A Robust Matching Method for Low-Textured Images Based on Co-Occurrence Probability of Geometry-Optimized Pixel Patterns

Shuichi Akizuki, Manabu Hashimoto

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a template matching algorithm that is applicable to low-texture images, such as range images. Among high-speed template matching methods, the co-occurrence probability-based template matching (CPTM) method is useful and effective. This method uses some sets of selected pixel patterns that have relatively low occurrence probability in a template image. By using such a small number of distinctive data, reliable matching has been achieved, in addition to high-speed processing. However, this method is problematic insofar as the extraction of distinctive pixels is difficult when the distribution of the occurrence probability is uniform. For example, this is frequently found in range images. We improve the CPTM method in order to address this problem. A key idea is to optimize geometric pixel relations in the pixel patterns when the proposed method calculates the occurrence probability of pixel patterns. Experimental results have confirmed that the proposed method increases the detection rate from 73% to 90% without sacrificing its capacity for high speed. This shows that the performance of our method is better than that of conventional methods.

Original languageEnglish
Pages (from-to)14-22
Number of pages9
JournalElectronics and Communications in Japan
Volume98
Issue number9
DOIs
Publication statusPublished - 2015 Sep 1
Externally publishedYes

Fingerprint

Template matching
templates
Pixel
Pixels
pixels
occurrences
Geometry
Template Matching
geometry
high speed
Range Image
High Speed
textures
Textures
Matching Algorithm
Processing
Texture
Template
Optimise
Calculate

Keywords

  • co-occurrence probability
  • combinatorial optimization
  • image matching
  • low-texture image
  • pixel selection

ASJC Scopus subject areas

  • Signal Processing
  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

A Robust Matching Method for Low-Textured Images Based on Co-Occurrence Probability of Geometry-Optimized Pixel Patterns. / Akizuki, Shuichi; Hashimoto, Manabu.

In: Electronics and Communications in Japan, Vol. 98, No. 9, 01.09.2015, p. 14-22.

Research output: Contribution to journalArticle

@article{0cf7559532ca43bf9f69400117510ce2,
title = "A Robust Matching Method for Low-Textured Images Based on Co-Occurrence Probability of Geometry-Optimized Pixel Patterns",
abstract = "In this paper, we propose a template matching algorithm that is applicable to low-texture images, such as range images. Among high-speed template matching methods, the co-occurrence probability-based template matching (CPTM) method is useful and effective. This method uses some sets of selected pixel patterns that have relatively low occurrence probability in a template image. By using such a small number of distinctive data, reliable matching has been achieved, in addition to high-speed processing. However, this method is problematic insofar as the extraction of distinctive pixels is difficult when the distribution of the occurrence probability is uniform. For example, this is frequently found in range images. We improve the CPTM method in order to address this problem. A key idea is to optimize geometric pixel relations in the pixel patterns when the proposed method calculates the occurrence probability of pixel patterns. Experimental results have confirmed that the proposed method increases the detection rate from 73{\%} to 90{\%} without sacrificing its capacity for high speed. This shows that the performance of our method is better than that of conventional methods.",
keywords = "co-occurrence probability, combinatorial optimization, image matching, low-texture image, pixel selection",
author = "Shuichi Akizuki and Manabu Hashimoto",
year = "2015",
month = "9",
day = "1",
doi = "10.1002/ecj.11694",
language = "English",
volume = "98",
pages = "14--22",
journal = "Electronics and Communications in Japan",
issn = "1942-9533",
publisher = "Scripta Technica",
number = "9",

}

TY - JOUR

T1 - A Robust Matching Method for Low-Textured Images Based on Co-Occurrence Probability of Geometry-Optimized Pixel Patterns

AU - Akizuki, Shuichi

AU - Hashimoto, Manabu

PY - 2015/9/1

Y1 - 2015/9/1

N2 - In this paper, we propose a template matching algorithm that is applicable to low-texture images, such as range images. Among high-speed template matching methods, the co-occurrence probability-based template matching (CPTM) method is useful and effective. This method uses some sets of selected pixel patterns that have relatively low occurrence probability in a template image. By using such a small number of distinctive data, reliable matching has been achieved, in addition to high-speed processing. However, this method is problematic insofar as the extraction of distinctive pixels is difficult when the distribution of the occurrence probability is uniform. For example, this is frequently found in range images. We improve the CPTM method in order to address this problem. A key idea is to optimize geometric pixel relations in the pixel patterns when the proposed method calculates the occurrence probability of pixel patterns. Experimental results have confirmed that the proposed method increases the detection rate from 73% to 90% without sacrificing its capacity for high speed. This shows that the performance of our method is better than that of conventional methods.

AB - In this paper, we propose a template matching algorithm that is applicable to low-texture images, such as range images. Among high-speed template matching methods, the co-occurrence probability-based template matching (CPTM) method is useful and effective. This method uses some sets of selected pixel patterns that have relatively low occurrence probability in a template image. By using such a small number of distinctive data, reliable matching has been achieved, in addition to high-speed processing. However, this method is problematic insofar as the extraction of distinctive pixels is difficult when the distribution of the occurrence probability is uniform. For example, this is frequently found in range images. We improve the CPTM method in order to address this problem. A key idea is to optimize geometric pixel relations in the pixel patterns when the proposed method calculates the occurrence probability of pixel patterns. Experimental results have confirmed that the proposed method increases the detection rate from 73% to 90% without sacrificing its capacity for high speed. This shows that the performance of our method is better than that of conventional methods.

KW - co-occurrence probability

KW - combinatorial optimization

KW - image matching

KW - low-texture image

KW - pixel selection

UR - http://www.scopus.com/inward/record.url?scp=84938830894&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938830894&partnerID=8YFLogxK

U2 - 10.1002/ecj.11694

DO - 10.1002/ecj.11694

M3 - Article

AN - SCOPUS:84938830894

VL - 98

SP - 14

EP - 22

JO - Electronics and Communications in Japan

JF - Electronics and Communications in Japan

SN - 1942-9533

IS - 9

ER -