• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.207-214
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• 2019

This paper proposes a new feature extraction method for automatically estimating the number of target and detecting the chaff using high range resolution profile(HRRP). Feature of one-dimensional range profile is expected to be limited or missing due to lack of information according to the time. The proposed method considers the dynamic movements of targets depending on the radial velocity. The observed HRRP sequence is used to construct a time-range distribution matrix, then assuming diverse radial velocities reflect the number of target and seduction chaff launch, the proposed method utilizes the characteristic of the gradient distribution on the time-range distribution matrix image, which is validated by electromagnetic computation data and dynamic simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.403-410
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• 2016

This paper proposes a robust image-based visual servoing scheme using a nonlinear observer for a monocular eye-in-hand manipulator. The proposed control method is divided into a range estimation phase and a target-tracking phase. In the range estimation phase, the range from the camera to the target is estimated under the non-moving target condition to solve the uncertainty of an interaction matrix. Then, in the target-tracking phase, the feature point uncertainty caused by the unknown motion of the target is estimated and feature point errors converge sufficiently near to zero through compensation for the feature point uncertainty.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.5 s.182
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• pp.68-76
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• 2006

The ability to recognize human emotion is one of the hallmarks of human-robot interaction. Hence this paper describes the realization of emotion recognition. For emotion recognition from voice, we propose a new feature called frequency range of meaningful signal. With this feature, we reached average recognition rate of 76% in speaker-dependent. From the experimental results, we confirm the usefulness of the proposed feature. We also define the noise environment and conduct the noise-environment test. In contrast to other features, the proposed feature is robust in a noise-environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4587-4605
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• 2019

High Efficiency Video Coding (HEVC) suffers from high computational complexity due to its quad-tree structure in motion estimation (ME). This paper exposes an adaptive search range decision algorithm for accelerating HEVC integer-pel ME on GPU which estimates the optimal search range (SR) using a MAP (Maximum A Posteriori) estimator. There are three main contributions; First, we define the motion feature as the standard deviation of motion vector difference values in a CTU. Second, a MAP estimator is proposed, which theoretically estimates the motion feature of the current CTU using the motion feature of a temporally adjacent CTU and its SR without any data dependency. Thus, the SR for the current CTU is parallelly determined. Finally, the values of the prior distribution and the likelihood for each discretized motion feature are computed in advance and stored at a look-up table to further save the computational complexity. Experimental results show in conventional HEVC test sequences that the proposed algorithm can achieves high average time reductions without any subjective quality loss as well as with little BD-bitrate increase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.7
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• pp.1311-1328
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• 2011

We re-render a photographic image as a simulated pencil drawing using two independent line integral convolution (LIC) algorithms that express tone and feature lines. The LIC for tone is then applied in the same direction across the image, while the LIC for features is applied in pixels close to each feature line in the direction of that line. Features are extracted using the coherent line scheme. Changing the direction and range of the LICs allows a wide range of pencil drawing style to be mimicked. We tested our algorithm on diverse images and obtained encouraging results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1132-1142
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• 1991

This paper proposed a method of feature parameter extraction for shape information analysis of moving object. In the 2-D plane, moving object are extracted by the difference method. Feature parameters of moving object are chosen area, perimeter, a/p ratio, vertex, x/y ratio. We changed brightness variation from the range of 600Lux to the 1400Lux and then determined Permissible Error range of feature parameter due to the brightness variation. So as to verify the validity of proposed method, experiment are performed with a toy car and it's results showed that decision error was less than 6%.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1614-1623
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• 2021

In this paper, the purpose is evaluation of the effect of using fractal feature in machine learning based pancreatic tumor classification. We used the data that Pancreas CT series 469 case including 1995 slice of benign and 1772 slice of malignant. Feature selection is implemented from 109 feature to 7 feature by Lasso regularization. In Fractal feature, fractal dimension is obtained by box-counting method, and hurst coefficient is calculated range data of pixel value in ROI. As a result, there were significant differences in both benign and malignancies tumor. Additionally, we compared the classification performance between model without fractal feature and model with fractal feature by using support vector machine. The train model with fractal feature showed statistically significant performance in comparison with train model without fractal feature.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.777-786
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• 2008

Human face avatar is important information in nowadays, such as describing real people in virtual world. In this paper, we have presented a face avatar creation and warping algorithm by using face feature analysis method, in order to detect face feature, we utilized local mean method based on facial feature appearance and face geometric information. Then detect facial candidates by using it's character in $YC_bC_r$ color space. Meanwhile, we also defined the rules which are based on face geometric information to limit searching range. For analyzing face feature, we used face feature points to describe their feature, and analyzed geometry relationship of these feature points to create the face avatar. Then we have carried out simulation on PC and embed mobile device such as PDA and mobile phone to evaluate efficiency of the proposed algorithm. From the simulation results, we can confirm that our proposed algorithm will have an outstanding performance and it's execution speed can also be acceptable.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.134-141
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• 2014

Mobile robots use various sensors for navigation such as wheel encoder, vision sensor, sonar, and laser sensors. Dead reckoning is used with wheel encoder, resulting in the accumulation of positioning errors. For that reason wheel encoder can not be used alone. Too much information of vision sensors leads to an increase in the number of features and complexity of perception scheme. Also Sonar sensor is not suitable for positioning because of its poor accuracy. On the other hand, laser sensor provides accurate distance information relatively. In this paper we propose to extract the angular information from the distance information of laser range finder and use the Kalman filter that match the heading and distance of the laser range finder and those of wheel encoder. For laser scanner with one feature point error may increase much when the feature point is variant or jumping to a new feature point. To solve the problem, we propose to use two feature points and show that the positioning error can be reduced much.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.617-624
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• 2004

This paper describes a new facial feature localization method that uses Adjacent Depth Differences(ADD) in 3D facial surface. In general, human recognize the extent of deepness or shallowness of region relatively, in depth, by comparing the neighboring depth information among regions of an object. The larger the depth difference between regions shows, the easier one can recognize each region. Using this principal, facial feature extraction will be easier, more reliable and speedy. 3D range images are used as input images. And ADD are obtained by differencing two range values, which are separated at a distance coordinate, both in horizontal and vertical directions. ADD and input image are analyzed to extract facial features, then localized a nose region, which is the most prominent feature in 3D facial surface, effectively and accurately.