• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.7
• /
• pp.2434-2448
• /
• 2014

This research proposed the domain division depth map quantization for multiview intermediate image generation using Depth Image-Based Rendering (DIBR). This technique used per-pixel depth quantization according to the percentage of depth bits assigned in domains of depth range. A comparative experiment was conducted to investigate the potential benefits of the proposed method against the linear depth quantization on DIBR multiview intermediate image generation. The experiment evaluated three quantization methods with computer-generated 3D scenes, which consisted of various scene complexities and backgrounds, under varying the depth resolution. The results showed that the proposed domain division depth quantization method outperformed the linear method on the 7- bit or lower depth map, especially in the scene with the large object.

• Bulletin of the Korean Mathematical Society
• /
• v.61 no.1
• /
• pp.147-160
• /
• 2024

In this paper, we define two new classes of monomial ideals I_𝑙,d and J_k,d. When d ≥ 2k + 1 and 𝑙 ≤ d - k - 1, we give the exact formulas to compute the depth and Stanley depth of quotient rings S/I^t_𝑙,d for all t ≥ 1. When d = 2k = 2𝑙, we compute the depth and Stanley depth of quotient ring S/I_𝑙,d. When d ≥ 2k, we also compute the depth and Stanley depth of quotient ring S/J_k,d.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.11a
• /
• pp.252-255
• /
• 2020

Unsupervised deep learning methods have shown impressive results for the challenging monocular depth estimation task, a field of study that has gained attention in recent years. A common approach for this task is to train a deep convolutional neural network (DCNN) via an image synthesis sub-task, where additional views are utilized during training to minimize a photometric reconstruction error. Previous unsupervised depth estimation networks are trained within a fixed depth estimation range, irrespective of its possible range for a given image, leading to suboptimal estimates. To overcome this suboptimal limitation, we first propose an unsupervised adaptive depth estimation method guided by minimum and maximum (min-max) depth priors for a given input image. The incorporation of min-max depth priors can drastically reduce the depth estimation complexity and produce depth estimates with higher accuracy. Moreover, we propose a novel network architecture for adaptive depth estimation, called the AdaMM-DepthNet, which adopts the min-max depth estimation in its front side. Intensive experimental results demonstrate that the adaptive depth estimation can significantly boost up the accuracy with a fewer number of parameters over the conventional approaches with a fixed minimum and maximum depth range.

• IEIE Transactions on Smart Processing and Computing
• /
• v.6 no.3
• /
• pp.175-182
• /
• 2017

In this paper, we present a multi-depth generation method using a time-of-flight (ToF) fusion camera system. Multi-view color cameras in the parallel type and ToF depth sensors are used for 3D scene capturing. Although each ToF depth sensor can measure the depth information of the scene in real-time, it has several problems to overcome. Therefore, after we capture low-resolution depth images by ToF depth sensors, we perform a post-processing to solve the problems. Then, the depth information of the depth sensor is warped to color image positions and used as initial disparity values. In addition, the warped depth data is used to generate a depth-discontinuity map for efficient stereo matching. By applying the stereo matching using belief propagation with the depth-discontinuity map and the initial disparity information, we have obtained more accurate and stable multi-view disparity maps in reduced time.

• Journal of The Korean Digital Architecture Interior Association
• /
• v.3 no.1
• /
• pp.1-8
• /
• 2003

Perceiving the depth of space in the spatial organization of architecture is perceiving spaces as well three dimensions as the fourth dimensions -perceive the time-. Physical depth in architectural space differs from perceptional depth in aspects of not only dimension but also perceptional effects. In this study, the perceptional depth is defined as visual depth and physical depth is depth of space. These purposes of this study are classifying the perceptional effects of visual depth -visual access, sense of variety, dynamic and cubic effect... - and the methods of spatial composition which causes visual depth in architectural space.

• Journal of the Korean Vacuum Society
• /
• v.12 no.3
• /
• pp.162-167
• /
• 2003

To find the concentration according to the depth-direction of ions implanted in the sample, with sputtering of the sample surface, one needs the depth profiling of ion implanted in the sample. On measuring of depth profiling, the sputtering rate to affect depth direction, is calculated by SRIM simulation. When ion is implanted in the sample, the atomic density of the sample rises up a little, and it alters sputtering yield. This alteration then causes differences of sputtering rate to affect depth-direction, on measuring of depth profiling. With the usage of SRIM Monte Carlo simulation code, one calculates sputtering rate, with sputtering yield by the alteration of atomic density of the sample through ion implantation. As a result, it goes to prove that its difference affects depth distribution, on measuring of depth profiling.

• Journal of Broadcast Engineering
• /
• v.9 no.3
• /
• pp.185-195
• /
• 2004

This paper presents a multi-depth map fusion method for the 3D scene reconstruction. It fuses depth maps obtained from the stereo matching technique and the depth camera. Traditional stereo matching techniques that estimate disparities between two images often produce inaccurate depth map because of occlusion and homogeneous area. Depth map obtained from the depth camera is globally accurate but noisy and provide a limited depth range. In order to get better depth estimates than these two conventional techniques, we propose a depth map fusion method that fuses the multi-depth maps from stereo matching and the depth camera. We first obtain two depth maps generated from the stereo matching of 3-view images. Moreover, a depth map is obtained from the depth camera for the center-view image. After preprocessing each depth map, we select a depth value for each pixel among them. Simulation results showed a few improvements in some background legions by proposed fusion technique.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.46 no.4
• /
• pp.376-391
• /
• 2010

To investigate seasonal variation and species composition by depth layers in the deep sea ecosystem of the East Sea of Korea, bottom trawl survey was conducted at 4 depth layers during spring and autumn from 2007 to 2009. A total of 47 species were collected and were composed of 23 fish species, 9 crustacea, 6 cephalopoda and 9 gastropoda. The main dominant species at each depth layers were Chionoecetes opilio in 300m, Berryteuthis magister in 500m, Chionoecetes japonicus in 700m and 900m. In spring, richness indices (R) showed low value of 2.01 in 500m depth, and high value of 2.16 in 300m depth. Diversity indices (H') showed low value of 1.53 in 300m depth, and high value of 2.09 in 700m depth. Dominance indices (D) showed low value of 0.15 in 700m depth, and high value of 0.31 in 300m depth. In Autumn, richness indices showed low value of 1.48 in 900m depth, and high value of 2.69 in 300m depth. Diversity' indices (H') showed low value of 1.13 in 300m depth, and high value of 2.23 in 700m depth. Dominance indices (D) showed low value of 0.14 in 700m depth, and high value of 0.54 in 300m depth. In spring, similarity analysis in each depth layers showed the difference between 900m and othe depth layer, on the contrary 500m and 700m showed the similarity. In autumn, similarity analyssis in each depth layers showed the difference between 700m and other depth layers, on the contrary 300m and 500m showed the similarity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.8
• /
• pp.2068-2082
• /
• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.11
• /
• pp.2175-2190
• /
• 2011

This paper presents the effects of depth map quantization for multiview intermediate image generation using depth image-based rendering (DIBR). DIBR synthesizes multiple virtual views of a 3D scene from a 2D image and its associated depth map. However, it needs precise depth information in order to generate reliable and accurate intermediate view images for use in multiview 3D display systems. Previous work has extensively studied the pre-processing of the depth map, but little is known about depth map quantization. In this paper, we conduct an experiment to estimate the depth map quantization that affords acceptable image quality to generate DIBR-based multiview intermediate images. The experiment uses computer-generated 3D scenes, in which the multiview images captured directly from the scene are compared to the multiview intermediate images constructed by DIBR with a number of quantized depth maps. The results showed that there was no significant effect on depth map quantization from 16-bit to 7-bit (and more specifically 96-scale) on DIBR. Hence, a depth map above 7-bit is needed to maintain sufficient image quality for a DIBR-based multiview 3D system.