• Journal of IKEEE
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• v.26 no.4
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• pp.686-692
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• 2022

In this paper, the matching error rates of modified area-based, energy-based algorithms, and learning-based structures were compared for stereo image matching. Census transform (CT) based on region and life propagation (BP) algorithm based on energy were selected, respectively.Existing algorithms have been improved and implemented in an embedded processor environment so that they can be used for stereo image matching in mobile systems. Even in the case of the learning base to be compared, a neural network structure that utilizes small-scale parameters was adopted. To compare the error rates of the three matching methods, Middlebury's Tsukuba was selected as a test image and subdivided into non-occlusion, discontinuous, and disparity error rates for accurate comparison. As a result of the experiment, the error rate of modified CT matching improved by about 11% when compared with the existing algorithm. BP matching was about 87% better than conventional CT in the error rate. Compared to the learning base using neural networks, BP matching was about 31% superior.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.183-191
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• 2016

Recently, in rehabilitation treatment, the situation that requires a measure of the accuracy of the pose and movement of joints is being increased due to the habits and lifestyle of modern people and the environment. In particular, there is a need for active automated system that can determine itself for the matching rate of pose Basically, a method for measuring the matching rate of pose is used by extracting an image using the Kinect or extracting a silhouette using the imaging device. However, in the case of extracting a silhouette, it is difficult to set the comparison, and in the case of using the Kinect sensor, there is a disadvantages that high accumulated error rate according to movement. Therefore, In this paper, we propose a method to reduce the accumulated error of matching rate of pose getting the rotation angle of joint by measuring the real-time amount of change of 9-axis sensor. In particular, it can be measured same conditions that unrelated of the physical condition and unaffected by the data for the back and forth movement, because of it compares the current rotation angle of the joint. Finally, we show a comparative advantage results by compared with traditional method of extracting a silhouette and a method using a Kinect sensor.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.66-73
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• 1992

This paper proposes a new motion estimation algorithm which takes into account the rate-distortion relation in encoding motion compensated error images. The proposed algorithm is based on a new block-matching criterion which is the function of not only the mean squared block-matching error but also the code length for the entropy coded motion vector. The proposed algorithm optimizes the trade-off between the bit rate for motion compensated error images and the bit rate for the motion vectors. Simulation results show that in the motion compensated image coding the proposed motion estimator improves the overall performance by 0.5 dB when compared to the motion estimator which uses MSE only.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.484-490
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• 2018

This paper describes an algorithm for calculating load impedance by measuring voltage and current components using a VI sensor in a 13.56 MHz impedance automatic matching system. We propose an algorithm that improves the error rate by using an arbitrary complex calibration load instead of the conventional $50{\Omega}$ calibration load. The error rate is targeted to attain average values of $R_{IN}$ and $X_{IN}$ at 1% and 20% or less, respectively. First, the IF frequency is calculated using a mixer to reduce the error rate. Second, when the arbitrary complex load is used as the calibration load, the error rate $R_{IN}$ decreased from 4.7 % to 0.3 % on average, and $X_{IN}$ decreased from 102 % to 18.3 % on average.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.23-28
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• 2013

The process of a feature matching for two different spatial data sets is similar to the process of classification as a binary class such as matching or non-matching. In this paper, we calculated a threshold by applying an equal error rate (EER) which is widely used in biometrics that classification is a main topic into spatial data sets. In a process of discriminating what's a matching or what's not, a precision and a recall is changed and a trade-off appears between these indexes because the number of matching pairs is changed when a threshold is changed progressively. This trade-off point is EER, that is, threshold. To the result of applying this method into training data, a threshold is estimated at 0.802 of a value of shape similarity. By applying the estimated threshold into test data, F-measure that is a evaluation index of matching method is highly value, 0.940. Therefore we confirmed that an accurate threshold is calculated by EER without person intervention and this is appropriate to matching different spatial data sets.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.737-738
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• 2008

An implementation of robust stereo matching algorithm under variable illumination conditions is presented in this paper. By applying window-based matchingmethod to global stereo matching algorthm using belief propagation, our implemented system shows low error rate and could efficiently cope with general radiometric disimilarities between images acquired from two different point of views.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.554-559
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• 2009

This paper proposes a new method of weighted template matching fur machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. The experiment compares confusion matrices of the template matching, error back propagation neural network classifier, and the proposed weighted template matching respectively. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.796-804
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• 2021

In computer vision, visual tracking method addresses the problem of localizing an specific object in video sequence according to the bounding box. In this paper, we propose a tracking method by introducing the feature correlation comparison into the siamese network to increase its matching identification. We propose a way to compute location of object to improve matching performance by a correlation operation, which locates parts for solving the searching problem. The higher layer in the network can extract a lot of object information. The lower layer has many location information. To reduce error rate of the object center point, we built a siamese network that extracts the distribution and location information of target objects. As a result of the experiment, the average center error rate was less than 25%.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.271-274
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• 2003

This paper presents a robust error concealment method for burst-packet-loss networks. The proposed error concealment algorithm can reduce the computational complexities of the existing error concealment methods. Moreover, experimental results show that the proposed method produces the better video quality than the conventional boundary matching algorithm.