• 대한조선학회논문집
• /
• 제55권1호
• /
• pp.83-91
• /
• 2018

In this paper, usefulness of marine radar for water waves measurement in coastal waters is presented. We installed a marine radar to acquire radar images of water wave around light beacon at Jujeon in Ulsan. Also, a series of analysis procedures for obtaining the wave information from the acquired image is described with a schematic diagram. We compared analysis results of radar images with measurement values using wave height gauge at light beacon. In order to improve accuracy of analysis results, detailed water depth information is essential. In conclusion, in case of the use of radar for water waves measurement, it is shown that it is very necessary to increase the accuracy of measurement by consideration of the water depth in the dispersion relation of water waves.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2006년도 춘계학술논문 발표대회 제6권1호
• /
• pp.163-166
• /
• 2006

The purpose of the research is to assess the accuracy of steel bar detector among other nondestructive testing equipment. The result of previous research shows that the average errors of rebar detector are 14.7% for the cover depth, 2.3% for the rebar spacing, and 11% for the rebar diameter. But this experiment was performed at the laboratory and the mortar was used for covering the steel bars instead of concrete. In situ condition can be different from the laboratory's so the outcomes do not correspond with those of laboratory. This research was performed at the buildings to be reconstructed. Nondestructive and destructive testing can be performed side by side since the building if to be destroyed. Steel bar detector was operated on the beam and the column and concrete cover of those members was removed for the actual measurement of rebar depth, spacing, and diameter finally, presumed value can be directly compared with actual data.

• Geomechanics and Engineering
• /
• 제17권6호
• /
• pp.597-606
• /
• 2019

Ultrasonic Time-of-Flight Diffraction (TOFD) techniques are useful methods for non-destructive evaluation of fracture characteristics. This study focuses on the reliability and accuracy of ultrasonic diffraction methods to estimate the depth of rock fractures. The study material includes three different rock types; andesite, basalt and ignimbrite. Four different ultrasonic techniques were performed on these intact rocks. Artificial near-surface fracture depths were created in the laboratory by sawing. The reliability and accuracy of each technique was assessed by comparison of the repeated measurements at different path lengths along the rock surface. The standard error associated with the predictive equations is very small and their reliability and accuracy seem to be high enough to be utilized in estimating the depth of rock fractures. The performances of these techniques were re-evaluated after filling the artificial fractures with another material to simulate natural infills.

• 대한원격탐사학회지
• /
• 제34권4호
• /
• pp.625-638
• /
• 2018

In this paper, we derive i) a function to estimate snow cover fraction (SCF) from a MODIS satellite image that has a wide observational area and short re-visit period and ii) a function to determine snow depth from the estimated SCF map. The SCF equation is important for estimating the snow depth from optical images. The proposed SCF equation is defined using the Gaussian function. We found that the Gaussian function was a better model than the linear equation for explaining the relationship between the normalized difference snow index (NDSI) and the normalized difference vegetation index (NDVI), and SCF. An accuracy test was performed using 38 MODIS images, and the achieved root mean square error (RMSE) was improved by approximately 7.7 % compared to that of the linear equation. After the SCF maps were created using the SCF equation from the MODIS images, a relation function between in-situ snow depth and MODIS-derived SCF was defined. The RMSE of the MODIS-derived snow depth was approximately 3.55 cm when compared to the in-situ data. This is a somewhat large error range in the Republic of Korea, which generally has less than 10 cm of snowfall. Therefore, in this study, we corrected the calculated snow depth using the relationship between the measured and calculated values for each single image unit. The corrected snow depth was finally recorded and had an RMSE of approximately 2.98 cm, which was an improvement. In future, the accuracy of the algorithm can be improved by considering more varied variables at the same time.

• 디지털산업정보학회논문지
• /
• 제12권1호
• /
• pp.27-33
• /
• 2016

In this study we propose a method for 3D landform reconstruction and object modeling method by generating contour lines on the map using a depth sensor which abstracts characteristics of geological layers from the depth map. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust contour and object can be extracted. The algorithm suggested in this paper first abstracts the characteristics of each geological layer from the depth map image and rearranges it into the proper order, then creates contour lines using the Bezier curve. Using the created contour lines, 3D images are reconstructed through rendering by mapping RGB images of the visual camera. Experimental results show that the proposed method using depth sensor can reconstruct contour map and 3D modeling in real-time. The generation of the contours with depth data is more efficient and economical in terms of the quality and accuracy.

• 한국정보디스플레이학회:학술대회논문집
• /
• 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
• /
• pp.1350-1353
• /
• 2009

Conventional depth extraction in integral imaging is based on the disparity information between the elemental images. Since the disparity is measured in pixel unit, however, the extracted depth is discrete, resulting in the quantization error. Moreover, the quantization error grows as the object depth increases, which limits the accuracy of the depth extraction for distant objects. In this paper, we propose a new method for depth extraction in integral imaging using sub-pixel registration information between subimages to obtain linear and accurate depth.

• Journal of Information Processing Systems
• /
• 제18권6호
• /
• pp.794-802
• /
• 2022

The recovery of reasonable depth information from different scenes is a popular topic in the field of computer vision. For generating depth maps with better details, we present an efficacious monocular depth prediction framework with coordinate attention and feature fusion. Specifically, the proposed framework contains attention, multi-scale and feature fusion modules. The attention module improves features based on coordinate attention to enhance the predicted effect, whereas the multi-scale module integrates useful low- and high-level contextual features with higher resolution. Moreover, we developed a feature fusion module to combine the heterogeneous features to generate high-quality depth outputs. We also designed a hybrid loss function that measures prediction errors from the perspective of depth and scale-invariant gradients, which contribute to preserving rich details. We conducted the experiments on public RGBD datasets, and the evaluation results show that the proposed scheme can considerably enhance the accuracy of depth prediction, achieving 0.051 for log10 and 0.992 for δ<1.25³ on the NYUv2 dataset.

• 한국멀티미디어학회논문지
• /
• 제21권2호
• /
• pp.138-146
• /
• 2018

In this paper, we describe an algorithm for extracting depth information from a single image based on CNN. When acquiring three-dimensional information from a single two-dimensional image using a deep-learning technique, it is difficult to accurately predict the edge portion of the depth image because it is a part where the depth changes abruptly. in this paper, we introduce the concept of pixel-wise confidence to take advantage of these characteristics. We propose an algorithm that estimates depth information from a highly reliable flat part and propagates it to the edge part to improve the accuracy of depth estimation.

• 한국멀티미디어학회논문지
• /
• 제19권4호
• /
• pp.681-688
• /
• 2016

In the intelligent video surveillance, recognizing the moving objects is important issue. However, the conventional moving object recognition methods have some problems, that is, the influence of light, the distinguishing between similar colors, and so on. The recognition methods for the moving objects using depth information have been also studied, but these methods have limit of accuracy because the depth camera cannot measure the depth value accurately. In this paper, we propose a recognition method for the moving objects by using both the depth and the color information. The depth information is used for extracting areas of moving object and then the color information for correcting the extracted areas. Through tests with typical videos including moving objects, we confirmed that the proposed method could extract areas of moving objects more accurately than a method using only one of two information. The proposed method can be not only used in CCTV field, but also used in other fields of recognizing moving objects.

• 반도체디스플레이기술학회지
• /
• 제21권4호
• /
• pp.77-80
• /
• 2022

Automatic Vision Inspection (AVI) systems automatically detect defect features and measure their sizes via camera vision. It has been populated because of the accuracy and consistency in terms of QC (Quality Control) of inspection processes. Also, it is important to predict the performance of an AVI to meet customer's specification in advance. AVI are usually suffered from false negative and positives. It can be overcome by providing extra information such as 3D depth information. Stereo vision processing has been popular for depth extraction of the 3D images from 2D images. However, stereo vision methods usually take long time to process. In this paper, retro optical system using reflectors is proposed and experimented to overcome the problem. The optical system extracts the depth without special SW processes. The vision sensor and optical components such as illumination and depth detecting module are integrated as a unit. The depth information can be extracted on real-time basis and utilized and can improve the performance of an AVI system.