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Learn Computer Vision, earn certificates with free online courses from Harvard, Stanford, MIT, University of Pennsylvania and other top universities around the world. Read reviews to decide if a class is right for you.
Explore camera models, coordinate systems, and transformations in computer vision. Learn about perspective projection, focal length, and essential concepts for image analysis and 3D reconstruction.
Explore mean shift algorithm for computer vision, covering theory, implementation, and applications in tracking and segmentation. Learn from expert Dr. Mubarak Shah.
Explore Lucas-Kanade Tracker (KLT) for computer vision, covering global motion estimation, object tracking, and 2D transformations. Gain insights into advanced tracking algorithms.
Explore global motion in computer vision, covering motion models, flow estimation, image warping, video mosaics, and projective transformations for advanced visual analysis techniques.
Learn about optical flow, rotation, image formation, and 3D rigid motion in computer vision. Explore displacement models and projective transformations using point correspondences.
Learn about optical flow in computer vision, covering applications, optimization problems, Laplacians, and solutions, with practical examples and in-depth explanations.
Learn about Scale-invariant Feature Transform (SIFT), a robust algorithm for detecting and describing local features in images. Explore key point extraction, scale space, and descriptor generation.
Explore edge detection techniques in computer vision, including Marr-Hildreth and Canny methods. Learn about 2-D Gaussian filters, zero crossings, and hysteresis thresholding for improved image analysis.
Learn about image filtering techniques, including noise reduction, edge detection, and smoothing. Explore applications in computer vision and gain hands-on experience with MATLAB functions for image processing.
Explore fundamental concepts of computer vision, including image processing, object detection, and facial recognition. Learn about real-world applications in various fields.
Explore computer vision fundamentals, deep learning applications, and advanced topics like object detection, segmentation, and action recognition in images and videos.
Explore computer vision fundamentals, from image processing to deep learning applications. Covers perception, image types, and real-world uses in retail and autonomous vehicles.
Introduction to graduate-level computer vision, covering mathematical foundations, deep learning applications, and advanced topics like object detection and image segmentation.
Explore video adversarial attacks using the Motion-Excited Sampler technique, covering problem formulation, methodology, gradient estimation, and results analysis.
Explore cutting-edge research on tiny action recognition, including data analysis, evaluation methods, and experimental results in computer vision and AI.
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