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Combining Local Descriptors for 3D Object Recognition and CategorizationAuthor :Andrea Selinger Salgian Department of Computer Science The College of New Jersey date:2009/02/23 repoter：鄒嘉恆
Introduction Combine keyed context patches and SIFT to significantly reduce the error rate on recognition and categorization.
Outline The descriptors Experimental results ConclusionKeyed context patches SIFT Experimental results Object recognition Object categorization Conclusion
Keyed context patches
SIFT(1/5) Scale-space extrema detection
SIFT(2/5) Keypoint localization Elimination low contrastElimination edge response
SIFT(3/5) Orientation assignment
SIFT(4/5) Keypoint descriptor extraction
Experimental results(1/2)Object recognition
Experimental results(2/2)Object categorization
Conclusion Confirm that the performance of the descriptor combination is higher than that of either of the descriptors alone.
Distinctive Image Features from Scale-Invariant Keypoints
Feature Detection. Description Localization More Points Robust to occlusion Works with less texture More Repeatable Robust detection Precise localization.
Distinctive Image Features from Scale-Invariant Keypoints David Lowe.
3D Model Matching with Viewpoint-Invariant Patches(VIP) Reporter ：鄒嘉恆 Date ： 10/06/2009.
Extrinsic and Depth Calibration of TOF-cameras Reporter ：鄒嘉恆 Date ： 2009/12/22.
Fusion of Time-of-Flight Depth and Stereo for High Accuracy Depth Maps Reporter ：鄒嘉恆 Date ： 2009/11/17.
Towards Geographical Referencing of Monocular SLAM Reconstruction Using 3D City Models: Application to Real- Time Accurate Vision-Based Localization Reporter.
Vision-based Motion Planning for an Autonomous Motorcycle on Ill-Structured Road Reporter ：鄒嘉恆 Date ： 08/31/09.
Context-based object-class recognition and retrieval by generalized correlograms by J. Amores, N. Sebe and P. Radeva Discussion led by Qi An Duke University.
Zhimin CaoThe Chinese University of Hong Kong Qi YinITCS, Tsinghua University Xiaoou TangShenzhen Institutes of Advanced Technology Chinese Academy of.
Presented by Xinyu Chang
The SIFT (Scale Invariant Feature Transform) Detector and Descriptor
Distinctive Image Features from Scale- Invariant Keypoints Mohammad-Amin Ahantab Technische Universität München, Germany.
Instructor: Mircea Nicolescu Lecture 15 CS 485 / 685 Computer Vision.
Recognition using Regions CVPR Outline Introduction Overview of the Approach Experimental Results Conclusion.
A Study of Approaches for Object Recognition
Object Recognition with Invariant Features n Definition: Identify objects or scenes and determine their pose and model parameters n Applications l Industrial.
Distinctive image features from scale-invariant keypoints. David G. Lowe, Int. Journal of Computer Vision, 60, 2 (2004), pp Presented by: Shalomi.
Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi.
Object Class Recognition Using Discriminative Local Features Gyuri Dorko and Cordelia Schmid.
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