• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.123-130
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• 1997

In this paper, we present an original stereo vision system which comprises two process: 1. An image segmentation algorithm based on new concept called declivity and using automatic thresholds. 2. A new stereo matching algorithm based on an optimal path search. This path is obtained by dynamic programming method which uses the threshold values calculated during the segmentation process. At present, a complete depth map of indoor scene only needs about 3 s on a Sun workstation IPX, and this time will be reduced to a few tenth of second on a specialised architecture based on several DSPs which is currently under consideration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.302-320
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• 2017

In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.119-128
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• 2014

In this paper, a fast human detection method using adaptive UV-histogram and template matching is proposed. The proposed method improves the detection rate in the scene of complex environment. The method firstly generates U-histogram to extract human candidates and adaptively generates V-histogram for each labled U-histogram, thus it could extract humans correctly, which was impossible in the previous method. The method tries to match the human candidates with the adaptively sized omega shape template to the focal length and distance in order to improve the detection accuracy. It also detects false positives by rematching the template with accumulated foreground images and hence is robust to the occlusion. Experimental results showed that the proposed method has superior performance to the Bae's method in the complex environment with about 15% improvement in precision and 80% in recall and has 20 times faster processing time than Xia's method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.4
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• pp.367-376
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• 2006

This study determines the optimum polynomial of exterior orientation parameters(EOPs) as a function of line number of linear array scanner. To estimate priori EOPs, meta data of IKONOS scene and ground control points are used. We select a first order polynomial and a constant for position elements modeling and rotation elements modeling. Positioning accuracy of the determined EOPs is compared with that of RPCs bias-corrected by the least squares adjustment. There is almost no difference between accuracies of the two methods. To obtain digital elevation model(DEM), matching line is established by the EOPs. The DEM is compared with DEM generated by ERDAS IMAGINE software, which utilizes the bias-corrected RPCs. Height differences of DEMs by the two methods are ranged within a allowable standard deviation. The produced DEM, therefore, shows accuracy similar to the verified method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.145-148
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• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.5
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• pp.708-716
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• 2017

We deal with the target selection in seeker-detection image through network, using the detection information from aerial vehicle and the target information from surveillance and reconnaissance system. Especially, we constrain the sea battle environment, where it is difficult to perform scene-matching rather than land. In this paper, we suggest the target selection algorithm based on the confidence estimation with respect to distance and size. In detail, we propose the generation method of reference point for distance evaluation, and we investigate the effect of pixel margin and target course for size evaluation. Finally, the proposed algorithm is simulated and analyzed through several scenarios.

• Journal of Sensor Science and Technology
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• v.7 no.6
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• pp.394-400
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• 1998

In this paper, we present a robust and fast method that enables real-time computing of axial motion component of different points of a scene from a stereo images sequence. The aim of our method is to establish axial motion maps by computing a range of disparity maps. We propose a solution in two steps. In the first step we estimate motion with a low level computing for an image point by a detection estimation-structure. In the second step, we use the neighbourhood information of the image point with morphology operation. The motion maps are established with a constant computation time without spatio-temporal matching.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.630-635
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• 2007

In this paper, we propose a system for multiple people tracking using fragment based histogram matching. Appearance model is based on IHLS color histogram which can be calculated efficiently using integral histogram representation. Since histograms will loss all spatial information, we define a fragment based region representation which retain spatial information, robust against occlusion and scale issue by using disparity information. Multiple people labeling is maintained by creating online appearance representation for each people detected in scene and calculating fragment vote map. Initialization is performed automatically from background segmentation step.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.3-100
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• 2002

This paper presents the method for detection and tracking of multiple humans robustly in mobile platform. The perception of human is performed in real time through the processing of images acquired from a moving stereo vision system. We performed multi-cue integration such as human shape, skin color and depth information to detect and track each human in moving background scene. Human shape is measured by edge-based template matching on distance transformed image. Improving robustness for human detection, we apply the human face skin color in HSV color space. And we could increase the accuracy and the robustness in both detection and tracking by applying random sampling stochastic estimati...

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.446-449
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• 2009

In this paper, we present a novel method for reconstructing a super-resolution image using multi-view low-resolution images captured for depth varying scene without requiring complex analysis such as depth estimation and feature matching. The proposed method is based on the iterative back projection technique that is extended to the 3D volume domain (i.e., space + depth), unlike the conventional superresolution methods that handle only 2D translation among captured images.