• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.438-440
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• 2022

산업 현장에서 인력으로 작업할 수 있는 물리적 한계를 극복하기 위해 특수 목적 차량 작업 시 차량의 넘어짐 방지와 차체 보호를 위해 아웃트리거를 착지시키는데 도로 상태에 따라 사용자가 직접 최적화를 수행하는데 어려움이 존재한다. 본 논문에서는 도로 표면 상태를 신속하게 판단하여 아웃트리거 수직 및 수평 전개 착지 시 시간 소모, 안전사고 발생을 낮추기 위해 시멘틱 분할을 이용한 도로 표면 상태를 분석하는 연구를 수행하였다. 13가지로 구분된 도로 표면 상황에 대하여 DeepLabV3+를 통해 실험한 결과 픽셀 성능0.7819, mIoU 0.7085 결과를 도출하였다.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.484-491
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• 2015

Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.34-40
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• 2018

This paper presents a new method to estimate the pose of a moving object accurately using a monocular camera and a low-end GPS+IMU sensor system. For this goal, we adopted a deep neural network for the semantic segmentation of input images and compared the results with a semantic map of a neighborhood. In this map matching, we use weight tables to deal with label inconsistency effectively. Signals from a low-end GPS+IMU sensor system are used to limit search spaces and minimize the proposed function. For the evaluation, we added noise to the signals from a high-end GPS-IMU system. The results show that the pose can be recovered from the noisy signals. We also show that the proposed method is effective in handling non-open-sky situations.

• Spatial Information Research
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• v.2 no.2
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• pp.189-196
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• 1994

Adaptive smoothing filter is a filter that averages out the intensities around the pixels of similar intensities while conserving the discontinuties. When human eyes rec-ognize a map, the brain can easily assign one color for each element such as road or building while computer distinguishes all the minute color differences even for one ele¬ment. We can approach to the solution by using the adaptive smoothing filter so that the machine can assign one color for each element as much as we want, and it is found to be a very essential tool foor map segmentation of urban areas. The filter is applied to a scanned map, and it is used to extract roads and residential areas.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.77-85
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• 2011

In this paper, we propose an region segmentation and texture based feature extraction method which split the pavement and roadway from the camera which equipped to the visually impaired person during a walk. We perform the hough transformation method for detect the boundary between pavement and roadway, and devide the segmented region into 3-level according to perspective. Next step, split into pavement and roadway according to the extracted texture feature of segmented regions. Our walking assistance system use rotation-invariant LBP and GLCM texture features for compare the characteristic of pavement block with various pattern and uniformity roadway. Our proposed method show that can segment two regions with illumination invariant in day and night image, and split there regions rotation and occlution invariant in complexed outdoor image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.163-171
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• 2012

High resolution satellite images have been used for building and road system analysis, landscape analysis, and ecological assessment for several years. However, in high resolution satellite images, shadows are necessarily cast by manmade objects such as buildings and over-pass bridges. This paper develops the shadow extraction procedures in urban area including various land-use classes, and the extracted shadow areas are evaluated by a manually digitized shadow map. For the shadow extraction, the Canny edge operator and the dilation filter are applied to make building edge buffer area. Also, the object-based segmentation was performed using Gram-Schmitt fusion image, and spectral and spatial parameters are calculated from the segmentation results. Finally, we proposed appropriate parameters and extraction rules for the shadow extraction. The accuracy of the shadow extraction results from the various assessment indices is 80% to 90%.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.45-52
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• 2023

Recently, as the importance of walking in daily life has been emphasized, projects to guarantee walking rights and create a pedestrian environment are being promoted throughout the region. In previous studies, a pedestrian environment assessment was conducted using Jeonju-si road images, and an image comparison pair data set was constructed. However, data sets expressed in numbers have difficulty in generalizing the judgment criteria of pedestrian environment assessors or visually identifying the pedestrian environment preferred by pedestrians. Therefore, this study proposes a method to interpret the results of the pedestrian environment assessment through data visualization by building a web application. According to the semantic segmentation result of analyzing the walking environment components that affect pedestrian environment assessors, it was confirmed that pedestrians did not prefer environments with a lot of "earth" and "grass," and preferred environments with "signboards" and "sidewalks." The proposed study is expected to identify and analyze the results randomly selected by participants in the future pedestrian environment evaluation, and believed that more improved accuracy can be obtained by pre-processing the data purification process.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.353-360
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• 2006

This paper presents a robust algorithm for segmenting a vehicle license plate area from a road image. We consider the features of license plates in three aspects : 1) edges due to the characters in the plate, 2) colors in the plate, and 3) geometric properties of the plate. In the preprocessing step, we compute the thresholds based on each feature to decide whether a pixel is inside a plate or not. A statistical approach is applied to the sample images to compute the thresholds. For a given road image, our algorithm binarizes it by using the thresholds. Then, we select three candidate regions to be a plate by searching the binary image with a moving window. The plate area is selected among the candidates with simple heuristics. This algorithm robustly detects the plate against the transformation or the difference of color intensity of the plate in the input image. Moreover, the preprocessing step requires only a small number of sample images for the statistical processing. The experimental results show that the algorithm has 97.8% of successful segmentation of the plate from 228 input images. Our prototype implementation shows average processing time of 0.676 seconds per image for a set of $1280{\times}960$ images, executed on a 3GHz Pentium4 PC with 512M byte memory.

• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.151-159
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• 2010

This paper presents a research result that was performed to develop a more accurate freeway crash prediction model than existing models. While the existing crash models only focus on developing crash relationships associated with highway geometric conditions found on a short section of a crash site, this research applies a different approach considering the upstream highway geometric conditions as well. Theoretically, crashes occur while motorists are in motion, and particularly at freeways vehicle speed at one specific point is very sensitive to upstream geometric conditions. Therefore, this is a reasonable approach. To form the analysis data base, this research gathers the geometric conditions of the West Seaside Freeway 269.3 km and six years crash data ranging 2003-2008 for these freeway sections. As a result, it is found that crashes fit well into Negative Binomial Distribution, and, based on the developed model, total number of crashes is inversely proportional to highway curve length and radius. Contrarily, crash occurrences are proportional to tangent length. This result is different from existing crash study results, and it seems to be resulted from this research assumption that a crash is influenced greatly by upstream geometric conditions. Also, this research provides the expected effects on crash occurrences of the length of downgrade sections, speed camera placements, and the on- and off- ramp presences. It is expected that this research result is useful for doing more reasonable highway designs and safety audit analysis, and applying the same research approach to national roads and other major roads in urban areas is recommended.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.86-96
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• 2010

In this paper, we propose stereo vision-based obstacle detection and vehicle verification methods using U-disparity map and bird's-eye view mapping. First, we extract a road feature using maximum frequent values in each row and column. And we extract obstacle areas on the road using the extracted road feature. To extract obstacle areas exactly we utilize U-disparity map. We can extract obstacle areas exactly on the U-disparity map using threshold value which consists of disparity value and camera parameter. But there are still multiple obstacles in the extracted obstacle areas. Thus, we perform another processing, namely segmentation. We convert the extracted obstacle areas into a bird's-eye view using camera modeling and parameters. We can segment obstacle areas on the bird's-eye view robustly because obstacles are represented on it according to ranges. Finally, we verify the obstacles whether those are vehicles or not using various vehicle features, namely road contacting, constant horizontal length, aspect ratio and texture information. We conduct experiments to prove the performance of our proposed algorithms in real traffic situations.