• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.193-198
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• 2017

In this study, we propose a three-dimensional (3D) visualization technique of occluded objects using integral imaging with a plenoptic camera. In previous studies, depth map estimation from elemental images was used to remove occlusion. However, the resolution of these depth maps is low. Thus, the occlusion removal accuracy is not efficient. Therefore, we use a plenoptic camera to obtain a high-resolution depth map. Hence, individual depth map for each elemental image can also be generated. Finally, we can regenerate a more accurate depth map for 3D objects with these separate depth maps, allowing us to remove the occlusion layers more efficiently. We perform optical experiments to prove our proposed technique. Moreover, we use MSE and PSNR as a performance metric to evaluate the quality of the reconstructed image. In conclusion, we enhance the visual quality of the reconstructed image after removing the occlusion layers using the plenoptic camera.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.113-121
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• 2020

Human Pose Estimation (HPE) which localizes the human body joints becomes a high potential for high-level applications in the field of computer vision. The main challenges of HPE in real-time are occlusion, illumination change and diversity of pose appearance. The single RGB image is fed into HPE framework in order to reduce the computation cost by using depth-independent device such as a common camera, webcam, or phone cam. However, HPE based on the single RGB is not able to solve the above challenges due to inherent characteristics of color or texture. On the other hand, depth information which is fed into HPE framework and detects the human body parts in 3D coordinates can be usefully used to solve the above challenges. However, the depth information-based HPE requires the depth-dependent device which has space constraint and is cost consuming. Especially, the result of depth information-based HPE is less reliable due to the requirement of pose initialization and less stabilization of frame tracking. Therefore, this paper proposes a new method of HPE which is robust in estimating self-occlusion. There are many human parts which can be occluded by other body parts. However, this paper focuses only on head self-occlusion. The new method is a combination of the RGB image-based HPE framework and the depth information-based HPE framework. We evaluated the performance of the proposed method by COCO Object Keypoint Similarity library. By taking an advantage of RGB image-based HPE method and depth information-based HPE method, our HPE method based on RGB-D achieved the mAP of 0.903 and mAR of 0.938. It proved that our method outperforms the RGB-based HPE and the depth-based HPE.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1003-1004
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• 2008

In this paper, we propose a 3D modeling method using Laser Slit Beam and Stereo Camera. We can get depth information of image by analyzing projected Laser Slit Beam on object. 3D modeling is demanded exquisite merge of 3D data. In our approach, we can get the depth image where the reliability is high. Each reconstructed 3D modeling is combined by the sink information which is acquired by SIFT (Scale Invariant Feature Transform) Algorithm. We perform experiments using indoor images. The results show that the proposed method works well in indoor environments

• Journal of the Korea Computer Industry Society
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• v.9 no.3
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• pp.105-114
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• 2008

We present an eye tracking system using a low cost 3D CMOS imager for 3D displays that ensures a correct auto stereoscopic view of position- dependent stereoscopic 3D images. The tracker is capable of segmenting the foreground objects (viewer) from background objects using their relative distance from the camera. The tracker is a novel 3D CMOS Image Sensor based on Time of Flight (TOF) principle using innovating photon gating techniques. The basic feature incorporates real time depth imaging by capturing the shape of a light-pulse front as it is reflected from a three dimensional object. The basic architecture and main building blocks of a real time depth CMOS pixel are described. For this application, we use a stereoscopic type of display using parallax barrier elements that is described as well.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.26 no.2
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• pp.180-183
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• 1990

The variability of ambient noise with time and water depth is measured in the coastal water of Pusan. The Noise Spectrum levels are relatively high, and have standard deviations amounting to 3 to 4 dB with time and 2 to 3 dB with water depth in the B area of high ship activity. On the other hand, in the A area where shipping is sparse the standard deviations are only 1 to 2 dB with time and water depth respectively. These results show that ship traffic is one of the dominent sources at frequencies greater than 500Hz.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.552-556
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• 2016

Obstacle detection is a key feature in the safe driving control of electric wheelchairs. The suggested obstacle detection algorithm was designed to provide obstacle avoidance direction and detect the existence of cliffs. By means of this information, the wheelchair can determine where to steer and whether to stop or go. A 3D depth camera (Microsoft KINECT) is used to scan the 3D point data of the scene, extract information on obstacles, and produce a steering direction for obstacle avoidance. To be specific, ground detection is applied to extract the obstacle candidates from the scanned data and the candidates are projected onto a 2D map. The 2D map provides discretized information of the extracted obstacles to decide on the avoidance direction (left or right) of the wheelchair. As an additional function, cliff detection is developed. By defining the "cliffband," the ratio of the predefined band area and the detected area within the band area, the cliff detection algorithm can decide if a cliff is in front of the wheelchair. Vehicle tests were carried out by applying the algorithm to the electric wheelchair. Additionally, detailed functions of obstacle detection, such as providing avoidance direction and detecting the existence of cliffs, were demonstrated.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.61-64
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• 2005

The calculated characteristics of groundwater within the Youngsan river basin are : casing depth-17.1m, well depth-74.8m, natural water-2.6m, pumping water-43.9m, yields-391$m^3/D$, transmissivity-16.3$m^3/D/m$, storativity-0.068. As far as hydrogeological units are concerned, in casing depth, weathered granites are deepest followed by gneiss, volcanics, and sediments. In major aquifer development areas, sediments are deepest followed by volcanics, granites and gneiss in more shallow areas, Altogether, the major aquifar development depth of the Youngsan river basin is within the $35{\sim}60m$ range.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4A
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• pp.239-249
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• 2012

Recently, the virtual view generation method using depth data is employed to support the advanced stereoscopic and auto-stereoscopic displays. Although depth data is invisible to user at 3D video rendering, its accuracy is very important since it determines the quality of generated virtual view image. Many works are related to such depth enhancement exploiting a time-of-flight (TOF) camera. In this paper, we propose a fast 3D scene capturing system using one TOF camera at center and two high-resolution cameras at both sides. Since we need two depth data for both color cameras, we obtain two views' depth data from the center using the 3D warping technique. Holes in warped depth maps are filled by referring to the surrounded background depth values. In order to reduce mismatches of object boundaries between the depth and color images, we used the joint bilateral filter on the warped depth data. Finally, using two color images and depth maps, we generated 10 additional intermediate images. To realize fast capturing system, we implemented the proposed system using multi-threading technique. Experimental results show that the proposed capturing system captured two viewpoints' color and depth videos in real-time and generated 10 additional views at 7 fps.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2563-2568
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• 2010

SAII (synthetic aperture integral imaging) is a useful technique to record many multi view images of 3D objects by using a moving camera and to reconstruct 3D depth images from the recorded multiviews. This is largely composed of two processes. A pickup process provides elemental images of 3D objects and a reconstruction process generates 3D depth images computationally. In this paper, a signal model for SAII is presented. We defined the granular noise and analyzed its characteristics. Our signal model revealed that we could reduce the noise in the reconstructed images and increase the computational speed by reducing the shifting distance of a single camera.