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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.
Participant’s Eligibility
- 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.
Program Outcomes
- Gain a solid understanding of computer vision and image processing principles.
- Develop proficiency in image classification, object detection, and image segmentation.
- Implement facial recognition systems and advanced computer vision models.
- Master the use of key computer vision libraries such as OpenCV, TensorFlow, and Keras.
- Apply computer vision concepts to real-world projects and scenarios.
- Enhance Python programming skills for advanced computer vision tasks.
- Complete practical coding exercises and projects demonstrating computer vision expertise.
- Earn a certificate of completion recognized by industry leaders.