Research of Image Matching Based on Evolutionary Algorithm

2012 ◽  
Vol 236-237 ◽  
pp. 1168-1171
Author(s):  
Qing Hua Wu ◽  
Fang Xie ◽  
Yu Xin Sun ◽  
Jin Zhang ◽  
Xue Song Yan
Keyword(s):  
Evolutionary Algorithm ◽  
Image Matching ◽  
Search Strategy ◽  
Orthogonal Test ◽  
Match Problem ◽  
Matching Method ◽  
Matching Algorithm ◽  
Image Match ◽  
Matching Process ◽  
Common Defect

In image matching research, how to ensure that best match’s accuracy of the premise and a significant reduction in the amount of computing is the focus of concern by researchers. Search strategy to find the best match location of the image matching process to determine the amount of computing of image matching, in the existing image matching method are used to traverse search strategy, it is difficult to reduce the amount of computing. This is a common defect of the existing image matching algorithm. Traditional evolutionary algorithm trapped into the local minimum easily. Therefore, based on a simple evolutionary algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, to prevent local convergence to form a new evolutionary algorithm. Compared the traditional evolutionary algorithm, the new algorithm enlarges the searching space and the complexity is not high. We use this new algorithm in image matching; from the results we reach the conclusion: in the optimization precision and the optimization speed, the new algorithm is efficiency for the image match problem.

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Image Feature ◽  
Brute Force ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Average Precision ◽  
Detection Model ◽  
Matching Process

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

scholarly journals OPTIMIZATION ALGORITHM OF TILTED IMAGE MATCHING BASED ON ADAPTIVE INITIAL OBJECT ASPECT

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 547-554
Author(s):  
C. Zhang ◽  
Y. Ge ◽  
Q. Zhang ◽  
B. Guo
Keyword(s):  
Least Squares ◽  
Image Matching ◽  
Normal Vector ◽  
Matching Method ◽  
Initial Value ◽  
Initial State ◽  
Matching Process ◽  
Low Degree ◽  
Verification Process ◽  
Initial Object

Abstract. When adopting the matching method of the least squares image based on object-patch to match tilted images, problems like the low degree of connection points for images with the discontinuity of depth or the discrepancy in elevation or low availability of aerotriangulation points would frequently appear. To address such problems, a tilted-image-matching algorithm based on an adaptive initial object-patch is proposed by this paper. By means of the existing initial values of the interior and exterior orientation elements of the tilted image and the information of object points generated in the matching process, the algorithm takes advantage of the method of multi-patch forward intersection and object variance partition so as to adaptively calculate the elevation of the object-patch and the initial value of the normal vector direction angle. Furthermore, this algorithm aims to solve the problem of difficulties in matching the tilted image with its corresponding points brought about by the low accuracy of the initial value of the tilted image when adopting the matching method of the least squares image based on object-patch to match the tilted image with high discrepancy in elevation. We adopt the algorithm as proposed in this paper and the least squares image matching method in which the initial state of the object-patch is horizontal to the object-patch respectively to conduct the verification process of comparing and matching two groups of tilted images. Finally, the effectiveness of the algorithm as proposed in this paper is verified by the testing results.

Research of Stereo Matching Based on HALCON

2013 ◽  
Vol 415 ◽  
pp. 361-364
Author(s):  
Hui Yu Xiang ◽  
Zhe Li ◽  
Jia Jun Huang ◽  
Baoan Han
Keyword(s):  
Image Matching ◽  
Stereo Matching ◽  
Vision System ◽  
Difficult Problem ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Matching Window

Binocular stereo matching is a hot and difficult problem in machine vision. In this paper, based on the matching method of Halcon which is visual software perform image matching. First, performing binocular stereo vision system calibration, based on the calibration results acquired the epipolar standard geometric structure. Then, image matching researched under this structure. At last, using ncc matching algorithm, through comparing the different parameters matching window obtain ideal match results. Experiments prove that this method not only can effectively shorten matching time, but also can achieve higher matching accuracy.

scholarly journals AN IMPROVED IMAGE MATCHING METHOD BASED ON SURF ALGORITHM

2018 ◽  
Vol XLII-3 ◽  
pp. 179-184 ◽  
Author(s):  
S. J. Chen ◽  
S. Z. Zheng ◽  
Z. G. Xu ◽  
C. C. Guo ◽  
X. L. Ma
Keyword(s):  
Remote Sensing ◽  
Information Entropy ◽  
Image Matching ◽  
Feature Matching ◽  
Operating Efficiency ◽  
Similarity Function ◽  
Entropy Theory ◽  
Matching Method ◽  
Matching Process ◽  
Invariant Transformation

Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

Base on SIFT-Harris Operator of the Document Image Matching Method

2012 ◽  
Vol 524-527 ◽  
pp. 3870-3874 ◽  
Author(s):  
Yuan Hang Cheng ◽  
Xiao Wei Han
Keyword(s):  
Image Matching ◽  
Affine Transformation ◽  
Document Image ◽  
Matching Method ◽  
Matching Algorithm ◽  
Gray Correlation ◽  
Coarse Search ◽  
Relationship Of ◽  
Speed And Accuracy ◽  
Correlation Matching

Abstract：Proposed a method of document image matching based on SIFT-Harris operator, Use of SIFT-Harris operator to accurately search match for the same name point. First use SIFT operator for the coarse search match to find out a rough affine transformation relationship of matched Image and based image, Then used the Harris operator and gray correlation matching algorithm refined search. It improved the matching speed and accuracy. Experimental results show that the method works well for document image matching.

Efficient Image Matching Algorithm Using Distance Transform and Particle Swarm Optimization

2011 ◽  
Vol 341-342 ◽  
pp. 753-757 ◽  
Author(s):  
Yong Fang Guo ◽  
Huang Kai
Keyword(s):  
Image Matching ◽  
Computation Time ◽  
Pso Algorithm ◽  
Search Space ◽  
Geometric Distortion ◽  
Illumination Change ◽  
Matching Method ◽  
Multiple Points ◽  
Matching Algorithm ◽  
Distance Transformation

Correspondence estimation is one of the fundamental challenges in computer vision lying in the core of many problems, from stereo and motion analysis to object recognition. The direct matching method would computer the fitness for every pixel in the resolution space and it would sensitive to illumination change and other change. For the image attained by distance transformation can weaken the bad influence under geometric distortion and edge change, and have the ability to resistant inversion. The PSO algorithm supports parallel search of multiple points that are changed along a smooth trajectory within the search space. The paper applied distance transformation and PSO to image matching and the experiment results showed that comparing with the traditional direct matching, the method of the paper is not sensitive to illumination change and drastically reduced the required computation time.

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