Computer Vision - Course Details
Learn how computers see and interpret images. From image classification to object detection and segmentation. Build vision AI applications.
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What you'll learn?
- Image processing fundamentals
- CNN for image classification
- Object detection (YOLO, R-CNN)
- Image segmentation
- Video analysis
Requirements
- Deep Learning basics
- Python and PyTorch/TensorFlow
- Introduction to computer vision
- Applications in industry, healthcare, and security
- Image representation (grayscale, RGB, channels)
- Image transformations: resizing, cropping, rotation, scaling
- Color spaces (RGB, HSV, Grayscale)
- Key points and descriptors (SIFT)
- Edge detection: Canny, Sobel, Laplacian
- Contours, shapes, and object boundaries
- Classical methods: Haar Cascades, HOG + SVM
- Deep learning-based methods: YOLO
- CNN architecture: convolution, pooling, fully connected layers
- Modern CNN architectures: ResNet
- CNN applications in image classification
- Semantic segmentation (e.g., U-Net)
- Instance segmentation
- Pose estimation and human keypoint detection
- Real-time object tracking (OpenCV tracking)
- Optical flow and motion detection
- OpenCV for image processing and feature extraction
- Integration with deep learning frameworks (PyTorch, TensorFlow, Keras)
- CNN implementation for image classification
- Face recognition using OpenCV and CNN
- Object detection project using YOLO
- Semantic segmentation project
- Real-time tracking application
- End-to-end computer vision project
- Combining image preprocessing, feature extraction, CNN/deep learning, and advanced CV tasks
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