Geometric hashing with attributed features

2002 ◽  
Author(s):  
Jyh-Jong Liu ◽  
R. Hummel
Keyword(s):  
Geometric Hashing

Object recognition with stereo vision and geometric hashing

2003 ◽  
Vol 24 (1-3) ◽  
pp. 137-146 ◽  
Author(s):  
Harrie van Dijck ◽  
Ferdinand van der Heijden
Keyword(s):  
Object Recognition ◽  
Stereo Vision ◽  
Geometric Hashing

Geometric hashing with line features

1994 ◽  
Vol 27 (3) ◽  
pp. 377-389 ◽  
Author(s):  
Frank C.D. Tsai
Keyword(s):  
Geometric Hashing ◽  
Line Features

An efficient minutiae based geometric hashing for fingerprint database

2014 ◽  
Vol 137 ◽  
pp. 115-126 ◽  
Author(s):  
Umarani Jayaraman ◽  
Aman Kishore Gupta ◽  
Phalguni Gupta
Keyword(s):  
Geometric Hashing ◽  
Fingerprint Database

scholarly journals MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

2016 ◽  
Vol III-1 ◽  
pp. 17-23 ◽  
Author(s):  
J. Jung ◽  
K. Bang ◽  
G. Sohn ◽  
C. Armenakis
Keyword(s):  
Feature Extraction ◽  
Texture Mapping ◽  
Least Square Method ◽  
Least Square ◽  
Aerial Images ◽  
Single Image ◽  
Registration Method ◽  
Geometric Hashing ◽  
Building Models ◽  
Corner Points

In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.

scholarly journals An Enhanced Geometric Hashing

2011 ◽  
Author(s):  
Umarani Jayaraman ◽  
Amit Kumar Gupta ◽  
Surya Prakash ◽  
Phalguni Gupta
Keyword(s):  
Geometric Hashing
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