• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.209-218
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• 2009

3D backprojection is a kind of reconstruction algorithm to generate volume data consisting of tomographic images, which provides spatial information of the original 3D data from hundreds of 2D projections. The computational time of backprojection increases in proportion to the size of volume data and the number of projection images since the value of every voxel in volume data is calculated by considering corresponding pixels from hundreds of projections. For the reduction of computational time, fast GPU based 3D backprojection methods have been studied recently and the performance of them has been improved significantly. This paper presents two multiple GPU based methods to maximize the parallelism of GPU and compares the efficiencies of two methods by considering both the number of projections and the size of volume data. The first method is to generate partial volume data independently for all projections after allocating a half size of volume data on each GPU. The second method is to acquire the entire volume data by merging the incomplete volume data of each GPU on CPU. The in-complete volume data is generated using the half size of projections after allocating the full size of volume data on each GPU. In experimental results, the first method performed better than the second method when the entire volume data can be allocated on GPU. Otherwise, the second method was efficient than the first one.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.339-346
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• 2000

Medical image is analyzed to get an anatomical information for diagnostics. Segmentation must be preceded to recognize and determine the lesion more accurately. In this paper, we propose automatic segmentation algorithm for MR brain images using T1-weighted, T2-weighted and PD images complementarily. The proposed segmentation algorithm is first, extracts cerebrum images from 3 input images using cerebrum mask which is made from PD image. And next, find 3D clusters corresponded to cerebrum tissues using scale filtering and 3D clustering in 3D space which is consisted of T1, T2, and PD axis. Cerebrum images are segmented using FCM algorithm with its initial centroid as the 3D cluster's centroid. The proposed algorithm improved segmentation results using accurate cluster centroid as initial value of FCM algorithm and also can get better segmentation results using multi spectral analysis than single spectral analysis.

• Economic and Environmental Geology
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• v.41 no.6
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• pp.747-761
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• 2008

In order to experiment the acoustic remote classification of seabed sediment, we achieved ground-truth data(i.e. video and grab samples, etc.) and developed post-processing for automatic classification procedure on the basis of 300 kHz MultiBeam Echo Sounder(MBES) backscattering data, which was acquired using KONGBERG Simrad EM3000 at Sock-Cho Port, East Sea of South Korea. Sonar signal and its classification performance were identified with geo-referenced video imagery with the aid of GIS (Geographic Information System). The depth range of research site was from 5 m to 22.7 m, and the backscattering amplitude showed from -36dB to -15dB. The mean grain sizes of sediment from equi-distanced sampling site(50 m interval) varied from 2.86$(\phi)$ to 0.88(\phi). To acquire the main feature for the seabed classification from backscattering amplitude of MBES, we evaluated the correlation factors between the backscattering amplitude and properties of sediment samples. The performance of seabed remote classification proposed was evaluated with comparing the correlation of human expert segmentation to automatic algorithm results. The cross-model perception error ratio on automatic classification algorithm shows 8.95% at rocky bottoms, and 2.06% at the area representing low mean grain size.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9B
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• pp.1126-1142
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• 2011

This paper addresses the stereoscopic 3D broadcasting technology that delivers the 3DTV contents through the digital cable networks. In order to convey the 3D contents via DCA TV network, specifications of 3D video format, compression, multiplexing, signalling and transport are to be developed. Since 3D has some constraints unlike 2D, it is required to be well designed by considering the capacity of the additional view and the backward/forward compatibility. This paper goes with the latest trends of 3D standard, requirements and service scenarios and then covers the 3D format, compression, multiplexing and signaling, service information and transport/reception technologies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.607-616
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• 2014

Face recognition is a big challenge in surveillance system since different rotation angles of the face make the difficulty to recognize the face of the same person. This paper proposes a novel method to recognize face with different head poses by using 3D information of the face. Firstly, head pose estimation (estimation of different head pose angles) is accomplished by the POSIT algorithm. Then, 3D face image data is constructed by using head pose estimation. After that, 2D image and the constructed 3D face matching is performed. Face verification is accomplished by using commercial face recognition SDK. Performance evaluation of the proposed method indicates that the error range of head pose estimation is below 10 degree and the matching rate is about 95%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.70-81
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• 2001

This paper presents a 3D face modeling method using a CCD camera and a projector (LCD projector or Slide projector). The camera faces the human face and the projector casts white stripe patterns on the human face. The 3D shape of the face is extracted from spatial and temporal locations of the white stripe patterns on a series of image frames. The proposed method employs region segmentation and multi-beam techniques for efficient 3D modeling of hair region and faster 3D scanning respectively. In the proposed method, each image is segmented into face, hair, and shadow regions, which are independently processed to obtain the optimum results for each region. The multi-beam method， which uses a number of equally spaced stripe patterns, reduces the total number of image frames and consequently the overall data acquisition time. Light beam calibration is adopted for efficient light plane measurement, which is not influenced by the direction (vertical or horizontal) of the stripe patterns. Experimental results show that the proposed method provides a favorable 3D face modeling results, including the hair region.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.295-312
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• 2017

Digital video content is the most information-intensive and high-value content. Therefore, it is necessary to protect the intellectual property rights of these contents, and this paper also proposes a watermarking method of digital image for this purpose. The proposed method uses the frequency characteristics of 2-Dimensional Discrete Wavelet Transform (2D-DWT) for digital images and digital watermark on global data without using local or specific data of the image for watermark embedding. The method to insert digital watermark data uses a simple Quantization Index Modulation (QIM) and a multiple watermarking method that inserts the same watermark data in multiple. When extracting a watermark, multiple watermarks are extracted and the final watermark data is determined by a simple statistical method. This method is an empirical method for experimentally determining the parameters in the watermark embedding process. The proposed method performs experiments on various images against various attacks and shows the superiority of the proposed method by comparing the performance with the representative existing methods.

• Journal of the Korean Society of Radiology
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• v.10 no.1
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• pp.45-51
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• 2016

The purpose of this study was tried to remove the noise and improve the spatial resolution in the computed tomography (CT) by using anisotropic 2-dimensional (2D) diffusion based filter. We used 4-channel multi-detector CT and american association of physicists in medicine (AAPM) phantom was used for CT performance evaluation to evaluate the image quality. X-ray irradiation conditions for image acquisition was fixed at 120 kVp, 100 mAs and scanned 10 mm axis with ultra-high resolution. The improvement of anisotropic 2D diffusion filtering that we suggested firstly, increase the contrast of the image by using histogram stretching to the original image for 0.4%, and multiplying the individual pixels by 1.2 weight value, and applying the anisotropic diffusion filtering. As a result, we could distinguished five holes until 0.75 mm in the original image but, five holes until 0.40 mm in the image with improved anisotropic diffusion filter. The noise of the original image was 46.0, the noise of the image with improved anisotropic 2D diffusion filter was decreased to 33.5(27.2%). In conclusion improved anisotropic 2D diffusion filter that we proposed could remove the noise of the CT image and improve the spatial resolution.

• Smart Media Journal
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• v.12 no.11
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• pp.48-56
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• 2023

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.83-84
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• 2011

초분광 영상은 기존의 다중분광 영상보다 많은 밴드를 통해 넓은 범위의 파장 영역에 대한 반사율을 담고 있는 고차원 데이터이다. 이와 같은 고차원 데이터를 기존의 R-Tree, X-Tree와 같은 다차원 색인 방법을 사용하게 되면 차원의 저주(Course of Dimensionality)라는 문제가 발생한다. 본 논문에서는 차원의 저주 문제를 해결하기 위해 피라미드 기법을 사용하여 초분광 영상 라이브러리의 색인을 구축하였다. 파라미드 기법은 D차원의 데이터를 2D차원의 피라미드에 사상하고, B+-트리를 이용하여 1차원적으로 색인하는 방법이다. 실험 결과 스펙트럼 매칭을 위한 영역질의 방법이 후보자 추출 시간, 데이터 접근 빈도 측면에서 순차적 접근 방법보다 좋은 성능을 나타냈다.