scholarly journals SIFT Saliency Analysis for Matching Repetitive Structures

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Yang ◽  
Xiaopeng Hu ◽  
Lijuan Xu ◽  
Fan Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Image Matching ◽  
Feature Matching ◽  
Region Of Interest ◽  
Reference Image ◽  
Matching Method ◽  
Feature Saliency ◽  
Saliency Analysis ◽  
Feature Stability

The ambiguity resulting from repetitive structures in a scene presents a major challenge for image matching. This paper proposes a matching method based on SIFT feature saliency analysis to achieve robust feature matching between images with repetitive structures. The feature saliency within the reference image is estimated by analyzing feature stability and dissimilarity via Monte-Carlo simulation. In the proposed method, feature matching is performed only within the region of interest to reduce the ambiguity caused by repetitive structures. The experimental results demonstrate the efficiency and robustness of the proposed method, especially in the presence of respective structures.

Identification of the Hook on Investment Casting Shell Line Based on Machine Vision

2012 ◽  
Vol 220-223 ◽  
pp. 1356-1361
Author(s):  
Xi Jie Tian ◽  
Jing Yu ◽  
Chang Chun Li
Keyword(s):  
Machine Vision ◽  
Investment Casting ◽  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Image Acquisition ◽  
Target Range ◽  
Target Area ◽  
Matching Method ◽  
Vertical Projection

In this paper, the idea identify the hook on investment casting shell line based on machine vision has been proposed. According to the characteristic of the hook, we do the image acquisition and preprocessing, we adopt Hough transform to narrow the target range, and find the target area based on the method combining the level projection and vertical projection, use feature matching method SIFT to do the image matching. Finally, we get the space information of the target area of the hook.

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 Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

2019 ◽  
Vol 9 (2) ◽  
pp. 309-314
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Local Features ◽  
Feature Descriptor ◽  
Matching Method ◽  
Multiple Images ◽  
Feature Sets ◽  
Precise Relationship

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

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.

scholarly journals Particle Swarm Image Matching on Epipolar Line

2014 ◽  
Vol XL-4 ◽  
pp. 87-91
Author(s):  
J.-S. Hsia
Keyword(s):  
Image Matching ◽  
Particle Swarm ◽  
Window Size ◽  
Pso Algorithm ◽  
Least Square ◽  
Image Point ◽  
Reference Image ◽  
Epipolar Line ◽  
Matching Method ◽  
The Difference

This paper presents a method for determining the 3D position of an image point on a reference image using particle swarm optimization (PSO) to search the height (Z value) that gives the biggest Normalized Cross Correlation (NCC) coefficient. The searching area is in the surrounding of the height of the image point. The NCC coefficient evaluates the similarity with the image point and a corresponding point on an epipolar line in the search image. The position of corresponding image point on the epipolar line is determined by the height point on a sloping line locus. The PSO algorithm starts with a swarm of random particles. The position of each particle is a potential solution in the problem space. Each particle is given a randomized velocity and attracted toward the location of the best fitness. The position of each particle is iteratively modified by adding a newly computed velocity to its current position. The velocity is updated by three factors which are two attractions from local best position and global best position, two strengths of the attractions, and two uniform random numbers for each attraction. The iteration will stop when the current solution is convergent. The time of computation is highly related to the range of height and the interval of height enumeration when the approach to find a corresponding image point of an image point on a reference image is based on the height enumeration along sloping line locus. The precision of results can be improved by decreasing the interval of height enumeration. This shows the limitation of the enumeration method in the efficiency and accuracy. The issue is overcome by a method of using PSO algorithm. The proposed method using different parameters such as the size of image window, the number of particles, and the size of the height searching range has been applied to aerial stereo images. The accuracy of tested results is evaluated on the base of the comparison to the reference data from the results of least-square matching being manually given initial points. The evaluation result shows that tested results has given a solution to a level of less than 1 centimetre without using refined image matching method. The same level of accuracy can reach even when the searching range is bigger than 90 meters. But the difference of image window size may lead to the change of the matching result. And, without the procedures of both coarse-to-fine hierarchical solution and refined image matching method, the algorithm still can give the same accuracy level of least-square image matching resulting. This method also shows its ability to give reasonable matching results without manual assistance.

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 Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation

2020 ◽  
Vol 9 (6) ◽  
pp. 347
Author(s):  
Jingxue Wang ◽  
Ning Zhang ◽  
Xiangqian Wu ◽  
Weixi Wang
Keyword(s):  
Image Matching ◽  
Three Dimensional ◽  
Similarity Score ◽  
Reference Image ◽  
Range Image ◽  
Matching Method ◽  
Point Matching ◽  
Second Stage ◽  
Edge Matching ◽  
Close Range

Reliable image matching is the basis of image-based three-dimensional (3D) reconstruction. This study presents a quasi-dense matching method based on triangulation constraint and propagation as applied to different types of close-range image matching, such as illumination change, large viewpoint, and scale change. The method begins from a set of sparse matched points that are used to construct an initial Delaunay triangulation. Edge-to-edge matching propagation is then conducted for the point matching. Two types of matching primitives from the edges of triangles with areas larger than a given threshold in the reference image, that is, the midpoints of edges and the intersections between the edges and extracted line segments, are used for the matching. A hierarchical matching strategy is adopted for the above-mentioned primitive matching. The points that cannot be matched in the first stage, specifically those that failed in a gradient orientation descriptor similarity constraint, are further matched in the second stage. The second stage combines the descriptor and the Mahalanobis distance constraints, and the optimal matching subpixel is determined according to an overall similarity score defined for the multiple constraints with different weights. Subsequently, the triangulation is updated using the newly matched points, and the aforementioned matching is repeated iteratively until no new matching points are generated. Twelve sets of close-range images are considered for the experiment. Results reveal that the proposed method has high robustness for different images and can obtain reliable matching results.

To Improve Accuracy of Radionuclide Therapy Dosimetry Planning Using Monte Carlo Method

2017 ◽  
Vol 62 (1) ◽  
pp. 49-55
Author(s):  
Наркевич ◽  
B. Narkevich ◽  
Гончаров ◽  
M. Goncharov ◽  
Лысак ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Count Rate ◽  
Radionuclide Therapy ◽  
Analytical Formula ◽  
Region Of Interest ◽  
The Monte Carlo Method ◽  
Improve Accuracy

Purpose: Development and clinical testing of methodology dosimetry planning of radionuclide therapy based on Monte Carlo simulation of radiation transfer process. Material and methods: The method of determination in absolute units of radiopharmaceutical (RP) activity accumulated in tumor lesions. The technique is based on scintigraphy syringe containing diagnostic RP activity, biplane patient scintigraphy after injection of the RP and determination of the RP accumulation when administered calculated using the Monte Carlo method for the absorption and scattering of radiation in the patient’s body and in the collimator of the gamma camera. Code MCNP Monte Carlo simulation was used. The layout of determination of the value of accumulated RP activity in the patient’s tumor site implies successive implementation of the following three steps. 1. Scintigraphic images are obtained of the vial containing already known activity of the RP placed at the fixed source-to-collimator distance, following which estimation of the detector count rate within the specified region of interest of the vial image is undertaken. 2. Therapeutic activity A0 is introduced in the patient’s body, scintigraphic examination of the patient is performed. Estimation of the detector count rate in the region where the tumor is located and the value of tissue background in the close enough vicinity to the tumor is performed using the tools for contouring the region of interest on the obtained planar image provided using the software imbedded in the scintigraphic equipment. 3. Value of accumulated activity RP in the affected tumor is determined according to the correction factor which is calculated using Monte-Carlo method for specific clinical case for the geometry used in obtaining scintigraphic images which is identical to the conditions of measurement of activity in the vial and in the patient’s body. The technique has been tested in the study, with an injection of 30 MBq of 123I-MIBG child with neuroblastoma. Results: The level of accumulation of radiopharmaceutical in the tumor of the adrenal gland was 0.78 MBq, i.e. 2.6 % of the administered activity. This corresponds to literature data (average about 2.4 %) for scintigraphic studies of children with neuroblastomas. When using the known calculation method for analytical formula without the introduction of corrections for the absorption and scattering of radiation was obtained a result of 1.02 MBq, i.e. overestimation was 31 %. Conclusions: Introduction calculated by the Monte Carlo method for the absorption and scattering of radiation during scintigraphy patient can improve the accuracy of dosimetry planning of radionuclide therapy.

scholarly journals A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization

2020 ◽  
Vol 13 (1) ◽  
pp. 47
Author(s):  
Lirong Ding ◽  
Ji Zhou ◽  
Lingxuan Meng ◽  
Zhiyong Long
Keyword(s):  
Image Matching ◽  
Geographic Location ◽  
Region Of Interest ◽  
Classification Problem ◽  
Matching Method ◽  
Matching Accuracy ◽  
Oblique View ◽  
Sample Number ◽  
Potential Applications ◽  
Uav Images

Cross-view image matching has attracted extensive attention due to its huge potential applications, such as localization and navigation. Unmanned aerial vehicle (UAV) technology has been developed rapidly in recent years, and people have more opportunities to obtain and use UAV-view images than ever before. However, the algorithms of cross-view image matching between the UAV view (oblique view) and the satellite view (vertical view) are still in their beginning stage, and the matching accuracy is expected to be further improved when applied in real situations. Within this context, in this study, we proposed a cross-view matching method based on location classification (hereinafter referred to LCM), in which the similarity between UAV and satellite views is considered, and we implemented the method with the newest UAV-based geo-localization dataset (University-1652). LCM is able to solve the imbalance of the input sample number between the satellite images and the UAV images. In the training stage, LCM can simplify the retrieval problem into a classification problem and consider the influence of the feature vector size on the matching accuracy. Compared with one study, LCM shows higher accuracies, and Recall@K (K ∈ {1, 5, 10}) and the average precision (AP) were improved by 5–10%. The expansion of satellite-view images and multiple queries proposed by the LCM are capable of improving the matching accuracy during the experiment. In addition, the influences of different feature sizes on the LCM’s accuracy are determined, and we found that 512 is the optimal feature size. Finally, the LCM model trained based on synthetic UAV-view images was evaluated in real-world situations, and the evaluation result shows that it still has satisfactory matching accuracy. The LCM can realize the bidirectional matching between the UAV-view image and the satellite-view image and can contribute to two applications: (i) UAV-view image localization (i.e., predicting the geographic location of UAV-view images based on satellite-view images with geo-tags) and (ii) UAV navigation (i.e., driving the UAV to the region of interest in the satellite-view image based on the flight record).

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