Generalized hough transform and conformal geometric algebra to detect lines and planes for building 3D maps and robot navigation

2010 ◽  
Author(s):  
E Bayro-Corrochano ◽  
M Bernal-Marin
Keyword(s):  
Hough Transform ◽  
Geometric Algebra ◽  
Robot Navigation ◽  
Conformal Geometric Algebra ◽  
Generalized Hough Transform

Omnidirectional vision and conformal geometric algebra for visual landmark identification

Robotica ◽  
2008 ◽  
Vol 26 (5) ◽  
pp. 559-569 ◽  
Author(s):  
C. López-Franco ◽  
E. Bayro-Corrochano
Keyword(s):  
Geometric Algebra ◽  
Robot Navigation ◽  
Vision System ◽  
Image Features ◽  
Conformal Geometric Algebra ◽  
Omnidirectional Vision ◽  
Projective Invariants ◽  
Landmark Identification ◽  
Single Cross ◽  
Autonomous Robot Navigation

SUMMARYThe automatic landmark identification is very important in autonomous robot navigation tasks. In this paper, we use a monocular omnidirectional vision system to extract the image features and the conformal geometric algebra to compute the projective invariants from such features. We show how these features can be used to compute projective and permutationp2-invariantsfrom any kind of omnidirectional vision system. Thep2-invariantsrepresent scene sublandmarks, and a set of them characterize a landmark. The advantage of this representation is that the landmarks are more robust than the single cross-ratio.

INVESTIGATION ON MULTISETS MIXTURE LEARNING BASED OBJECT DETECTION

2007 ◽  
Vol 21 (08) ◽  
pp. 1339-1351 ◽  
Author(s):  
ZHI-YONG LIU ◽  
HONG QIAO ◽  
LEI XU
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Line Segment ◽  
Reconstruction Error ◽  
Mean Square ◽  
Noise Resistance ◽  
Generalized Hough Transform ◽  
The Mean ◽  
Sample Reconstruction

By minimizing the mean square reconstruction error, multisets mixture learning (MML) provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.

FPGA Implementation of the Generalized Hough Transform

2009 ◽  
Author(s):  
Sergio Rubén Geninatti ◽  
José Ignacio Benavides Benítez ◽  
Manuel Hernández Calviño ◽  
Nicolás Guil Mata ◽  
Juan Gómez Luna
Keyword(s):  
Hough Transform ◽  
Fpga Implementation ◽  
Generalized Hough Transform
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