• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1671-1679
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• 2004

This study has proposed visual-feature extraction methods for each band in wavelet domain with both spatial frequency features and multi resolution features. In addition, it has brought forward similarity measurement method using fuzzy theory and new color feature expression method taking advantage of the frequency of the same color after color quantization for reducing quantization error, a disadvantage of the existing color histogram intersection method. Experiments are performed on a database containing 1,000 color images. The proposed method gives better performance than the conventional method in both objective and subjective performance evaluation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3038-3047
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• 1996

In telerobot systems, visual servoing of a task object for a slave arm with an eye-in-hand has drawn an interesting attention. As such a task ingenerally conducted in an unstructured environment, it is very difficult to define the inverse feature Jacobian matrix. To overcome this difficulty, this paper proposes an auto-tuning fuzzy rule-based visual servo algorithm. In this algorithm, a visual servo controller composed of fuzzy rules, receives feature errors as inputs and generates the change of have position as outputs. The fuzzy rules are tuned by using steepest gradient method of the cost function, which is defined as a quadratic function of feature errors. Since the fuzzy rules are tuned automatically, this method can be applied to the visual servoing of a slave arm in real time. The effctiveness of the proposed algorithm is verified through a series of simulations and experiments. The results show that through the learning procedure, the slave arm and track object in real time with reasonable accuracy.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.32-41
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• 1990

This paper proposes a method of using image features in serving a robot manipulator. Specifically, the con-cept 'feature' is first mathematically defined and then differential relationship between the robot motion and feature vector is derived in terms of Feature Jacobian Matrix and its generalized inverse. Also, by using more features than the number of DOFs of the robot, the visual servoing performance is shown to be improv-ed. Via various examples, the method of feature-based servoing of a robot proposed in this paper is proved to be effective for conducting object-oriented robotic tasks.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1311-1316
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• 1999

A visual servoing method has been proposed based on disturbance observer to eliminate the effect of the off-diagonal component of image feature Jacobian, since performance indices such as measurement sensitivity of visual features, sensitivity of the control to noise and controllability could be improved when an image feature Jacobian was given as a block diagonal matrix. In this paper, experimental results of disturbance observer-based visual servoing are discussed where Samsung FARAMAN-AS1 6-axis industrial robot manipulator is employed. Also, the feature saturator is proposed to stabilize the disturbance observer loop by saturating the differential changes of the image features.

• Korean Journal of Cognitive Science
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• v.10 no.3
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• pp.1-15
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• 1999

Based on known physiological and psychophysical results, a neural network model for visual selection, called FeaureGate is proposed. The model consists of a hierarchy of spatial maps. and the flow of information from each level of the hierarchy to the next is controlled by attentional gates. The gates are jointly controlled by a bottom-up system favoring locations with unique features. and a top-down mechanism favoring locations with features designated as target features. The present study focuses on the top-down mechanism of the FeatureGate model that produces results similar to Moran and Desimone's (1985), which many current models have failed to explain, The FeatureGate model allows a consistent interpretation of many different experimental results in visual attention. including parallel feature searches and serial conjunction searches. attentional gradients triggered by cuing, feature-driven spatial selection, split a attention, inhibition of distractor locations, and flanking inhibition. This framework can be extended to produce a model of shape recognition using upper-level units that respond to configurations of features.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.323-330
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• 2021

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.

• Korean Institute of Interior Design Journal
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• v.22 no.5
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• pp.152-161
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• 2013

This study is about the visual appreciation by sex with the analysis of time range of observing data which was got through observation experiment with the space of lobby in hospitals. The observation data of the subjects who observed the space include the frequency and time, through which the process of visual appreciation could be evaluated with the definition of the frequency and the time of observation. First, the fact that men had higher frequency of observation than women means the former had more movement than the latter, and another fact of their fewer times can be understood as the time of their staying was shorter. That is, even though the men had more movements of sight, they showed the feature of staying shorter. Second, the rate high and low of observation frequency and times made it possible for observation characteristics to be defined as 'intensive search' 'active search' 'fixed concentration' and 'search wandering.' The definition of understanding this process of visual appreciation can be available for a frame of effective analysis of observation characteristics according to the passage of time. Third, the intense search is the case of 'high frequency' having the feature of high visual appreciation owing to the active visual actions for acquiring information. Men were found to have more intense search which decreased gradually as time passed, while women showed the feature of many times of intense search. Fourth, it was found that with many observation data in a certain range of time the subjects had fixed concentration, where women were found to have repetitive fixed concentration along with the change of observation time while men were seen to have more observation tendency for fixed concentration. Fifth, at the cross tabulation of frequency and times, men had the feature of dispersed visual appreciation while women had more distinction between fixation and movement, which revealed that there is surely the difference between men and women in the process of visual appreciation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.410-414
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• 1996

A visual servoing algorithm is proposed for a robot with a camera in hand. Specifically, novel image features are suggested by employing a viewing model of perspective projection to estimate relative pitching and yawing angles between the object and the camera. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a, commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.182-188
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• 2019

Autonomous mobile robots need SLAM (simultaneous localization and mapping) to look for the location and simultaneously to make the map around the location. In order to achieve visual SLAM, it is necessary to form an algorithm that detects and extracts feature points from camera images, and gets the camera pose and 3D points of the features. In this paper, we propose MPROSAC algorithm which combines MSAC and PROSAC, and compare the performance of optimization method and the filtering method for feature-based monocular visual SLAM. Sparse Bundle Adjustment (SBA) is used for the optimization method and the extended Kalman filter is used for the filtering method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.1010-1019
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• 1994

It is shown that there exists a nonlinear mapping which transforms image features and their changes to the desired camera motion without measuring of the relative distance between the camera and the object. This nonlinear mapping can eliminate several difficulties occurring in computing the inverse of the feature Jacobian as in the usual feature-based visual feedback control methods. Instead of analytically deriving the closed form of this mapping, a Fuzzy Membership Function-based Neural Network (FMFNN) incorporating a Fuzzy-Neural Interpolating Network is used to approximate the nonlinear mapping. Several FMFNN's are trained to be capable of tracking a moving object in the whole workspace along the line of sight. For an effective implementation of the proposed FMF network, an image feature selection process is investigated. Finally, several numerical examples are presented to show the validity of the proposed visual servoing method.