• Journal of Digital Contents Society
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• v.9 no.2
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• pp.363-369
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• 2008

In this paper, we have proposed 3D shape reconstruction using superquadrics and single z-buffer Constructive Solid Geometry (CSG) rendering algorithm. Superquadrics can obtain various 3D model using 11 parameters and both superquadrics and deformed-superquadrics play a role of primitives which are consisted of CSG tree. In addition, we defined some effective equations using z-buffer algorithm and stencil buffer for synthesizing 3D model. Using this proposed algorithm, we need not to consider the coordinate of each 3D model because we simply compare the depth value of 3D model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.355-362
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• 2013

A hardware implementation of phase calculator for extracting 3D depth image from TOF(Time-Of-Flight) sensor is described. The designed phase calculator, which adopts a pipelined architecture to improve throughput, performs arctangent operation using vectoring mode of CORDIC algorithm. Fixed-point MATLAB modeling and simulations are carried out to determine the optimized bit-widths and number of iteration. The designed phase calculator is verified by FPGA-in-the-loop verification using MATLAB/Simulink, and synthesized with a TSMC 0.18-${\mu}m$ CMOS cell library. It has 16,000 gates and the estimated throughput is about 9.6 Gbps at 200Mhz@1.8V.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.182-189
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• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3924-3940
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• 2020

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1099-1102
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• 2009

Conventional stereoscopic (3D) displays using binocular parallax generate unnatural conflicts between convergence and accommodation. Those conflicts can affect the ability to fuse binocular images and may cause visual fatigue. This study examined time-series changes in visual fatigue and depth sensation while viewing stereoscopic images with changing parallax. We examined the physiological changes, including the subjective symptoms of visual fatigue, when viewing five parallax conditions. The time-series results suggest that 2D and 3D images produce significantly different types of visual fatigue over the range of binocular disparity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1155-1163
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• 2011

In this paper, we propose a new pre-processing algorithm applied to depth map to improve the coding efficiency. Now, 3DV/FTV group in the MPEG is working for standard of 3DVC(3D video coding), but compression method for depth map images are not confirmed yet. In the proposed algorithm, after dividing the histogram distribution of a given depth map by EM clustering method based on GMM, we classify the depth map into several layered images. Then, we apply different mean shift filter to each classified image according to the existence of background or foreground in it. In other words, we try to maximize the coding efficiency while keeping the boundary of each object and taking average operation toward inner field of the boundary. The experiments are performed with many test images and the results show that the proposed algorithm achieves bits reduction of 19% ~ 20% and computation time is also reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.466-468
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• 2022

3D reconstruction is important issue in many applications such as Augmented Reality (AR), eXtended Reality (XR), and Metaverse. For 3D reconstruction, depth map can be acquired by stereo camera and time-of-flight (ToF) sensor. We used both sensors complementarily to improve the accuracy of 3D information of the data. First, we applied general multi-camera calibration technique which uses both color and depth information. Next, the depth map of the two sensors are fused by 3D registration and reprojection approach. The fused data is compared with the ground truth data which is reconstructed using RTC360 sensor. We used Geomagic Wrap to analysis the average RMSE of the two data. The proposed procedure was implemented and tested with real-world data.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.707-709
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• 2008

We proposed the method to present corrected depth cue to an observer by stereoscopic display. It was performed in sequence that designing the displayed space having a constant interval of depth and then defining the object space which had considered to an environment of display and based on computer graphics. Consequently, we had performed a different process of reported existing methods distinctively and taken the result which correctly designed depth cue having linearity whatever various sizes of display would be used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• Journal of information and communication convergence engineering
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• v.13 no.1
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• pp.62-68
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• 2015

In this paper, we propose a watermarking method for protecting the ownership of three-dimensional (3D) content generated from depth and texture images. After selecting the target areas to preserve the watermark by depth-image-based rendering, the reference viewpoint image is moved right and left in the depth map until the maximum viewpoint change is obtained and the overlapped region is generated for marking space. The region is divided into four subparts and scanned. After applying discrete cosine transform, the watermarks are inserted. To extract the watermark, the viewpoint can be changed by referring to the viewpoint image and the corresponding depth image initially, before returning to the original viewpoint. The watermark embedding and extracting algorithm are based on quantization. The watermarked image is attacked by the methods of JPEG compression, blurring, sharpening, and salt-pepper noise.