A Modified Generalized Hough Transform for Image Search

Preeyakorn TIPWAI; Suthep MADARASMI

A Modified Generalized Hough Transform for Image Search

Preeyakorn TIPWAI, Suthep MADARASMI

Full Text Views

0

Cite this

Summary :

We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.

Publication: IEICE TRANSACTIONS on Information Vol.E90-D No.1 pp.165-172

Publication Date: 2007/01/01

Publicized

Online ISSN: 1745-1361

DOI

Type of Manuscript: Special Section PAPER (Special Section on Advanced Image Technology)

Category

Cite this

Copy

Preeyakorn TIPWAI, Suthep MADARASMI, "A Modified Generalized Hough Transform for Image Search" in IEICE TRANSACTIONS on Information, vol. E90-D, no. 1, pp. 165-172, January 2007, doi: .
Abstract: We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.
URL: https://global.ieice.org/en_transactions/information/10.1587/e90-d_1_165/_p

Copy

@ARTICLE{e90-d_1_165,
author={Preeyakorn TIPWAI, Suthep MADARASMI, },
journal={IEICE TRANSACTIONS on Information},
title={A Modified Generalized Hough Transform for Image Search},
year={2007},
volume={E90-D},
number={1},
pages={165-172},
abstract={We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.},
keywords={},
doi={},
ISSN={1745-1361},
month={January},}

Copy

TY - JOUR
TI - A Modified Generalized Hough Transform for Image Search
T2 - IEICE TRANSACTIONS on Information
SP - 165
EP - 172
AU - Preeyakorn TIPWAI
AU - Suthep MADARASMI
PY - 2007
DO -
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2007
AB - We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.
ER -