'Scale invariant feature transform' presentation slideshows

Richard G. Baraniuk Chinmay Hegde Sriram Nagaraj

Manifold Learning in the Wild A New Manifold Modeling and Learning Framework for Image Ensembles Aswin C. Sankaranarayanan Rice University. Richard G. Baraniuk Chinmay Hegde Sriram Nagaraj. Sensor Data Deluge. Concise Models.

964 views • 80 slides

Motivation

Motivation. Where is my W-2 Form?. Video-based Tracking. Camera. Camera view of the desk. Overhead video camera. Example. 40 minutes, 1024x768 @ 15 fps. System Overview. User. Input Video. System Overview. Internal Representation. User. Input Video. Desk. Desk. Video Analysis. T.

742 views • 73 slides

Attention Model Based SIFT Keypoints Filtration for Image Retrieval

Attention Model Based SIFT Keypoints Filtration for Image Retrieval. Ke Gao 1,2 , Shouxun Lin 1 , Yongdong Zhang 1 , Sheng Tang 1 , Huamin Ren 1,3 1 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, 100080

567 views • 43 slides

COMP 9517 Computer Vision

COMP 9517 Computer Vision. Frequency Techniques. Frequency Domain V.S. Spatial Domain. Spatial domain The image plane itself Direct manipulation of pixels Changes in pixel position correspond to changes in the scene Frequency domain Fourier transform of an image

391 views • 38 slides

Richard G. Baraniuk Chinmay Hegde

Manifold Learning in the Wild A New Manifold Modeling and Learning Framework for Image Ensembles Aswin C. Sankaranarayanan Rice University. Richard G. Baraniuk Chinmay Hegde. Sensor Data Deluge. Internet Scale Databases. Tremendous size of corpus of available data

608 views • 36 slides

Video Google: A Text Retrieval Approach to Object Matching in Videos

Video Google: A Text Retrieval Approach to Object Matching in Videos. Josef Sivic and Andrew Zisserman Robotics Research Group, Department of Engineering Science University of Oxford, United Kingdom. Goal. To retrieve those key frames and shots of a video containing a particular object .

582 views • 32 slides

UCF REU: Weeks 1 & 2

UCF REU: Weeks 1 & 2. Gradient Code. Gradient. D irection of the Gradient: Calculating theta. Picking a threshold for the gradient map. Subtract the gradient from the original image display it . Detailing on camera. Edge Detection. Filters. Lapacian of Gaussian ( LoG ).

442 views • 29 slides

Image Localization and Geo-registration

Image Localization and Geo-registration. Embedded and High Performance Computing Karl Ni, karl.ni@ll.mit.edu MIT Lincoln Laboratory 22 September 2011.

415 views • 27 slides

A Study of Approaches for Object Recognition

A Study of Approaches for Object Recognition. Presented by Wyman Wong 12/9/2005. Outlines. Introduction Model-Based Object Recognition AAM Inverse Composition AAM View-Based Object Recognition Recognition based on boundary fragments Recognition based on SIFT Proposed Research

635 views • 26 slides

Specific Object Recognition using SIFT

Machine Vision and Image Processing Group (Student Group) Electronic Research Center of Iran University of Science and Technology http://mvip.iust.ac.ir. Specific Object Recognition using SIFT. Presentation by: Amir Azizi. ???? ?????? ????? ? ?????? ?????. ???? 1389 November 2010.

457 views • 26 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 ?????

461 views • 26 slides

Patch Descriptors

406 views • 21 slides

Lecture 07

330 views • 18 slides

Puzzle Solver

Puzzle Solver. Sravan Bhagavatula EE 638 Project Stanford ECE. Overview. Purpose of Project High Level Implementation Scale Invariant Feature Transform Explanation of Algorithm Results Future Work. Purpose of Project. Solving a jigsaw Finding placements

276 views • 11 slides