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Rotationally Invariant Descriptors Using Intensity Order Pooling Yi-Chung Chen Advisor S.J.Wang
2012 IEEE, Bin Fan, Fuchao Wu, and Zhanyi Hu
Proposed method SIFT
Outline Why do we need this ? What is the key concepts ? How does it work exactly ? When should we use it ?
Why do we need this ?
0 2 36% 64%
Why do we need this ? Robustness Distinctiveness
Why do we need this ?
0255/2π 0 1
Why do we need this ? 0255/2π 0 1 Robustness Distinctiveness
Why do we need this ? RobustnessDistinctiveness
Why do we need this ? RobustnessDistinctiveness
What is the key concepts ? RobustnessDistinctiveness
What is the key concepts ?
Intensity Order Pooling
What is the key concepts ? Robustness Distinctiveness
How does it work exactly ?
How does it work exactly ? MRRID Multisupport Region Rotation and Intensity Monotonic Invariant Descriptor MROGH Multisupport Region Order-Based Gradient Histogram
When should we use it ? Blur/illu. changes MRRID > MROGH > SIFT,RIFT,DAISY …… Most cases MROGH > MRRID > SIFT,RIFT,DAISY ……
When should we use it ?
Line Matching Jonghee Park GIST CV-Lab.. Lines –Fundamental feature in many computer vision fields 3D reconstruction, SLAM, motion estimation –Useful.
Local Invariant Feature Descriptors Bin Fan National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences.
Distinctive Image Features from Scale-Invariant Keypoints Presenter :JIA-HONG,DONG Advisor : Yen- Ting, Chen 1 David G. Lowe International Journal of Computer.
Feature Detection. Description Localization More Points Robust to occlusion Works with less texture More Repeatable Robust detection Precise localization.
Viewpoint Invariant Human Re-identification in Camera Networks Using Pose Priors and Subject-Discriminative Features Ziyan Wu, Student Member, IEEE, Yang.
Evaluating Color Descriptors for Object and Scene Recognition Koen E.A. van de Sande, Student Member, IEEE, Theo Gevers, Member, IEEE, and Cees G.M. Snoek,
Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi.
Computer vision. Applications and Algorithms in CV Tutorial 9: Descriptors Visual Descriptors Motivation:: Scene Classification Introduction How to differentiate.
1 Invariant Local Feature for Object Recognition Presented by Wyman 2/05/2006.
Distinctive Image Features from Scale-Invariant Keypoints By David G. Lowe, University of British Columbia Presented by: Tim Havinga, Joël van Neerbos.
Reporter: Fei-Fei Chen. Wide-baseline matching Object recognition Texture recognition Scene classification Robot wandering Motion tracking.
Distinctive Image Features from Scale-Invariant Keypoints David Lowe.
Scale Invariant Feature Transform (SIFT) JOJO
AUTOMATIC ANNOTATION OF GEO-INFORMATION IN PANORAMIC STREET VIEW BY IMAGE RETRIEVAL Ming Chen, Yueting Zhuang, Fei Wu College of Computer Science, Zhejiang.
Deconstruction: Discriminative learning of local image descriptors Samantha Horvath Learning Based Methods in Vision 2/14/2012.
SIFT DESCRIPTOR K Wasif Mrityunjay
Distinctive Image Features from Scale-Invariant Keypoints.
A Fast Local Descriptor for Dense Matching Engin Tola, Vincent Lepetit, Pascal Fua Computer Vision Laboratory, EPFL Reporter ： Jheng-You Lin 1.
Instructor: Mircea Nicolescu Lecture 15 CS 485 / 685 Computer Vision.
A NOVEL LOCAL FEATURE DESCRIPTOR FOR IMAGE MATCHING Heng Yang, Qing Wang ICME 2008.
1 A Study of Approaches for Object Recognition Presented by Wyman Wong 12/9/2005.
11 Scale Invariant Feature Transform (SIFT) David G. Lowe University of British Columbia.
Robust and large-scale alignment Image from
AiRobots Lab., EE Dept., NCKU aiRobots Lab., EE Dept., NCKU 1 SURF: Speeded Up Robust Features 授課教授 : 連震杰 教授 Group number: 20 Advisor: Tzuu-Hseng S. Li.
Recognizing specific objects Matching with SIFT Original suggestion Lowe, 1999,2004.
Picture Comparison; now with shapes! Slightly weak during MS1, only colour comparison Several comparisons will be done Turn picture into greyscale to do.
1 Shape Descriptors for Maximally Stable Extremal Regions Per-Erik Forss´en and David G. Lowe Department of Computer Science University of British Columbia.
Chao-Yeh Chen and Kristen Grauman University of Texas at Austin Efficient Activity Detection with Max- Subgraph Search.
Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs CVPR 2005 Another Descriptor.
WLD: A Robust Local Image Descriptor Jie Chen, Shiguang Shan, Chu He, Guoying Zhao, Matti Pietikäinen, Xilin Chen, Wen Gao 报告人：蒲薇榄.
A Tutorial on using SIFT Presented by Jimmy Huff (Slightly modified by Josiah Yoder for Winter )
21 June 2009Robust Feature Matching in 2.3μs1 Simon Taylor Edward Rosten Tom Drummond University of Cambridge.
Histograms of Oriented Gradients for Human Detection(HOG) Presenter :JIA-HONG,DONG Advisor : Yen- Ting, Chen 1 Dalal, N.; Triggs, B., IEEE Computer Society.
Distinctive Image Features from Scale- Invariant Keypoints Mohammad-Amin Ahantab Technische Universität München, Germany.
776 Computer Vision Jan-Michael Frahm Spring 2012.
4. CONCLUSIONS The proposed method is computationally efficient and totally automatic. It works on very low quality images that the vascular network is.
Read Distinctive Image Features from Scale- Invariant Keypoints by Lowe Dr. Tappen also gave a presentation on SIFT. Helped clarify, what is going.
Matching results comparison between the Gixel Array Descriptor (GAD) & SIFT / SURF / BRIEF / ORB.
Scale Invariant Feature Transform Tom Duerig. Why do we care about matching features? Object Recognition Wide baseline matching Tracking/SFM.
Dermoscopic Interest Point Detector and Descriptor Howard Zhou 1, Mei Chen 2, James M. Rehg 1 1 School of Interactive Computing, Georgia Tech 2 Intel Research.
Evaluation of interest points and descriptors. Introduction Quantitative evaluation of interest point detectors –points / regions at the same relative.
Reversible Data Hiding Based on Two- Dimensional Prediction Errors 1 Source : IET Image Processing, Vol. 7, No. 9, pp , 2013 Authors : Shyh-Yih.
Distinctive Image Features from Scale-Invariant Keypoints David Lowe Presented by Tony X. Han March 11, 2008.
Distinctive Image Feature from Scale-Invariant KeyPoints David G. Lowe, 2004.
Pedestrian Detection and Localization Members: Đặng Trương Khánh Linh Bùi Huỳnh Lam Bửu Advisor: A.Professor Lê Hoài Bắc UNIVERSITY OF SCIENCE.
Object Recognition with Invariant Features n Definition: Identify objects or scenes and determine their pose and model parameters n Applications l Industrial.
Overview Introduction to local features Harris interest points + SSD, ZNCC, SIFT Scale & affine invariant interest point detectors Evaluation and comparison.
Patch Descriptors CSE P 576 Larry Zitnick Many slides courtesy of Steve Seitz.
A Statistical Approach to Speed Up Ranking/Re-Ranking Hong-Ming Chen Advisor: Professor Shih-Fu Chang.
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