• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1671-1680
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• 2014

There are always much concern about the leakage of radiation materials in the event of dismantle or unexpected accident of nuclear power plant. In order to remove the leakage of radiation materials, appropriate dispersion detection techniques for radiation materials are necessary. However, because direct handling of radiation materials is highly restricted and risky, developing radiation-related techniques needs computer simulation in advance to evaluate the feasibility. In this paper, we propose a radiation imaging technique which can acquire 3D dispersion information of radiation materials and tested by simulation. Using two virtual 1D radiation sensors, we obtain stereo radiation images and acquire the 3D depth to virtual radiation materials using stereo disparity. For point and plane type virtual radiation materials, the possibility of the acquisition of stereo radiation image and 3D information are simulated.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.59-66
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• 2004

In this paper, a fast 3D model extraction algorithm with an enhanced PBIL of preserving depth consistency is proposed for the extraction of 3D depth information from 2D images. Evolutionary computation algorithms are efficient search methods based on natural selection and population genetics. 2D disparity maps acquired by conventional matching algorithms do not match well with the original image profile in disparity edge regions because of the loss of fine and precise information in the regions. Therefore, in order to decrease the imprecision of disparity values and increase the quality of matching, a compact genetic algorithm is adapted for matching environments, and the adaptive window, which is controlled by the complexity of neighbor disparities in an abrupt disparity point is used. As the result, the proposed algorithm showed more correct and precise disparities were obtained than those by conventional matching methods with relaxation scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1872-1879
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• 2001

As multiview video applications become more popular, correspondence problem for stereo image matching plays an important role in expanding view points. Thus, we propose an efficient dense disparity estimation algorithm considering features of each image pair of multiview image sets. Main concepts of the proposed algorithm are based on the region-dividing-bidirectional-pixel-matching method. This algorithm makes matching process efficient and keeps the reliability of the estimated disparities. Other improvement have obtained by proposed cost function, matching window expanding technique, disparity regularization, and disparity assignment in ambiguous region. These techniques make disparities more stable by removing false disparities and ambiguous regions. The estimated disparities are used to synthesize intermediate views of multiview images. Computer simulation demonstrates the excellence of the proposed algorithm in both subjective and objective evaluations. In addition, processing time is reduced as well.

• Smart Media Journal
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• v.2 no.4
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• pp.27-33
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• 2013

Multi-view video system provides both realistic 3D feelings and free-view navigation. But it is hard to transmit too huge data, so we send only two or three view images and generate intermediate view image using depth information. In this paper, we propose high quality multi-view image generation method from stereoscopic images. Since the stereo matching method does not provide accurate disparity values for all the pixels, especially at the occlusion area, we propose an occlusion handling method using the background pixels at first. We also apply a joint bilateral filtering to enhance the disparity map at the object boundary since it can affect the quality of synthesized images significantly. Finally, we can generate virtual view images at intermediate view positions using confidence map to reduce bad pixel and hole's error. Experimental results show the proposed method performs better than the conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.9-16
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• 2005

In this paper, we compare face recognition rate by distance change using Principal Component Analysis algorithm being input left and right image in stereo image. Change to YCbCr color space from RGB color space in proposed method and face region does detection. Also, after acquire distance using stereo image extracted face image's extension and reduce do extract robust face region, experimented recognition rate by using PCA algorithm. Could get face recognition rate of 98.61%(30cm), 98.91%(50cm), 99.05%(100cm), 99.90%(120cm), 97.31%(150cm) and 96.71%(200cm) by average recognition result of acquired face image. Therefore, method that is proposed through an experiment showed that can get high recognition rate if apply scale up or reduction according to distance.

• 전자공학회논문지 IE
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• v.46 no.1
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• pp.23-30
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• 2009

In this paper, the intelligent vision system for an effective and intelligent path-planning of an industrial AGV system based on stereo camera system is proposed. The depth information and disparity map are detected in the inputting images of a parallel stereo camera. The distance between the industrial AGV system and the obstacle detected and the 2D Path coordinates obtained from the location coordinates, and then the relative distance between the obstacle and the other objects obtained from them. The industrial AGV system move automatically by effective and intelligent path-planning using the obtained 2D path coordinates. From some experiments on AGV system driving with the stereo images, it is analyzed that error ratio between the calculated and measured values of the distance between the objects is found to be very low value of 2.5% on average, respectably.

• The Journal of the Korea Contents Association
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• v.16 no.1
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• pp.515-520
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• 2016

Volumetric image system has many applications recently in education, 3D movie, medical images but these applications have several problems that need to be overcome. Volumetric display may process a amount of visual data and design the high efficient vision system for realtime display. In case of stereo system for volumetric display motion vectors, disparity vectors from the stereoscopic sequences and residual images with the reference images has been transmitted, and the stereoscopic sequences have been reconstructed at the receiver for volumetric display. So central issue for the design of efficient volumetric image system lies in selecting an appropriate stereo matching and robust vision system. In this paper, we proposed high efficient vision system, which design vision stage with rotating and moving horizontally, and match the successive stereo image efficiently. In experimental results with volumetric image system, the proposed method represents high efficiency with minimizing error and low computational load for volumetric display.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.53-61
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• 2012

This paper proposes a real-time distance measurement system of high temperature and high speed target using infrared stereo camera. We construct an infrared stereo camera system that measure the difference between target and background temperatures for automatic target measurement. First, the proposed method detects target region based on target motion and intensity variation of local region using difference between target and background temperatures. Second, stereo matching by left and right target information is used to estimate disparity about real-time distance of target. In the proposed method using infrared stereo camera system, we compare distances in three dimension trajectory measuring instrument and in infrared stereo camera measurement. In this experiment from three video data, the result shows an average 9.68% distance error rate. The proposed method is suitable for distance and position measurement of varied targets using infrared stereo system.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.2
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• pp.121-128
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• 2013

There have been various research efforts for pedestrian recognition in embedded imaging systems. However, many suffer from their heavy computational complexities. SVM classification method has been widely used for pedestrian recognition. The reduction of candidate region is crucial for low-complexity scheme. In this paper, We propose a real time HOG based pedestrian detection system on GPU which images are captured by a pair of cameras. To speed up humans on road detection, the proposed method reduces a number of detection windows with disparity-search and near-search algorithm and uses the GPU and the NVIDIA CUDA framework. This method can be achieved speedups of 20% or more compared to the recent GPU implementations. The effectiveness of our algorithm is demonstrated in terms of the processing time and the detection performance.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1003-1013
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• 2016

This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.