← Back to the catalogueComputer Vision
Intermediate to Advanced
Level
Classroom . Online . Hybrid
Mode
Course overview
An applied computer vision course from pixel operations to deployed detection systems. The course begins with classical image processing in OpenCV, then moves to convolutional models for classification, detection, tracking and segmentation, with optical character recognition and face recognition treated as complete build exercises.
Projects are deployment-shaped: number plate recognition, vehicle counting and an attendance system.
Who it is for
- AI and ML practitioners
- Embedded and robotics engineers
- Security and surveillance product teams
- Engineering students in final year
Prerequisites
Python and basic machine learning. Deep learning exposure is helpful.
How it runs
Learn → Practise → Build → Experience → Demonstrate, ending in a capstone. Delivered by practitioners from the engineering bench, in Madurai, Coimbatore and online.
Final project
End-To-End Vision System
Programme sheet
The printed sheet carries the full module breakdown, labs, project work and certification path. Fees, dates and formats for the next intake are confirmed by the education team on enquiry.
Learning outcomes
01
Process and transform images with OpenCV.
02
Train image classifiers with transfer learning.
03
Run and tune object detection with YOLO.
04
Track objects across video frames.
05
Perform semantic and instance segmentation.
06
Build face detection and recognition pipelines.
07
Extract text from documents and plates with OCR.
Module structure
8 modulesModule 01
Image Fundamentals
Digital images . Histograms . Filtering . Edge detection . Morphological operations
Tools — OpenCV, NumPy
Module 02
Feature Extraction
SIFT and ORB . Template matching . Image stitching
Module 03
Classification with CNNs
Datasets and augmentation . Fine-tuning . Transfer learning . Evaluation
Lab
Product Image Classifier
Module 04
Object Detection
Detection metrics . YOLO family . Annotation workflow . Anchor boxes . Training on custom data
SORT and DeepSORT . Multi-camera basics . Kalman filter
Lab
Vehicle Counting . Counting and Zones
Semantic segmentation . U-Net . Post-processing masks . Instance segmentation . Mask R-CNN
Face detection . Embeddings and recognition . Liveness basics . Tesseract OCR . Document parsing . Number plate pipelines
Video pipelines . Edge devices . Model optimisation . Streaming . Alerts and logging
Assessment & certification
Module assignments and labs
25%
Final project and review
40%
Learners who complete all modules, submit the final project and clear the review receive a course completion certificate from Kaizen Infinities Private Limited. Project work is documented for the learner's portfolio, and interview preparation is included in the closing sessions.
Career outcomes . Roles this programme prepares for
Computer Vision EngineerAI EngineerRobotics Software EngineerEdge AI DeveloperVideo Analytics Developer
