PATTERN RECOGNITION AND COMPUTER VISION LAB

Introduction

The Pattern Recognition and Computer Vision (PRCV) Laboratory in the Department of Electronics and Communication Engineering provides practical exposure to techniques used for analyzing and interpreting visual and pattern-based data. The lab focuses on concepts such as image processing, feature extraction, pattern classification, object detection, and machine learning-based recognition systems. Students implement algorithms using tools like MATLAB and Python to perform tasks such as edge detection, image segmentation, and object recognition. The laboratory supports hands-on experiments, mini projects, and research activities, enabling students to apply theoretical concepts to real-world applications in areas such as biometrics, surveillance, healthcare imaging, robotics, and intelligent systems.

Course Objectives

COB-1 Understand the in-depth concept of Pattern Recognition
COB-2 Implement Bayes Decision Theory
COB-3 Understand the in-depth concept of Perception and related Concepts
COB-4 Understand the concept of ML Pattern Classification

Course Outcomes

CO Statement Bloom’s Level
ML411P.1 Discuss various concepts of pattern recognition Understand, Remember
ML411P.2 Understanding various algorithms Understand, Create
ML411P.3 Explain and apply various computer vision techniques Apply, Evaluate
ML411P.4 Describe the concept of shape analysis and filtering Analyse, Evaluate

CO-PO-PSO Mapping

CO/PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
ML411P.1 3 2 2 3 3 2 1 3 2
ML411P.2 3 3 1 1 1 1 1 2 2 1
ML411P.3 3 2 3 3 2 2 2 3 1
ML411P.4 1 2 3 2 2 1 1 2 2 1

Facilities

Operating System / Software

Sr. No. Name Version
1. Windows 10
2. MATLAB 2022B
3. Jupyter Open Source

Hardware

Sr.No. Equipment Name Specification Quantity
1. Computer Intel Core (i5), 4th gen, 2.90 GHz, RAM-16GB 20
2. Printer HP Laser Jet Pro P1022 01

Staff

  • Lab Incharge: Dr. Apoorva Aggarwal
  • Lab Assistant: Ms. Deepa

Click for the Lab e-Content