, cropping, resizing images, creating image masks).

Mathematical operations on images. Data Type Conversion, Contrast Enhancement, Brightness Enhancement

Image Annotation: draw a line ,Circle ,Rectangle Ellipse and Draw text over an image

 

9.00
Unit III: 

Image Processing – point operators, linear filtering, neighborhood operators, fourier transforms, segmentation. Feature Detection and Matching – points and patches, edges, lines, Feature-based Alignment – 2D, 3D feature-based alignment, pose estimation.

Recognition – object detection, face recognition, instance recognition, category recognition

 

9.00
Unit IV: 

Concept of Artificial Neural Networks, Types of neural networks, MLP, KNN, Restricted Boltzmann Machine, topology, training and applications of RBM. Implementation of MLP, KNN and RBM. Metrics, Accuracy Measures: Precision, recall, F-measure, confusion matrix, cross-validation.

 

9.00
Unit V: 

Deep belief networks, deep learning, applying and validating DBN, Autoencoders, denoising and applying autoencoders and assessing performance. Ensemble methods (bagging, stacking, boosting algorithms), random forest. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).

 

ESSENTIAL READINGS: 
  • Elements of Deep Learningg for Computer Vision, Bharat Sikka,bpb,2021.
  • Computer Vision. Algorithms and Applications, Richard Szeliski          http.//szeliski.org/Book/
  • Sebastian Raschka & Vahid Mirjalili,” Python Machine Learning”, Second        Edition,Packt>.
  • McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.

 

REFERENCES: 

SUGGESTED REFERENCE BOOKS:

  • Learning OpenCV. Computer Vision in C++ with the OpenCV Library (2nd edition) by Gary  Bradski, Adrian Kaehler
  • Madhavan, “Mastering Python for Data Science”, Packt, 2015.

E-RESOURCES INCLUDING LINKS:

  • NPTEL: Deep Learning for Computer Vision, IIT Hyderabad : https://nptel.ac.in/courses/106106224
  • Image processing by Saikiran Panjala, Slideshare: https://www.slideshare.net/MadhushreeGhosh3/image-processing-76619758

 

REFERENCE JOURNALS:

  • Journal of Real-Time Image Processing (JRTIP) springer: https://www.springer.com/journal/11554
  • IEEE Transactions on Image Processing: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83

 

 

Academic Year: 
2023-24
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