COMPUTER VISION & IOT

Paper Code: 
DBDA 511A
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 

This course will enable students to 

  1. Explore the concepts of computer vision and image processing
  2. Understand about the sensors and IOT based systems.

Course Outcomes (COs). 

Course outcome (at course level)

Learning and teaching strategies

Assessment Strategies 

On completion of this course, the students will:

CO246. Apply concepts of computer vision and image processing.

CO247. Apply basic operations on images.

CO248. Able to perform image recognition and object detection.

CO249. Describe the concepts of IOT and its applications.

CO250. Identify different kinds of sensors and their characteristics with respect to real life problems.

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: 

Getting started with OPENCV,Introduction to computer vision .Image Processing VS Computer Vision , Problems in Computer Vision Introduction to images .How images are formed,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 . Datatype Conversion ,Contrast Enhancement ,Brightness Enhancement

Image Annotation. Draw a line over an image, Draw a Circle over an image , Draw a Rectangle over an image ,Draw an Ellipse over an image ,Draw text over an image

 

9.00
Unit III: 

Image Processing – point operators, linear filtering, neighbourhood 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: 
9

IoT Global LANDSCAPE. What is the Internet of things, IoT global adoption, IoT common Patterns. sensor, data, analytics, IoT challenges. security and scalability, Resources.IoT Application Domains. IoT Solution Anatomy – Device and Networks. IoT Solution Architecture, Physical Layer (Devices, Hardware, Sensors), Communication layer (IoT networks), Resources IoT Solution Anatomy – IoT Data Platform. IoT Platform Layer, Data Analytics and applications Layer, Resources

 

9.00
Unit V: 

Industrial sensors – Description & Characteristics–First Generation – Description & Characteristics– Advanced Generation – Description & Characteristics–Integrated IoTSensors – Description & Characteristics–Polytronics Systems – Description & Characteristics–Sensors' Swarm – Description & Characteristics.

 

ESSENTIAL READINGS: 
  • Computer Vision. Algorithms and Applications, Richard Szeliski http.//szeliski.org/Book/
  • Research Methods in Human-Computer Interaction, Jonathan Lazar, Jinjuan Heidi Feng and Harry Hochheiser

 

REFERENCES: 

SUGGESTED READINGS.

  • Learning OpenCV. Computer Vision in C++ with the OpenCV Library (2nd edition) by Gary Bradski, Adrian Kaehler

 

E RESOURCES.

JOURNALS.

 

Academic Year: 
2022-23
  • Printer-friendly version