Accurate matching method of multimodal image based on phase congruency and local mutual information

2014 ◽  
Author(s):  
Chunyang Zhao ◽  
Huaici Zhao ◽  
Gang Zhao
Keyword(s):  
Mutual Information ◽  
Phase Congruency ◽  
Matching Method ◽  
Accurate Matching

scholarly journals Structural Representation: Reducing Multi-Modal Image Registration to Mono-Modal Problem

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Keyvan Kasiri ◽  
David Clausi ◽  
Paul Fieguth
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
The Other ◽  
Optimization Process ◽  
Computational Time ◽  
Phase Congruency ◽  
Structural Representation ◽  
Brain Images ◽  
Modal Approach ◽  
Gradient Information

<p>Registration of multi-modal images has been a challenging task<br />due to the complex intensity relationship between images. The<br />standard multi-modal approach tends to use sophisticated similarity<br />measures, such as mutual information, to assess the accuracy<br />of the alignment. Employing such measures imply the increase in<br />the computational time and complexity, and makes it highly difficult<br />for the optimization process to converge. The presented registration<br />method works based on structural representations of images<br />captured from different modalities, in order to convert the multimodal<br />problem into a mono-modal one. Two different representation<br />methods are presented. One is based on a combination of<br />phase congruency and gradient information of the input images,<br />and the other utilizes a modified version of entropy images in a<br />patch-based manner. Sample results are illustrated based on experiments<br />performed on brain images from different modalities.</p>

scholarly journals RESEARCH PROGRESS ON FEATURE MATCHING OF SAR AND OPTICAL IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 77-82 ◽  
Author(s):  
H. Qian ◽  
J. W. Yue ◽  
M. Chen
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Optical Image ◽  
Research Progress ◽  
Phase Congruency ◽  
Matching Method ◽  
Sift Algorithm ◽  
Matching Problems ◽  
Optical Images ◽  
Edge Features

Abstract. Before obtaining information and identifying ground target from images, image matching is necessary. However, problems of strong pixel noise interference and nonlinear gray scale differences in synthetic aperture radar image still exist. Feature matching becomes a kind possible solution. To learn the research progress of SAR and optical image matching, as well as finding solutions for above matching problems, a summary for feature matching with SAR and optical image is indispensable. By listing three typical methods below, we can discuss and compare how researchers improve and innovate methods for feature matching from different angles in matching process. First method is feature matching method proposed by CHEN Min et. It uses phase congruency method to detect point features. Feature descriptors are based on gaussian-gamma-shaped edge strength maps instead of original images. This method combines both edge features and point features to reach a match target. The second one is SAR-SIFT algorithm of F. Dellinger et. This kind of method is based on improvement of sift algorithm. It proposes a SAR-Harris method and also a calculation method for features descriptors named gradient by ratio. Thirdly, it is feature matching method proposed by Yu Qiuze et. By using edge features of image and improvement of hausdorff distance for similarity measure, it applies genetic algorithm to accelerate matching search process to complete matching tasks. Those methods are implemented by using python programs, and are compared by some indexes. Experimental data used multiple sets of terrasar and optical image pairs of different resolutions. To some extent, the results demonstrate that all three kinds of feature methods can improve the matching effect between SAR and optical images. It can be easier to reach match purposes of SAR and optical images by using image edge features, while such methods are too dependent on the edge features.

scholarly journals An Accurate Matching Method for Projecting Vector Data into Surveillance Video to Monitor and Protect Cultivated Land

2020 ◽  
Vol 9 (7) ◽  
pp. 448 ◽  
Author(s):  
Zhenfeng Shao ◽  
Congmin Li ◽  
Deren Li ◽  
Orhan Altan ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Feature Matching ◽  
Video Image ◽  
Cultivated Land ◽  
Surveillance Video ◽  
Vector Data ◽  
Matching Method ◽  
Plain Area ◽  
Video Frames ◽  
Gis Data ◽  
Accurate Matching

The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or systems have their own shortcomings when implemented in monitoring cultivated land. To address this problem, this paper aims to propose an accurate matching method for projecting vector data into surveillance video, considering the topographic characteristics of cultivated land in plain area. Once an adequate number of control points are identified from 2D (two-dimensional) GIS data and the selected reference video image, the alignment of 2D GIS data and PTZ (pan-tilt-zoom) video frames can be realized by automatic feature matching method. Based on the alignment results, we can easily identify the occurrence of farmland destruction by visually inspecting the image content covering the 2D vector area. Furthermore, a prototype of intelligent surveillance video system for cultivated land is constructed and several experiments are conducted to validate the proposed approach. Experimental results show that the proposed alignment methods can achieve a high accuracy and satisfy the requirements of cultivated land monitoring.

Assembly process case matching based on a multilevel assembly ontology method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hanqing Gong ◽  
Lingling Shi ◽  
Xiang Zhai ◽  
Yimin Du ◽  
Zhijing Zhang
Keyword(s):  
Individual Case ◽  
Case Based Reasoning ◽  
Assembly Process ◽  
Matching Method ◽  
Process Knowledge ◽  
Content Type ◽  
Distance Model ◽  
Case Structure ◽  
Structure Assembly ◽  
Accurate Matching

Purpose The purpose of this study is to achieve accurate matching of new process cases to historical process cases and then complete the reuse of process knowledge and assembly experience. Design/methodology/approach By integrating case-based reasoning (CBR) and ontology technology, a multilevel assembly ontology is proposed. Under the general framework, the knowledge of the assembly domain is described hierarchically and associatively. On this basis, an assembly process case matching method is developed. Findings By fully considering the influence of ontology individual, case structure, assembly scenario and introducing the correction factor, the similarity between non-correlated parts is significantly reduced. Compared with the Triple Matching-Distance Model, the degree of distinction and accuracy of parts matching are effectively improved. Finally, the usefulness of the proposed method is also proved by the matching of four practical assembly cases of precision components. Originality/value The process knowledge in historical assembly cases is expressed in a specific ontology framework, which makes up for the defects of the traditional CBR model. The proposed matching method takes into account all aspects of ontology construction and can be used well in cross-ontology similarity calculations.

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