Computer Vision Training
Introduction to Computer Vision
Gain an overview of computer vision, including its purpose, applications, and the role it plays in analyzing and interpreting visual data. Learn about the basic concepts, history, and key areas of research.
Image Processing Fundamentals
Study the fundamentals of image processing. Learn about image representation, color spaces, image transformations, filtering, and enhancement techniques. Understand how to preprocess and analyze images for further processing.
Feature Detection and Matching
Explore techniques for feature detection and matching in computer vision. Learn about keypoint detection, descriptors, and algorithms such as SIFT, SURF, and ORB. Understand how to match and track features across images.
Object Detection and Recognition
Learn about object detection and recognition methods. Study various approaches, including classical techniques (Haar cascades, HOG) and modern methods (YOLO, SSD, Faster R-CNN). Understand how to detect and classify objects in images.
Image Segmentation
Study image segmentation techniques to partition images into meaningful regions. Learn about segmentation methods such as thresholding, clustering (K-means), and advanced techniques (deep learning-based segmentation).
Deep Learning for Computer Vision
Explore the application of deep learning in computer vision. Learn about convolutional neural networks (CNNs), architectures like VGG, ResNet, and Inception, and how to train and evaluate deep learning models for image analysis.
3D Vision and Reconstruction
Study 3D vision techniques and applications. Learn about stereo vision, depth estimation, 3D reconstruction, and the use of LiDAR and depth sensors in creating 3D models from images.
Computer Vision in Robotics
Understand the role of computer vision in robotics. Learn how robots use vision systems for navigation, object manipulation, and interaction with the environment. Explore techniques for visual SLAM and real-time vision processing.
Applications and Case Studies
Explore various applications of computer vision in real-world scenarios. Study case studies from fields such as healthcare, autonomous vehicles, surveillance, and augmented reality to understand the practical use of computer vision technologies.
Ethics and Future Trends
Discuss the ethical considerations and future trends in computer vision. Learn about privacy concerns, bias in computer vision systems, and emerging technologies and research areas shaping the future of the field.
Project Work and Practical Exercises
Engage in project work and practical exercises to apply computer vision concepts. Work on real-world problems, implement algorithms, and build computer vision applications to gain hands-on experience.
Computer Vision Syllabus
Introduction to Computer Vision
- Definition and goals of computer vision
- Applications in various fields
- Robotics
- Medical imaging
- Autonomous vehicles
- Others
- History and evolution of computer vision
Image Processing Basics
- Image representation
- Pixels
- Color models
- Image enhancement
- Filters
- Transformations
- Image segmentation
- Thresholding
- Region-based methods
- Noise reduction techniques
Image Transformations
- Geometric transformations
- Translation
- Rotation
- Scaling
- Image warping and morphing
- Homogeneous coordinates and transformations
Feature Extraction and Selection
- Point features
- Harris corner detection
- SIFT (Scale-Invariant Feature Transform)
- SURF (Speeded-Up Robust Features)
- Edge detection
- Sobel
- Canny edge detector
- Region-based features
- Histogram of Oriented Gradients (HOG)
Image Classification and Object Recognition
- Supervised learning basics
- Classification methods
- SVM (Support Vector Machines)
- k-Nearest Neighbors (k-NN)
- CNNs (Convolutional Neural Networks)
- Object detection
- Haar cascades
- R-CNN (Region-based Convolutional Neural Networks)
- YOLO (You Only Look Once)
Deep Learning for Computer Vision
- Introduction to neural networks
- Convolutional Neural Networks (CNNs)
- Transfer learning and fine-tuning
- Advanced architectures
- ResNet
- VGG
- Others
Motion Analysis and Tracking
- Optical flow techniques
- Motion estimation and tracking algorithms
- Kalman filters
- Mean-shift
- Others
- Multiple object tracking
3D Computer Vision
- Depth perception methods
- Stereo vision
- Structured light
- Time-of-flight
- 3D reconstruction techniques
- SFM (Structure from Motion)
- SLAM (Simultaneous Localization and Mapping)
- Applications in augmented reality and virtual reality
Video Analysis
- Video processing basics
- Temporal filtering
- Frame interpolation
- Action recognition and event detection
- Video summarization and understanding
Applications and Case Studies
- Real-world applications
- Biometrics
- Surveillance
- Image retrieval
- Case studies
- Medical imaging
- Autonomous driving
- Robotics
- Ethical considerations and challenges in computer vision
Hands-on Projects and Practical Applications
- Implementing algorithms in Python with libraries
- OpenCV
- TensorFlow
- PyTorch
- Building and evaluating models for specific tasks
- Object detection
- Image classification
- Working with real datasets and deploying models in applications
Future Directions and Emerging Trends
- Generative models
- GANs (Generative Adversarial Networks)
- Explainable AI in computer vision
- Integration with other AI disciplines
- NLP (Natural Language Processing)
- Reinforcement learning
Additional Topics (Optional)
- Image and video compression
- GPU programming for computer vision
- Mobile and embedded vision applications
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details