• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.435-449
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• 2023

The state-of-the-art video-based point cloud compression(V-PCC) has a high efficiency of compressing 3D point cloud by projecting points onto 2D images. These images are then padded and compressed by High-Efficiency Video Coding(HEVC). Pixels in padded 2D images are classified into three groups including origin pixels, padded pixels and unoccupied pixels. Origin pixels are generated from projection of 3D point cloud. Padded pixels and unoccupied pixels are generated by copying values from origin pixels during image padding. For padded pixels, they are reconstructed to 3D space during geometry reconstruction as well as origin pixels. For unoccupied pixels, they are not reconstructed. The rate distortion optimization(RDO) used in HEVC is mainly aimed at keeping the balance between video distortion and video bitrates. However, traditional RDO is unreliable for padded pixels and unoccupied pixels, which leads to significant waste of bits in geometry reconstruction. In this paper, we propose a new RDO scheme which takes 3D-Distortion into account instead of traditional video distortion for padded pixels and unoccupied pixels. Firstly, these pixels are classified based on the occupancy map. Secondly, different strategies are applied to these pixels to calculate their 3D-Distortions. Finally, the obtained 3D-Distortions replace the sum square error(SSE) during the full RDO process in intra prediction and inter prediction. The proposed method is applied to geometry frames. Experimental results show that the proposed algorithm achieves an average of 31.41% and 6.14% bitrate saving for D1 metric in Random Access setting and All Intra setting on geometry videos compared with V-PCC anchor.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.464-472
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• 2005

Purpose: SPECT using a fan-beam collimator improves spatial resolution and sensitivity. For the reconstruction from fan-beam projections, it is necessary to implement direct fan-beam reconstruction methods without transforming the data into the parallel geometry. In this study, various fan-beam reconstruction algorithms were implemented and their performances were compared. Materials and Methods: The projector for fan-beam SPECT was implemented using a ray-tracing method. The direct reconstruction algorithms implemented for fan-beam projection data were FBP (filtered backprojection), EM (expectation maximization), OS-EM (ordered subsets EM) and MAP-EM OSL (maximum a posteriori EM using the one-step late method) with membrane and thin-plate models as priors. For comparison, the fan-beam protection data were also rebinned into the parallel data using various interpolation methods, such as the nearest neighbor, bilinear and bicubic interpolations, and reconstructed using the conventional EM algorithm for parallel data. Noiseless and noisy projection data from the digital Hoffman brain and Shepp/Logan phantoms were reconstructed using the above algorithms. The reconstructed images were compared in terms of a percent error metric. Results: for the fan-beam data with Poisson noise, the MAP-EM OSL algorithm with the thin-plate prior showed the best result in both percent error and stability. Bilinear interpolation was the most effective method for rebinning from the fan-beam to parallel geometry when the accuracy and computation load were considered. Direct fan-beam EM reconstructions were more accurate than the standard EM reconstructions obtained from rebinned parallel data. Conclusion: Direct fan-beam reconstruction algorithms were implemented, which provided significantly improved reconstructions.

• Progress in Medical Physics
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• v.21 no.3
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• pp.281-290
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• 2010

Cone-beam digital tomosynthesis (CBDT) has greatly been paid attention in the image-guided radiation therapy because of its attractive advantages such as low patient dose and less motion artifact. Image quality of tomograms is, however, dependent on the imaging conditions such as the scan angle (${\beta}_{scan}$) and the number of projection views. In this paper, we describe the principle of CBDT based on filtered-backprojection technique and investigate the optimization of imaging conditions. As a system performance, we have defined the figure-of-merit with a combination of signal difference-to-noise ratio, artifact spread function and floating-point operations which determine the computational load of image reconstruction procedures. From the measurements of disc phantom, which mimics an impulse signal and thus their analyses, it is concluded that the image quality of tomograms obtained from CBDT is improved as the scan angle is wider than 60 degrees with a larger step scan angle (${\Delta}{\beta}$). As a rule of thumb, the system performance is dependent on $\sqrt{{\Delta}{\beta}}{\times}{\beta}^{2.5}_{scan}$. If the exact weighting factors could be assigned to each image-quality metric, we would find the better quantitative imaging conditions.

• Tunnel and Underground Space
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• v.24 no.4
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• pp.255-274
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• 2014

The target of this study is large scale rock slope collapsed by around 7 pm on August, 2012, which is located at ${\bigcirc}{\bigcirc}$ limestone quarries of Gangneung city, Gangwondo. The slope prior to the collapse is formed as the height of about 200 m and the average inclination of $45^{\circ}$. The estimated amount of the collapse is about $1,500,000m^3$ with respect to the slope after the collapse. Geotechnical and field investigations such as boring, geophysical prospecting, surface geological survey, geological lineaments, borehole imaging, metric 3D imaging, experimental and field test, mining work by year, and daily rainfall were performed to find the cause of rock slope failure. Various analyzes using slope mass rating, stereonet projection, limit equilibrium method, continuum and non-continuum model were conducted to check of the stability of the slope. It is expected that the cause of slope failure from the results of various analysis and survey is due to the combined factors such as topography, rainfall, rock type and quality, discontinuities, geo-structural characteristics as the limestone cavity and fault zones, but the failure of slope in case of the analysis without the limestone cavity is not occurred. Safe factor of 0.66 was obtained from continuum analysis of the slope considering the limestone cavity, so the ultimate causes of slope failure is considered to be due to the influence of limestone cavity developed along fault zone.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.22-30
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• 2009

Purpose : Patient motion during magnetic resonance (MR) imaging is one of the major problems due to its long scan time. Entropy based post-processing motion correction techniques have been shown to correct motion artifact effectively. One of main limitations of these techniques however is its long processing time. In this study, we propose several methods to reduce this long processing time effectively. Materials and Methods : To reduce the long processing time, we used the separability property of two dimensional Fourier transform (2-D FT). Also, a computationally light metric (sum of all image pixel intensity) was used instead of the entropy criterion. Finally, partial Fourier reconstruction, in particular the projection onto convex set (POCS) method, was combined thereby reducing the size of the data which should be processed and corrected. Results : Time savings of each proposed method are presented with different data size of brain images. In vivo data were processed using the proposed method and showed similar image quality. The total processing time was reduced to 15% in two dimensional images and 30% in the three dimensional images. Conclusion : The proposed methods can be useful in reducing image motion artifacts when only post-processing motion correction algorithms are available. The proposed methods can also be combined with parallel imaging technique to further reduce the processing times.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.