What Will You Learn?

• Introduction to Computer Vision: Understand the basics of computer vision, its applications, and its importance in various fields.
• Image Processing: Learn techniques for image enhancement, noise reduction, and filtering to preprocess images for further analysis.
• Image Representation and Feature Extraction: Explore methods to represent and extract relevant features from images, such as histograms, texture analysis, and edge detection.
• Image Classification: Understand the fundamentals of image classification and learn algorithms like support vector machines (SVM) and deep learning-based approaches for classifying images into different categories.
• Object Detection and Tracking: Study techniques for detecting and localizing objects within images or videos, including methods like Haar cascades, histogram of oriented gradients (HOG), and deep learning-based object detectors like Faster R-CNN and YOLO.
• Image Segmentation: Learn algorithms for partitioning images into meaningful regions, such as thresholding, region-based segmentation, and advanced methods like U-Net and DeepLab for semantic segmentation.
• Feature Matching and Correspondence: Explore techniques for finding correspondences between features in different images, including point-based matching, feature descriptors, and robust estimation methods like RANSAC.
• 3D Computer Vision: Introduction to 3D reconstruction, stereo vision, and depth estimation techniques to understand the 3D structure of scenes from multiple images.
• Deep Learning for Computer Vision: Gain knowledge of deep learning frameworks and architectures like convolutional neural networks (CNNs) for various computer vision tasks.
• Applications of Computer Vision: Discover real-world applications of computer vision, including facial recognition, object tracking, augmented reality, autonomous vehicles, medical imaging, and surveillance systems.
• Hands-on Projects: Work on practical projects to apply the concepts learned and gain hands-on experience in solving computer vision problems using programming languages and libraries like Python and OpenCV.
• Ethical Considerations: Understand the ethical implications of computer vision technologies, including privacy concerns, biases in algorithms, and responsible use of AI-powered visual systems.