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Computer Vision and Image Processing
Transform Visual Data into Insights with Advanced Computer Vision Techniques.
Skills you will gain:
This course is designed to provide a comprehensive understanding of computer vision and image processing techniques and applications. Participants will explore the foundational principles of computer vision, including image classification, object detection, and image segmentation. The course will delve into advanced topics such as facial recognition and the use of deep learning in computer vision. By the end of the course, participants will be proficient in using key computer vision
Aim:
Program Objectives:
- Understand and apply foundational computer vision concepts and techniques.
- Perform image preprocessing, classification, and segmentation.
- Implement object detection algorithms like YOLO and Faster R-CNN.
- Develop and train convolutional neural networks (CNNs) for image analysis.
- Utilize transfer learning for efficient model development.
- Implement facial recognition systems, including face detection and alignment.
- Gain proficiency in advanced computer vision tasks using deep learning frameworks.
- Apply computer vision techniques to real-world applications and projects.
- Enhance coding skills in Python and use libraries such as OpenCV, TensorFlow, and Keras.
What you will learn?
Introduction to Computer Vision:
- Overview of Computer Vision and its Applications.
- Key Concepts and Terminologies in Computer Vision.
- Image Processing Basics.
Image Classification:
- Introduction to Image Classification.
- Convolutional Neural Networks (CNNs).
- Training CNNs for Image Classification.
- Transfer Learning for Image Classification.
Object Detection:
- Basics of Object Detection.
- YOLO (You Only Look Once) Algorithm.
- Faster R-CNN (Region-Based Convolutional Neural Networks).
- Implementing Object Detection Models.
Image Segmentation:
- Introduction to Image Segmentation.
- Semantic Segmentation.
- Instance Segmentation.
- Using U-Net for Image Segmentation.
Facial Recognition:
- Understanding Facial Recognition.
- Face Detection and Alignment.
- Face Embeddings and Recognition.
- Implementing Facial Recognition Systems.
Advanced Computer Vision with Deep Learning:
- Deep Learning Architectures for Computer Vision.
- Implementing Advanced Models with TensorFlow and Keras.
- Real-World Applications of Computer Vision.
Intended For :
- Senior undergraduates and graduate students in Computer Science and related fields.
- Professionals in IT, data science, and software development looking to enhance their computer vision skills.
Career Supporting Skills