Aim
The Advanced Computer Vision with OpenCV program aims to equip students and professionals with the skills needed to excel in computer vision. This course combines theory with practical applications, helping participants:
- Build expertise in traditional and cutting-edge computer vision techniques.
- Encourage innovation and creative problem-solving in AI and machine learning applications.
- Provide hands-on experience with OpenCV to enhance technical skills in building, analyzing, and optimizing computer vision applications.
- Prepare for industry roles in robotics, autonomous vehicles, security, and healthcare by learning how to deploy and integrate computer vision technology.
- Drive technological advancement in digital imaging and machine perception.
Program Objectives
- Master foundational computer vision concepts.
- Perform image preprocessing, classification, and segmentation tasks.
- Implement object detection algorithms such as YOLO and Faster R-CNN.
- Build convolutional neural networks (CNNs) for image analysis.
- Apply transfer learning for faster model development.
- Develop facial recognition systems using OpenCV.
- Gain expertise in Python and deep learning frameworks like TensorFlow and Keras.
- Work on real-world projects to apply advanced computer vision techniques.
Program Structure
Introduction to Computer Vision and OpenCV
- Overview of computer vision and its key applications.
- Important concepts and terms in the field.
- Introduction to OpenCV and its role in computer vision.
Setting Up OpenCV
- How to install OpenCV and basic image handling techniques.
Image Processing with OpenCV
- Learn how to read, write, and display images.
- Explore image transformations and filtering methods.
Image Manipulation and Analysis
- Work with color spaces, blending, and thresholding techniques.
- Master edge detection and geometric transformations.
Image Classification
- Introduction to CNNs and building models for image classification.
- Hands-on experience with TensorFlow and Keras.
- Apply advanced techniques like transfer learning.
Object Detection and Analysis
- Fundamentals of object detection.
- Implement YOLO and Faster R-CNN algorithms.
- Real-time object detection using OpenCV for video processing.
Image Segmentation Techniques
- Differences between semantic and instance segmentation.
- Learn U-Net for detailed image segmentation tasks.
Facial Recognition Systems
- Basics of face detection and alignment using OpenCV.
- Build and enhance facial recognition systems with advanced methods.
Advanced Computer Vision with Deep Learning
- Explore advanced neural network architectures for specific tasks.
- Customize deep learning models for real-world applications.
- Integrate OpenCV with deep learning workflows.
Capstone Project
- Build a comprehensive computer vision project using OpenCV and deep learning technologies.
- Explore industry applications, such as surveillance, autonomous driving, and healthcare diagnostics.
Who Should Enroll?
- Software developers, data scientists, engineers, students, researchers, AI enthusiasts, and professionals from other fields transitioning to AI.
Prerequisites
- Basic knowledge of Python, familiarity with algorithms and statistics, some exposure to machine learning, and appropriate hardware for running computer vision applications.
Program Outcomes
- Gain skills to build computer vision applications, master object detection, image classification, segmentation techniques, and develop proficiency with OpenCV and deep learning frameworks.
- Prepare for roles in robotics, security, autonomous systems, and more, and earn a certificate recognized by industry leaders.
Future Career Prospects
- Potential career paths include Computer Vision Engineer, Machine Learning Engineer, AI Research Scientist, Data Scientist, Software Developer for Robotics, Product Manager, Healthcare Imaging Specialist, Automotive AI Engineer, and Security Analyst.
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