scholarly journals Multi-Template Matching: a versatile tool for object-localization in microscopy images

2019 ◽  
Author(s):  
Laurent S. V. Thomas ◽  
Jochen Gehrig
Keyword(s):  
Template Matching ◽  
Object Localization ◽  
Source Codes ◽  
Biological Objects ◽  
Versatile Tool ◽  
Microscopy Images ◽  
Multiple Template ◽  
Automatic Localization

AbstractWe implemented multiple template matching as both a Fiji plugin and a KNIME workflow, providing an easy-to-use method for the automatic localization of objects of interest in images. We demonstrate its application for the localization of entire or partial biological objects. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow can be downloaded from nodepit space or the associated GitHub repository. Python source codes and documentations are available on the following GitHub repositories: LauLauThom/MultiTemplateMatching and LauLauThom/MultipleTemplateMatching-KNIME.

An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection

2011 ◽  
Vol 38 (12) ◽  
pp. 15172-15182 ◽  
Author(s):  
Na Dong ◽  
Chun-Ho Wu ◽  
Wai-Hung Ip ◽  
Zeng-Qiang Chen ◽  
Ching-Yuen Chan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Template Matching ◽  
Multiple Template

Development of a multiple-template matching technique for removal of false positives in a computer-aided diagnostic scheme

2001 ◽  
Author(s):  
Qiang Li ◽  
Shigehiko Katsuragawa ◽  
Roger M. Engelmann ◽  
Samuel G. Armato III ◽  
Heber MacMahon ◽  
...  
Keyword(s):  
Template Matching ◽  
False Positives ◽  
Computer Aided ◽  
Matching Technique ◽  
Multiple Template

scholarly journals Automatic identification of resting state networks: an extended version of multiple template-matching

2015 ◽  
Author(s):  
Javier Guaje ◽  
Juan Molina ◽  
Jorge Rudas ◽  
Athena Demertzi ◽  
Lizette Heine ◽  
...  
Keyword(s):  
Resting State ◽  
Template Matching ◽  
Automatic Identification ◽  
Extended Version ◽  
Resting State Networks ◽  
Multiple Template

scholarly journals Automatic Localization Algorithm for Ultrasound Breast Tumors Based on Human Visual Mechanism

2017 ◽  
Author(s):  
Yuting Xie ◽  
Ke Chen ◽  
Jiangli Lin
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Breast Tumors ◽  
Input Image ◽  
Object Localization ◽  
Initial Contour ◽  
Natural Image ◽  
Localization Algorithm ◽  
Localization Method ◽  
Automatic Localization

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image segmentation [15]-[19]. In ultrasound (US) breast images, compared with background areas, tumor is more salient because of its higher contrast. In this paper, we develop a novel automatic localization method based on HVM for automatic segmentation of ultrasound (US) breast tumors. First, the input image is smoothed by convolution with a linearly separable Gaussian filter and then subsampled into a 9-layer Gaussian pyramid. Then intensity, blackness ratio, and superpixel contrast features are combined to compute saliency map, in which Winner Take All algorithm is used to localize the most salient region, presenting with a circle on the localized target. Finally the circle is taken as the initial contour of CV level set to finish the extraction of breast tumor. The localization method has been tested on 400 US beast images, among which 378 images have higher saliency than background areas and succeed in localization, with high accuracy 92.00%. The HVM localization method can be used to localize the tumors, combined with this method, CV level set can achieve the fully automatic segmentation of US breast tumors. By combing intensity, blackness ratio and superpixel contrast features, the proposed localization method can successfully avoid the interference caused by background areas with low echo and high intensity. Moreover, multi-object localization of US breast images can be considered in future employment.

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