'Sift' diaporamas de présentation

Object Removal in Multi-View Photos

Object Removal in Multi-View Photos. Aaron McClennon-Sowchuk , Michail Greshischev. Objectives. Remove an object from a set of images by using information (pixels) from other images in the set. The images must be of the same scene but can vary in time of taken and/or perspective of scene.

611 views • 53 slides

SIFT Flow: Dense Correspondence across Scenes and Its Applications

SIFT Flow: Dense Correspondence across Scenes and Its Applications. Ce Liu, Jenny Yuen, and Antonio Torralba, MIT PAMI 2011 (ECCV 2008) Presented by Han Hu, I-vision Lab. Ce Liu, Researcher at Microsoft Research New England Experience:

802 views • 34 slides

Feature Extraction and Matching Feature Tracking Sudipta N Sinha Sep 19, 2006

Feature Extraction and Matching Feature Tracking Sudipta N Sinha Sep 19, 2006. Outline. Feature Extraction and Matching (for Larger Motion) What are features ? Tasks Detection: finding the feature locations Representation: computing a compact descriptor

612 views • 29 slides

Following Autonomous Mobile Robot

Following Autonomous Mobile Robot. Outline. ????? ????????? LED ? ODCS-SIFT ? (hybird ? ). ?????. ?????. ????. ?????????. Human Following Behavior of an Autonomous Mobile Robot Using Light-Emitting Device. LED ?. ????. L? ?? LED ? ??? ??? u? ?? LED ? ??? ?????? d?LED ? robot ?????

466 views • 26 slides

REU Report I

REU Report I. Alla Petrakova UCF. Material covered. MATLAB Derivatives, Filters, Thresholding , Equalization, etc. Correlation, Convolution Edge Detection ( Sobel , Laplacian of Gaussian, Canny) Harris Corner Detector SIFT Adaboost , face detection SVM Optical Flow Bag of Features.

287 views • 15 slides

210 views • 14 slides