Department Events
Computer Vision and IoT
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 | Learning outcome (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Title | CO295. Analyse image formation and manipulation techniques in computer vision. CO296. Implement basic image processing operations. CO297. Apply advanced techniques to address image processing and recognition problems. CO298. Analyse IoT adoption, patterns, challenges, and solution anatomy. CO299. Identify different kinds of sensors and their characteristics with respect to real life problems. CO300.Contribute effectively in course- specific interaction | Approach in teaching:Interactive Lectures, Discussion, Reading assignments,Demonstration.
Learning activities for the students: Self learning assignments, Effective questions, Seminar presentation. | Class test, Semester end examinations, Quiz, Practical Assignments, Presentation. |
25DBDA 511A |
Computer Vision and IoT (Theory) | |||
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.
Basic image operations and annotation: 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.
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.
Introduction to 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.
Industrial sensors: Description & Characteristics–First Generation – Description & Characteristics– Advanced Generation – Description & Characteristics–Integrated IoT Sensors – Description & Characteristics–Polytronics Systems – Description & Characteristics–Sensors' Swarm – Description & Characteristics.
1. Lakhwani, Kamlesh, Hemant Kumar Gianey, Joseph Kofi Wireko, and Kamal Kant Hiran.Internet of Things (IoT): Principles, paradigms and applications of IoT. Bpb Publications, 2020.
2. Szeliski, Richard. Computer vision: algorithms and applications. Springer Nature, 2022.
SUGGESTED READINGS:
1. Kaehler, Adrian, and Gary Bradski. Learning OpenCV 3: computer vision in C++ with the OpenCV library. " O'Reilly Media, Inc.", 2016.
2. Misra, Sudip, Chandana Roy, and Anandarup Mukherjee. Introduction to industrial internet of things and industry 4.0. CRC Press, 2021.
e RESOURCES
1. NPTEL: Deep Learning for Computer Vision, IIT Hyderabad:
https://nptel.ac.in/courses/106106224
2. Image processing by Saikiran Panjala, Slideshare:
https://www.slideshare.net/MadhushreeGhosh3/image-processing-76619758
JOURNALS
1. Journal of Real-Time Image Processing (JRTIP) springer:
https://www.springer.com/journal/11554
2. IEEE Transactions on Image Processing:
https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83
