Computer Vision Applications
Why colleges run this
Computer vision underpins inspection, retail, healthcare and autonomous systems; a working vision application on real images is a compelling portfolio piece.
Course outcomes
CO1
Apply image-processing operations using OpenCV
K3CO2
Detect features and edges in an image
K3CO3
Run an object detector and interpret its output
K3CO4
Classify images using a convolutional network
K3CO5
Build a computer-vision application on real images
K3Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01Image fundamentals4 h
Theory
Pixels, colour spaces, image operations
Lab
Manipulate images programmatically
Output
Image-operations notebook
02Classical processing4 h
Theory
Filtering, edges, contours with OpenCV
Lab
Detect edges and contours
Output
Processing notebook
03Feature detection4 h
Theory
Keypoints, matching, transformations
Lab
Match features across images
Output
Feature-matching notebook
04Object detection4 h
Theory
Pre-trained detectors, bounding boxes
Lab
Run a detector and read its output
Output
Detection notebook
05Deep vision4 h
Theory
Classifying images with a convolutional network
Lab
Classify images with a CNN
Output
Classification notebook
06Vision capstone4 h
Theory
A working application on real images
Lab
Ship a vision application
Output
Capstone application
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment