• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3217-3238
• /
• 2018

In this paper, a depth image up-sampling method is put forward by using pixel classifying and jointed bilateral filtering. By analyzing the edge maps originated from the high-resolution color image and low-resolution depth map respectively, pixels in up-sampled depth maps can be classified into four categories: edge points, edge-neighbor points, texture points and smooth points. First, joint bilateral up-sampling (JBU) method is used to generate an initial up-sampling depth image. Then, for each pixel category, different refinement methods are employed to modify the initial up-sampling depth image. Experimental results show that the proposed algorithm can reduce the blurring artifact with lower bad pixel rate (BPR).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2730-2747
• /
• 2016

Due to the limitation of the bandwidth resource and capture resolution of depth cameras, low resolution depth maps should be up-sampled to high resolution so that they can correspond to their texture images. In this paper, a novel depth map up-sampling algorithm is proposed by exploiting the fractal internal self-referential feature. Fractal parameters which are extracted from a depth map, describe the internal self-referential feature of the depth map, do not introduce inherent scale and just retain the relational information of the depth map, i.e., fractal transforms provide a resolution-independent description for depth maps and could up-sample depth maps to an arbitrary high resolution. Then, an enhancement method is also proposed to further improve the performance of the up-sampled depth map. The experimental results demonstrate that better quality of synthesized views is achieved both on objective and subjective performance. Most important of all, arbitrary resolution depth maps can be obtained with the aid of the proposed scheme.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.6
• /
• pp.1175-1184
• /
• 2015

A depth map is an image which contains 3D distance information. Generally, it is difficult to acquire a high resolution (HD), noise-removed, good quality depth map directly from the camera. Therefore, many researches have been focused on acquisition of the high resolution and the good quality depth map by up-sampling and pre/post image processing of the low resolution depth map. However, many researches are lack of effective up-sampling for the edge region which has huge impact on image perceptual-quality. In this paper, we propose an up-sampling method, based on joint bilateral filter, which improves up-sampling of the edge region and visual quality of synthetic images by adopting different weights for the edge parts that is sensitive to human perception characteristics. The proposed method has gains in terms of PSNR and subjective video quality compared to previous researches.

• Smart Media Journal
• /
• v.1 no.3
• /
• pp.22-28
• /
• 2012

As the smart media becomes more popular, the demand for high-quality 3D images and depth maps is increasing. However, performance of the current technologies to acquire depth maps is not sufficient. The depth maps from stereo matching methods have low accuracy in homogeneous regions. The depth maps from depth cameras are noisy and have low-resolution due to technical limitations. In this paper, we introduce the state-of-the-art algorithms for depth map enhancement and up-sampling from conventional methods using only depth maps to the latest algorithms referring to both depth maps and their corresponding color images. We also present depth map enhancement algorithms for hybrid camera systems in detail.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37A no.11
• /
• pp.918-926
• /
• 2012

In 3D video transmission, a depth map being used for depth image based rendering (DIBR) is generally compressed by reducing resolution for coding efficiency. Errors in resolution reduction are recovered by an appropriate up-sampling method after decoding. However, most previous works only focus on up-sampling techniques to reduce errors. In this paper, we propose a novel down-sampling technique of depth map that applies different down-sampling rates on moving objects and background in order to enhance human perceptual quality. Experimental results demonstrate that the proposed scheme provides both higher visual quality and peak signal-to-noise ratio (PSNR). Also, our method is compatible with other up-sampling techniques.

• Economic and Environmental Geology
• /
• v.25 no.2
• /
• pp.145-151
• /
• 1992

In order to study the optimum depth for the soil geochemical exploration in the area which is affected by agricultural activities and waste disposal of metal mine, the soil samples were sampled from the B layer of residual soil and vertical 7 layers up to 250 cm in the rice field and 3 layers up to 90 cm in the ordinary field. They were analyzed for Au, As, Cu, Pb and Zn by AAS, AAS-graphite furnace and ICP. To investigate the proper depth for the soil sampling in the contaminated area, the data were treated statistically by applying correlation coefficient, factor analysis and trend analysis. It is conclude that soil geochemical exploration method could be applied in the farm-land and a little contaminated area. The optimum depth of soil sampling is 60 cm in the ordinary field, and 150~200 cm in the rice field. Soil sampling in the area of a huge mine waste disposal is not recommendable. Plotting of geochemical map with factor scores as a input data shows a clear pattern compared with the map of indicater element such as As or Au. The second or third degree trend surface analysis is effective in inferring the continuity of vein in the area where the outcrop is invisible.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.67-72
• /
• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• Korean Journal of Environmental Agriculture
• /
• v.22 no.1
• /
• pp.70-73
• /
• 2003

Nitrate contamination in the aquatic systems is the primary indicator of poor agricultural management. The influence of sewage sludge application rates (0, 10, 25, 50 and 100 dry Mg/ha) on distribution of nitrate originating from the sewage sludge in soil profiles was investigated. Soil profile monitoring of nitrate was carried out with a Lakeland clay soil in 1997. Irrespectively of the sewage sludge application rates up to 50 dry Mg/ha, the concentration of $NO_3$-N at the 120 cm depth was below 10 mg/kg and the difference due to the amount of sewage sludge application was negligible at this depth. There was virtually no $NO_3$-N below 120 cm depth and this was confirmed by a deep sampling up to 300 cm depth. Most of the nitrate remained in the surface 60 cm of the soil. Below 120 cm depth nitrate concentration was very low because of the denitrification even at high sewage sludge rate of 100 dry Mg/ha. The $NO_3$-N concentrations in the soil fluctuated over the growing season due to plant uptake and denitrification. The risk of groundwater contamination by nitrate from sewage sludge application up to high rate of 100 dry Mg/ha was very low in a wheat grown clay soil with high water table ( < 3 m).

• Proceedings of the Korean Geotechical Society Conference
• /
• 2002.10a
• /
• pp.577-584
• /
• 2002

We have employed two methods to remove slime at the end of the sampler in clay layers. The first method is a sampling process that harnesses low pressure to clean up the ground around the sampler tip. The second method, in consideration of a disturbed layer, involves a technique of inserting the sampler 50 cm deep into the ground before cleaning up the verge of the sampler by using high pressure. Physical and mechanical properties of these two methods have been compared and analyzed to investigate how different sampling methods affect degree of disturbance. The first method shows little disturbance since the unconfined compression test results in quite greater E$\_$50//q$\_$u/ in the first method than in the second method. On the other hand, the consolidation test results in a slightly greater compression index in the second method than in the first method, when their indexes are compared in the same depth. This suggests that the second method demonstrates less disturbance than the first method does. It is assumed that the second method may reduce disturbance slightly, However, we suspect that choosing any of the two methods would not obtain a considerable difference in sampling.

• Journal of Broadcast Engineering
• /
• v.21 no.6
• /
• pp.957-964
• /
• 2016

Acquisition of reliable depth maps is a critical requirement in many applications such as 3D videos and free-viewpoint TV. Depth information can be obtained from the object directly using physical sensors, such as infrared ray (IR) sensors. Recently, Time-of-Flight (ToF) range camera including KINECT depth camera became popular alternatives for dense depth sensing. Although ToF cameras can capture depth information for object in real time, but are noisy and subject to low resolutions. Recently, filter-based depth up-sampling algorithms such as joint bilateral upsampling (JBU) and noise-aware filter for depth up-sampling (NAFDU) have been proposed to get high quality depth information. However, these methods often lead to texture copying in the upsampled depth map. To overcome this limitation, we formulate a convex optimization problem using higher order regularization for depth map upsampling. We decrease the texture copying problem of the upsampled depth map by using edge weighting term that chosen by the edge information. Experimental results have shown that our scheme produced more reliable depth maps compared with previous methods.