• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1150-1156
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• 2009

In this paper, we propose the gradual encryption method of image using LFSR(Linear Feedback Shift Register) and 2D CAT(Two-Dimensional Cellular Automata Transform). First, an LFSR is used to create a PN(pseudo noise) sequence, which is identical to the size of the original image. Then the created sequence goes through an XOR operation with the original image resulting to the first encrypted image. Next, the gateway value is set to produce a 2D CAT basis function.The created basis function multiplied with the first encrypted image produces the 2D CAT encrypted image which is the final output. Lastly, the stability analysis verifies that the proposed method holds a high encryption quality status.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.164-167
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• 2009

In this paper, we propose the image encryption using LFSR(Linear Feedback Shift Register) and 2D CAT(Two-Dimensional Cellular Automata Transform). First, a LFSR is used to create a PN(pseudo noise) sequence, which is identical to the size of the original image. Then, the created sequence goes through a XOR operation with the original image to convert the original image. Next, the gateway value is set to produce a 2D CAT basis function. Using the created basis function, multiplication is done with the converted original image to process 2D CAT image encipherment. Lastly, the stability analysis verifies that the proposed method holds a high encryption quality status.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.610-615
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• 2005

Visual servoing is the fusion of results from many elemental areas including high-speed image processing, kinematics, dynamics, control theory, and real-time computing. It has much in common with research into active vision and structure from motion, but is quite different from the often described use of vision in hierarchical task-level robot control systems. We present a new approach to visual feedback control using image-based visual servoing with the stereo vision in this paper. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using a binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only at around a desired location but also at the other locations. The suggested technique can guide a robot manipulator to the desired location without giving such priori knowledge as the relative distance to the desired location or the model of an object even if the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by the simulation and experimental results and compared with the case of conventional method for dual-arm robot made in Samsung Electronics Co., Ltd.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1312-1320
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• 2004

Recently, even if a lot of visual feature representations have been studied and systems have been built, there is a limit to existing content-based image retrieval mechanism in its availability. One of the limits is the gap between a user's high-level concepts and a system's low-level features. And human beings' subjectivity in perceiving similarity is excluded. Therefore, correct visual information delivery and a method that can retrieve the data efficiently are required. Relevance feedback can increase the efficiency of image retrieval because it responds of a user's information needs in multimedia retrieval. This paper proposes an efficient CBIR introducing positive and negative relevance feedback with threshold value and pre-fetching to improve the performance of conventional relevance feedback mechanisms. With this Proposed feedback strategy, we implement an image retrieval system that improves the conventional retrieval system.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.3
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• pp.107-113
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• 2021

PURPOSE: Selective strengthening of the transverse abdominis muscle (TrA) during abdominal hollowing makes an important contribution to the stability and control of the spine. This study examined the effects of abdominal hollowing exercise (AHE) according to the visual feedback method on the external oblique, internal oblique, and transverse abdominis muscles. METHODS: Twenty healthy subjects were assigned randomly to an AHE with visual feedback from real-time ultrasound image (group A, n = 10), AHE with visual feedback with pressure biofeedback unit (group B, n = 10). Both groups underwent 20 min of AHE with visual feedback once daily, five days/week for two weeks. The changes in the muscle thickness of the TrA, internal oblique abdominal muscle (IO), and external oblique abdominal muscle (EO) were measured by ultrasonography. RESULTS: The thickness of TrA was changed significantly in both groups (p < .05). However, the lowest minimal detectable changes were achieved in Group A. The thickness of the IO and EO muscles in group A was changed significantly, but there were no significant changes in group B. CONCLUSION: Both visual feedback methods were effective for strengthening the TrA muscles selectively. Nevertheless, AHE with visual feedback using real-time ultrasound images may be more useful in trA muscle contraction.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.383-392
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• 2006

In this paper, we propose a new relevance feedback algorithm using Probabilistic Neural Networks(PNN) while supporting multi-class learning. Then, to validate the effectiveness of our feedback approach, we incorporate the proposed algorithm into our region-based image retrieval tool, FRIP(Finding Regions In the Pictures). In our feedback approach, there is no need to assume that feature vectors are independent, and as well as it allows the system to insert additional classes for detail classification. In addition, it does not have a long computation time for training because it only has four layers. In the PNN classification process, we store the user's entire past feedback actions as a history in order to improve performance for future iterations. By using a history, our approach can capture the user's subjective intension more precisely and prevent retrieval performance errors which originate from fluctuating or degrading in the next iteration. The efficacy of our method is validated using a set of 3000 images derived from a Corel-photo CD.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.731-737
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• 2013

In this paper, we propose an image-based output feedback robust controller of robot manipulators which have bounded parametric uncertainty. The proposed controller contains an integral action and high-gain observer in order to improve steady state error of joint position and performance deterioration due to measurement errors of joint velocity. The stability of the closed-loop system is proved by Lyapunov approach. The performance of the proposed method is demonstrated by simulations on a 5-link robot manipulators with two degrees of freedom.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.117-118
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• 2007

The received signal of ICS repeater is composed of BS(Base Station) and feedback signals. In this case, if the feedback signal is not rejected, system is oscillated. we use the IF signal for rejecting the feedback signal at ICS system. In general, SAW filter is used to reject the image of If signal. the use of this filter generates the system delay problems. In this paper, instead of SAW filter we use the Hilbert transform for rejecting image signal.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.434-445
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• 2009

Content-based image retrieval(CBIR) is the retrieval technique which uses the contents of images. However, in contrast to text data, multimedia data are ambiguous and there is a big difference between system's low-level representation and human's high-level concept. So it doesn't always mean that near points in the vector space are similar to user. We call this the semantic-gap problem. Due to this problem, performance of image retrieval is not good. To solve this problem, the relevance feedback(RF) which uses user's feedback information is used. But existing RF doesn't consider user's region-of-interest(ROI), and therefore, irrelevant regions are used in computing new query points. Because the system doesn't know user's ROI, RF is proceeded in the image-level. We propose a new ROI RF method which guides a user to select ROI from relevant images for the retrieval of complex satellite image, and this improves the accuracy of the image retrieval by computing more accurate query points in this paper. Also we propose a pruning technique which improves the accuracy of the image retrieval by using images not selected by the user in this paper. Experiments show the efficiency of the proposed ROI RF and the pruning technique.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.54-60
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• 2014

The equipment of SCARA robot in processing and assembly lines has rapidly increased. In order to achieve high productivity and flexibility, it becomes very important to develop the visual feedback control system with Off-Line Programming System(OLPS). We can save much efforts and time in adjusting robots to newly defined workcells by using OLPS. A proposed visual calibration scheme is based on position-based visual feedback. The calibration program firstly generates predicted images of objects in an assumed end-effector position. The process to generate predicted images consists of projection to screen-coordinates, visible range test, and construction of simple silhouette figures. Then, camera images acquired are compared with predicted ones for updating position and orientation data. Computation of error is very simple because the scheme is based on perspective projection, which can be also expanded to experimental results. Computation time can be extremely reduced because the proposed method does not requirethe precise calculation of tree-dimensional object data and image Jacobian.