PPT-Deep Learning for Computer Vision

Author : yoshiko-marsland | Published Date : 2017-05-23

Presenter Yanming Guo Adviser Dr Michael S Lew Deep learning Human Computer 14 Human vs Computer Deep learning Human Computer 14 Human vs Computer Deep Learning

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Deep Learning for Computer Vision: Transcript

Presenter Yanming Guo Adviser Dr Michael S Lew Deep learning Human Computer 14 Human vs Computer Deep learning Human Computer 14 Human vs Computer Deep Learning Why better. Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Chapter 14 . The pinhole camera. Structure. Pinhole camera model. Three geometric problems. Homogeneous coordinates. Solving the problems. Exterior orientation problem. Camera calibration. 3D reconstruction. Chapter 5 . The Normal Distribution. Univariate. Normal Distribution. For short we write:. Univariate. normal distribution describes single continuous variable.. Takes 2 parameters . m. and . s. 2. Pierre . Baldi. University of California, Irvine. Two Questions. “If we solve computer vision, we have pretty much solved AI.” . A-NNs . vs. B-NNs and Deep Learning.. If we solve computer vision…. Moitreya Chatterjee, . Yunan. . Luo. Image Source: Google. Outline – This Section. Why do we need Similarity Measures. Metric Learning as a measure of Similarity. Notion of a metric. Unsupervised Metric Learning. Moitreya Chatterjee, . Yunan. . Luo. Image Source: Google. Outline – This Section. Why do we need Similarity Measures. Metric Learning as a measure of Similarity. Notion of a metric. Unsupervised Metric Learning. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). Chapter 19 . Temporal models. 2. Goal. To track object state from frame to frame in a video. Difficulties:. Clutter (data association). One image may not be enough to fully define state. Relationship between frames may be complicated. It’s no secret that this world we live in can be pretty stressful sometimes. If you find yourself feeling out-of-sorts, pick up a book.According to a recent study, reading can significantly reduce stress levels. In as little as six minutes, you can reduce your stress levels by 68%. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand About the class. COMP 648: Computer Vision Seminar. Instructor: . Vicente. . Ordóñez. (Vicente . Ordóñez. Román). Website: . https://www.cs.rice.edu/~vo9/cv-seminar. Location: Zoom – Keck Hall 101. Manoranjan . Paul. , PhD, SMIEEE, MACS (Snr) CP. Associate Professor in Computer Science. School . of Computing & . Mathematics, Faculty of BJBS. Steering Committee Member. CSU Machine Learning (CML) Research Unit.

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