• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.3
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• pp.364-377
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• 2014

In this paper, we have developed a target recognition algorithm based on a template matching technique using Synthetic Aperture Radar (SAR) images. For efficient computations, Radon transform-based azimuth estimation algorithm was used with the template matching. MSTAR data set was divided into two groups according to the depression angles, which were a train set and a test set. Template data were generated by rotating and cropping chips which were from MSTAR train set using the azimuth estimation algorithm. Then the template matching process between test data and template data was performed under various conditions. Performance variation according to contrast enhancement preprocessing which is scarce in open literature was also presented. The analysis results show that the target recognition algorithm could be useful for the automatic target recognition using SAR images.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.413-423
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• 2008

The template matching method is used as a simple method to track objects or patterns that we want to search for in the input image data from image sensors. It recognizes a segment with the highest correlation as a target. The concept of this method is similar to that of SNF (Strongest Neighbor Filter) that regards the measurement with the highest signal intensity as target-originated among other measurements. The SNF assumes that the strongest neighbor (SN) measurement in the validation gate originates from the target of interest and the SNF utilizes the SN in the update step of a standard Kalman filter (SKF). The SNF is widely used along with the nearest neighbor filter (NNF), due to computational simplicity in spite of its inconsistency of handling the SN as if it is the true target. Probabilistic Strongest Neighbor Filter for m validated measurements (PSNF-m) accounts for the probability that the SN in the validation gate originates from the target while the SNF assumes at any time that the SN measurement is target-originated. It is known that the PSNF-m is superior to the SNF in performance at a cost of increased computational load. In this paper, we suggest an image tracking algorithm that combines the template matching and the PSNF-m to estimate the states of a tracked target. Computer simulation results are included to demonstrate the performance of the proposed algorithm in comparison with other algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.15-23
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• 2017

Template attack is a powerful side-channel analysis technique which can be performed by an attacker who has a test device that is identical to target device. Template attack is consisted of building template in profiling phase and matching the target device using template that were calculated in profiling phase. One methods to improve the success rate of template attack is to better estimate template which is consisted sample mean and sample covariance matrix of gaussian distribution in template profiling. However restriction of power trace in profiling phase led to poor template estimation. In this paper, we propose new method to select noisy power trace in profiling phase. By eliminating noisy power trace in profiling phase, we can construct more advanced mean and covariance matrix which relates to better performance in template attack. We proved that the proposed method is valid through experiments.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.283-298
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• 2022

As Synthetic Aperture Radar (SAR) image can be acquired regardless of the weather and day or night, it is highly recommended to be used for Automatic Target Recognition (ATR) in the fields of surveillance, reconnaissance, and national security. However, there are some limitations in terms of cost and operation to build various and vast amounts of target images for the SAR-ATR system. Recently, interest in the development of an ATR system based on simulated SAR images using a target model is increasing. Attributed Scattering Center (ASC) matching and template matching mainly used in SAR-ATR are applied to target classification. The method based on ASC matching was developed by World View Vector (WVV) feature reconstruction and Weighted Bipartite Graph Matching (WBGM). The template matching was carried out by calculating the correlation coefficient between two simulated images reconstructed with adjacent points to each other. For the performance analysis of the two proposed methods, the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset was used, which has been recently published by the U.S. Defense Advanced Research Projects Agency (DARPA). We conducted experiments under standard operating conditions, partial target occlusion, and random occlusion. The performance of the ASC matching is generally superior to that of the template matching. Under the standard operating condition, the average recognition rate of the ASC matching is 85.1%, and the rate of the template matching is 74.4%. Also, the ASC matching has less performance variation across 10 targets. The ASC matching performed about 10% higher than the template matching according to the amount of target partial occlusion, and even with 60% random occlusion, the recognition rate was 73.4%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.99-108
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• 2004

In this paper, a new fast detection and clutter rejection method is proposed for CCD-image-based Automatic Target Detection System. For defence application, fast computation is a critical point, thus we concentrated on the ability to detect various targets with simple computation. In training stage, 1D template set is generated by regional vertical projection and K-means clustering, and binary tree structure is adopted to reduce the number of template matching in test stage. We also use adaptive skip-width by Correlation-based Adaptive Predictive Search(CAPS) to further improve the detecting speed. In clutter rejection stage, we obtain Fourier Descriptor coefficients from boundary information, which are useful to rejected clutters.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.532-535
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• 2003

In this paper, we propose a new algorithm that can enhance the speed of template matching of hand vascular pattern person verification or recognition system. Various template matching algorithms have advantages in the matching accuracy, but most of the algorithms suffer from computational burden. To reduce the computational amount, with accuracy maintained, we propose following template matching scenario as follows. firstly, original hand vascular image is re-sampled in order to reduce spatial resolution. Secondly, reconstructed image is projected to vertical and horizontal direction, being converted to two one dimensional (1D) data. Thirdly, converted data is used to estimate spatial discrepancy between stored template image and target image. Finally, matching begins from where the estimated order is highest, and finishes when matching decision function is computed to be over certain threshold. We've applied the proposed algorithm to hand vascular pattern identification application for biometrics, and observed dramatic matching speed enhancement. This paper presents detailed explanation of the proposed algorithm and evaluation results.

• Ocean Systems Engineering
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• v.10 no.4
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• pp.435-449
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• 2020

This paper proposes a method to estimate the underwater target object's yaw angle using a sonar image. A simulator modeling imaging mechanism of a sonar sensor and a generative adversarial network for style transfer generates realistic template images of the target object by predicting shapes according to the viewing angles. Then, the target object's yaw angle can be estimated by comparing the template images and a shape taken in real sonar images. We verified the proposed method by conducting water tank experiments. The proposed method was also applied to AUV in field experiments. The proposed method, which provides bearing information between underwater objects and the sonar sensor, can be applied to algorithms such as underwater localization or multi-view-based underwater object recognition.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1224-1227
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• 2002

In this paper, we propose a composition method of the template image whose detection performance does not have incorrect detection and improves also on the tough photography conditions of the outdoors, rainy weather and night. This research was done to measure a relative position between a spreader and a target container with image processing to realize full-automatic quayside gantry cranes. By the proposal method, we confirmed that the template image for object detection has a contour image more effective than a gray image.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.838-846
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• 2004

In the case where both a camera and a target are moving at the same time, the image background is successively changed, and the overlap with other moving objects is apt to be generated. The snake algorithms have been variously used in tracking the object, but it is difficult to be applied in the excessive overlap with other objects and the large bias between the snake and the target. To solve this problem, this paper presents an extended snake model. It includes an additional energy function which considers the temporal variation rate of the snake's area and a SSD algorithm which generates the template adaptive to the snake detected in the previous frame. The new energy function prevents the snake from over-shrinking or stretching and the SSD algorithm with adaptively changing template allows the prediction of the target's position in the next frame. The experimental results have shown that the proposed algorithm successfully tracks the target even when the target is temporarily occluded by other objects.