• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7302-7308
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• 2014

Detecting target objects robustly in natural environments is a difficult problem in the computer vision and image processing areas. This paper suggests a method of robustly detecting target objects in the environments where reflection exists. The suggested algorithm first captures scenes with a stereo camera and extracts the line and corner features representing the target objects. This method then eliminates the reflected features among the extracted ones using a homographic transform. Subsequently, the method robustly detects the target objects by clustering only real features. The experimental results showed that the suggested algorithm effectively detects the target objects in reflection environments rather than existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.272-287
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• 2016

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.7-14
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.41-47
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.363-363
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• 2000

The conventional algorithms which identify and follow a moving target using a camera located at a fixed position are not appropriate for applying to the cases o( using mobile robots, due to their long processing time. This paper proposes a new tracking algorithm based on the sensing system which uses a line light with a single camera. The algorithm categirizes the motion patterns of a pair of mobile objects into parallel, branching, and merging motion, to decide of which objects the trajectories should be calculated to follow the reference object. Kalman Filter is used to estimate the trajectories of selected objects. The proposed algorithm has shown in the experiments that the mobile robot does not miss the target in most cases.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.4
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• pp.21-25
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• 1982

This paper describes a new algorithm for the extraction of target-like objects. In order to extract target-like objects, a given image is transformed into two-valued image by thresholding, and then a double gate is applied to the two-valued image. With the synthetic-image data, we have shown the good performance of the new algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4807-4813
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• 2012

Since the existing methods of segmenting target objects from various images mainly use 2-dimensional features, they have several constraints due to the shortage of 3-dimensional information. In this paper, we therefore propose a new method of accurately segmenting target objects from three dimensional stereoscopic images using 2D and 3D feature clustering. The suggested method first estimates depth features from stereo images by using a stereo matching technique, which represent the distance between a camera and an object from left and right images. It then eliminates background areas and detects foreground areas, namely, target objects by effectively clustering depth and color features. To verify the performance of the proposed method, we have applied our approach to various stereoscopic images and found that it can accurately detect target objects compared to other existing 2-dimensional methods.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.55 no.3
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• pp.142-148
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• 2006

The resonances that contain the information on the properties of the scattering target can be used for target reconstruction approaches. The inverse scattering theory for the resonances has been applied to the problems of the scattering for a spherical, cylindrical dielectric objects and dielectrically coated conductors, shown reasonable results. Though by using this method the thickness and the dielectric constants of the target can be obtained from a determination of the spacing and of the widths of the scattering resonances, the radius of the target should be given. In this paper, we suggest the improved inverse theory combined with the resonance scattering theory to obtain the radius in addition to the dielectric constant of the target. The applications of this method for scattering problems of electromagnetic waves from cylindrical targets were accomplished, and it shows its validity.

• Physical Therapy Korea
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• v.15 no.3
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• pp.44-52
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• 2008

The purpose of this study was to investigate the effects of different objects and target location of dominant hand on the non-dominant hand movement kinematics in a bimanual reaching task. Fifteen right-handed volunteers were asked to reach from same starting point to the different target point of right and left hand with grasping the objects of different size. Independent variables were 1) three different object types (small mug cup, name pen, and PET bottle), and 2) three different target locations (shorter distance, same distance, and longer distance than the non-dominant hand) of the dominant hand. Dependent variables were movement time (MT), movement distance (MD), movement mean velocity ($MV_{mean}$), and movement peak velocity ($MV_{peak}$) of the non-dominant hand. Repeated measures two-way analysis of variance (ANOVA) was used to test for differences in the non-dominant hand movement kinematics during bimanual reaching. The results of this study were as follows: 1) MT of the non-dominant hand was increased significantly when traveling with grasping the mug cup and reaching the far target location, and was decreased significantly when traveling with grasping the PET bottle and reaching the near target location of the dominant hand. 2) MD of the non-dominant hand was significantly increased during reaching the far target location, and significantly decreased during reaching the near target location with dominant hand. 3) $MV_{mean}$ of the non-dominant hand was increased significantly when traveling with grasping the PET bottle, and was decreased significantly when traveling with grasping the mug cup of the dominant hand. Therefore, it can be concluded that the changes of the ipsilateral hand movement have influence on coupling of the contralateral hand movement in bimanual reaching.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.388-394
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• 2014

In this paper, we propose a three-dimensional (3D) image correlator by use of computational integral imaging reconstruction based on the modified convolution property of periodic functions (CPPF) for recognition of partially occluded objects. In the proposed correlator, elemental images of the reference and target objects are picked up by a lenslet array, and subsequently are transformed to a sub-image array which contains different perspectives according to the viewing direction. The modified version of the CPPF is applied to the sub-images. This enables us to produce the plane sub-image arrays without the magnification and superimposition processes used in the conventional methods. With the modified CPPF and the sub-image arrays, we reconstruct the reference and target plane sub-image arrays according to the reconstruction plane. 3D object recognition is performed through cross-correlations between the reference and the target plane sub-image arrays. To show the feasibility of the proposed method, some preliminary experiments on the target objects are carried out and the results are presented. Experimental results reveal that the use of plane sub-image arrays enables us to improve the correlation performance, compared to the conventional method using the computational integral imaging reconstruction algorithm.