• 대한임베디드공학회논문지
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• 제10권5호
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• 한국통신학회논문지
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• 제38A권2호
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• pp.174-182
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• 2013

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.

• 한국조경학회:학술대회논문집
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• 한국조경학회 2007년도 Journal of Landscape Architecture in Asia Vol.3
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• pp.162-167
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• 2007

The courtyard life style ever existed as the major traditional living form in wide north China areas. The research made case study and specific analysis on the space organization, constitution and order of the courtyard living, as well as the dwelling accessory manners and transfer space patterns both inside and outside of the courtyard. Pingyao ancient city as the research sample (object), its courtyard composition, classification and characteristic are studied profoundly to explore local traditional cultural traits. On upon the analysis, the research is concluded that the civil courtyard in the ancient Pingyao city bears the feature of in a continuous changing process, which represented as 'from confined to opening', 'from narrow to wide', 'from public to private'. Despite of the courtyard dwelling location and direction, all accessories architectural items on the main room roof were influenced by the geomantic omen culture. As of the inside wall body, entrance and side wall of the courtyard, the woodcraft, stone carving, brick carving on above not only could functioned in architecture artistic, but also express the traditional education spiritual feature.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.49-60
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• 2020

To solve the edge ringing or block effect caused by the partial differential diffusion in image enhancement domain, a new image enhancement algorithm based on bidirectional diffusion, which smooths the flat region or isolated noise region and sharpens the edge region in different types of defect images on aviation composites, is presented. Taking the image pixel's neighborhood intensity and spatial characteristics as the attribute descriptor, the presented bidirectional diffusion model adaptively chooses different diffusion criteria in different defect image regions, which are elaborated are as follows. The forward diffusion is adopted to denoise along the pixel's gradient direction and edge direction in the pixel's smoothing area while the backward diffusion is used to sharpen along the pixel's gradient direction and the forward diffusion is used to smooth along the pixel's edge direction in the pixel's edge region. The comparison experiments were implemented in the delamination, inclusion, channel, shrinkage, blowhole and crack defect images, and the comparison results indicate that our algorithm not only preserves the image feature better but also improves the image contrast more obviously.

• Journal of Mechanical Science and Technology
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• 제19권12호
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• pp.2213-2223
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• 2005

In order to reconstruct a full 3D human model in reverse engineering (RE), a 3D scanner needs to be placed arbitrarily around the target model to capture all part of the scanned surface. Then, acquired multiple scans must be registered and merged since each scanned data set taken from different position is just given in its own local co-ordinate system. The goal of the registration is to create a single model by aligning all individual scans. It usually consists of two sub-steps: rough and fine registration. The fine registration process can only be performed after an initial position is approximated through the rough registration. Hence an automated rough registration process is crucial to realize a completely automatic RE system. In this paper an automated rough registration method for aligning multiple scans of complex human face is presented. The proposed method automatically aligns the meshes of different scans with the information of features that are extracted from the estimated principal curvatures of triangular meshes of the human face. Then the roughly aligned scanned data sets are further precisely enhanced with a fine registration step with the recently popular Iterative Closest Point (ICP) algorithm. Some typical examples are presented and discussed to validate the proposed system.

• ETRI Journal
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• 제44권6호
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• pp.1034-1046
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• 2022

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.

• 천문학회보
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• 제46권2호
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• pp.66.1-66.1
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• 2021

Extinction curves observed toward individual Active Galactic Nuclei (AGN) usually show a steep rise toward Far-Ultraviolet (FUV) wavelengths and can be described by the Small Magellanic Cloud (SMC)-like dust model. This feature suggests the dominance of small dust grains of size a < 0.1 ㎛ in the local environment of AGN, but the origin of such small grains is unclear. In this paper, we aim to explain this observed feature by applying the RAdiative Torque Disruption (RATD) to model the extinction of AGN radiation from FUV to Mid-Infrared (MIR) wavelengths. We find that in the intense radiation field of AGN, large composite grains of size a > 0.1 ㎛ are significantly disrupted to smaller sizes by RATD up to dRATD > 100 pc in the polar direction and dRATD ~ 10 pc in the torus region. Consequently, optical-MIR extinction decreases, whereas FUV-near-Ultraviolet extinction increases, producing a steep far-UV rise extinction curve. The resulting total-to selective visual extinction ratio thus significantly drops to RV < 3.1 with decreasing distances to AGN center due to the enhancement of small grains. The dependence of RV with the efficiency of RATD will help us to study the dust properties in the AGN environment via photometric observations. In addition, we suggest that the combination of the strength between RATD and other dust destruction mechanisms that are responsible for destroying very small grains of a <0.05 ㎛ is the key for explaining the dichotomy observed "SMC" and "gray" extinction curve toward many AGN.

• Asia pacific journal of information systems
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• 제29권1호
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• pp.35-49
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• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.

• 한국전자통신학회논문지
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• 제8권8호
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• pp.1179-1186
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• 2013

본 논문에서는 터널 내에서 차량의 운행 상태를 모니터링하기 위하여 차량 검출 및 추적 알고리즘을 제안한다. 제안하는 알고리즘은 세 단계로 이루어진다. 첫 단계는 배경추정으로서 비교적 간단한 Running Gaussian Average (RGA)를 사용한다. 두 번째 단계는 차량검출 단계이며, Adaboost 알고리즘을 적용한다. 상대적으로 먼거리의 차량에 대한 오검출을 줄이기 위하여 차량의 높이별 부분 특징을 이용하여 차량을 검출한다. 물체의 부분 특징들이 임계값 이상이면 차량으로 분류한다. 마지막 단계는 차량추적 단계이며, Kalman 필터를 적용하여 이동하는 물체를 추적한다. 컴퓨터 시뮬레이션을 통하여 제안하는 알고리즘이 터널 내에서 차량 검출 및 추적에 유용한 것을 확인하였다.

• 한국지역지리학회지
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• 제15권4호
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• pp.449-463
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• 2009

일본의 다문화공생정책의 특징은 중앙정부가 지방정부의 역할의 중요성을 강조하고, 지방정부가 주체가 되어 다문화공생 정책을 추진해나갈 수 있도록 제도적 틀을 제시한다는 점에 있다. 즉, 일본의 다문화공생 정책은 실제로 정책이 실시되고 있는 지역의 다양성을 반영하기 위한 아래로부터 위로의 의견의 전달, 중앙정부 차원에서의 위로부터 아래로의 정책방향의 제시 및 권고를 통하여 다문화정책의 한계점을 극복하고자 하고 있다. 다문화공동체내의 시민단체, 연구자, 다문화가정 등과 같은 각 집단을 연결해주는 네트워크의 중심자의 역할에 따라 각 집단이 수행하는 기능의 활성화와 효율성은 달라진다. 그러므로 다문화현상이 실제로 진행되고 있는 지역에서 지방정부가 조정과 통합의 역할을 수행함으로서 중심 자로서의 역할을 담당하도록 하고 있다는 점에서 일본의 다문화공생정책은 사회통합적인 다문화정책의 방향을 본격적으로 모색하기 시작한 한국의 다문화정책에 많은 시사점을 제공한다.