색상과 에지에 대한 통계 처리를 이용한 번호판 영역 분할 알고리즘 (A license plate area segmentation algorithm using statistical processing on color and edge information)
-
- 정보처리학회논문지B
- /
- 제13B권4호
- /
- pp.353-360
- /
- 2006
본 논문에서는 도로 영상으로부터 차량 번호판 영역을 분할하는 알고리즘을 제시한다. 차량의 번호판 영역이 다른 영역에 비해 차별되는 특정을 세 가지 측면으로 나누어, 1) 번호판의 내부 문자, 2) 번호판의 색상, 3) 번호판의 형태에 대해 분석한다. 전처리 과정에서는, 이와 같은 세 가지 측면을 고려하여 번호판의 내부 영역 및 크기를 판별할 수 있는 임계값들을 계산하며, 이를 위해 표본 영상에 대한 통계적 처리를 수행한다. 차량 영역 분할 알고리즘에서는 임계값들을 이용하여 입력영상 내부에서 번호판 영역이 강조되도록 영상을 이진화한다. 일정한 크기의 윈도우로 이진 영상(binary image) 전체를 탐색하여, 윈도우 내부 픽셀 값의 합이 높은 순으로 서로 중복이 없도록 후보 영역을 찾은 후, 간단한 휴리스틱을 이용하여 후보 영역들 중에서 번호판 영역을 선택한다. 이 알고리즘은 번호판의 변형 또는 색상 명암도에 차이가 있는 경우에 대해서 안정적이다. 또한 이 알고리즘은 복잡한 전처리 과정을 요구하지 않고, 적은 수의 표본 영상에 대한 통계 처리만으로도 228장의 실험 영상들에 대해 97.8% 정도의 높은 성공률을 보였다. 프로토타입 시스템을 구현한 결과는 512M 바이트 메모리를 장착한 3GHz 펜티엄4 PC에서
In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.
The purpose is to develop a system to reduce the organ movement from the respiration during the 3DCRT or IMRT. This research reports the experience of utilizing personally developed system for mobile tumors. The patients clinical database was structured for 10 mobile tumors and patient setup error measurement and immobilization device effects were investigated. The RMRD system is composed of the respiratory motion reduction device utilized in prone position and abdominal strip device(ASD) utilized in the supine position, and the analysis program, which enables the analysis on patients setup reproducibility. Dose to normal tissue between patients with RMRDs and without RMRDs was analyzed by comparing the normal tissue volume, field margins and dose volume histogram(DVH) using fluoroscopy and CT images. And, reproducibility of patients setup verify by utilization of digital images. When patients breathed freely, average movement of diaphragm was 1.2 cm in prone position in contrast to 1.6 cm in supine position. In prone position, difference in diaphragm movement with and without RMRDs was 0.5 cm and 1.2 cm, respectively, showing that PTV margins could be reduced to as much as 0.7 cm. With RMRDs, volume of the irradiated normal tissue (lung, liver) reduced up to 20 % in DVH analysis. Also by obtaining the digital image, reproducibility of patients setup verify by visualization using the real-time image acquisition, leading to practical utilization of our software. Internal organ motion due to breathing can be reduced using RMRDs, which is simple and easy to use in clinical setting. It can reduce the organ motion-related PTV margin, thereby decrease volume of the irradiated normal tissue.
잎의 수분 함유량은 식물의 건강상태를 나타내는 중요한 척도 중 하나로써, 이를 원격탐사를 활용 하여 모니터링 하는 것은 산림관리에 있어서 매우 중요하다. 본 연구에서는 식생 캐노피의 수분량을 연구하는데 유용한 지수인 Normalized Difference Water Index (NDWI)를 이용하여 한반도 산림의 water stress 정도를 알아보고자 한다. SPOT/VEGETATION S10 채널자료를 1999년부터 2013년까지 취득하여 NDWI 를 산출하였고, 데이터의 노이즈를 제거하기 위하여 단순이동평균, NDWI의 시간적 변화를 파악하기 위하 여standardized anomaly를 수행했으며, 직관적인 모니터링을 위해 NDWI anomaly를 등급화 하였다. 또한 피해면적 150 ha 이상의 대형 산불과 비교 검증을 통해, 산림 캐노피의 water stress 평가 인자로서 NDWI의 적합성을 파악하였다. 그 결과 연구 기간 중 대형 산불은 총 24회 발생하였으며 모든 발생 지점 및 인접 지역에서 음의 anomaly가 나타났다. 특히 NDWI anomaly의 등급이 'high'일 경우 대형 산불이 빈번하게 발생하는 것을 확인하였다.
본 논문에서는 현재 미국과 유럽에서 GPS와 같은 위성을 이용한 광역 항법 시스템의 백업 및 대체 항법 시스템으로 사용하기 위해 활발히 연구되어지고 있는 Loran-C와 같은 지상파 신호를 사용하였을 때, 이의 성능에 큰 영향을 미치는 요소 중 하나인 일변효과를 보정해 줄 수 있는 방안에 대해서 논한다. 일변효과를 보정해 주기 위해 먼저 최소제곱주파수분석(LSSA) 방법을 사용하여 신호에 포함되어 있는 개별적인 주기 성분을 찾아내고 이러한 신호에 대한 진폭 및 위상을 추정하여 보정 신호를 생성하고 이를 본래 신호에서 빼 줌으로써 주기 성분에 대해 보정해 준다. 본 논문에서는 이를 위한 간단한 알고리즘을 제안하고 이의 성능을 모의실험을 통해서 분석하였다. 모의실험 결과 신호대잡음비(SNR)가 0 dB인 상대적으로 열악한 수신 환경에 있어서 진폭 및 위상에 대해 각각 5 % 및 0.17 % 이내의 오류를 가지고 추정할 수 있음을 고찰하였다. 또한 실측한 Loran-C 데이터를 사용하여 보정 후 얻을 수 있는 성능 향상에 대해서도 분석하였으며, 이 결과 5 분의 이동 평균 간격을 사용하였을 때 대략 22 %의 성능 향상을 얻을 수 있음을 고찰하였다.
Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.
목적 : 호흡주기에 따른 위치변동 감지센서를 이용하여 종양의 위치가 일정워치에 있을 때만 방사선을 치료하는 호흡 동기치료기구를 제작하고 일정한 호흡주기 상태에서 수행된 CT simulation과 3차원 입체조형치료계획에 따라 방사선을 치료하는 시스템을 개발하고자 하였다. 호흡유무에 따른 종양의 치료 마진(margin)을 측정하고 계획용표적체적(planning target volume:PTV)의 크기에 따른 선량체적표(dose volume histogram:DVH)와 종양억제확률(tumor control probability:NTCP), 건강조직손상확률(normal tissue complication probability:NTCP) 및 선량 통계자료를 통하여 치료성과를 평가하고 선량증강 범위를 예측하고자 하였다. 대상 및 방법 : 종양이 비교적 작고 전이가 없는(T1N0M0) 5명의 폐암환자를 선택하여 X-선 조준장치를 이용하여 횡격막의 이동거리를 측정하는 방법으로 내부장기의 운동을 평가하였다. 호흡동기치료기구는 끌어당김 센서가 부착된 허리띠 모양으로 구성되었으며 이를 흉곽 또는 복부에 부착하여 호흡주기에 의한 흉곽의 크기변동에 따라 센서의 회로가 개폐되고 이것을 선형가속기의 조종간에 연결하는 간단한 기구로서 감도와 재현성이 높았다. 호흡을 배기한 후 일시적 호흡이 정지된 상태에서 Spiral-CT (PQ-5000)로 3차원 영상을 획득하고 Virtual CT-simulator (AcQ-SIM)에 의하여 종양의 위치와 주위 장기들을 확인 도시하였으며 3차원 치료계획장치(Pinnacle, ADAC Co.)를 이용하여 3차원 입체조형치료를 계획하였다. 치료계획의 평가는 호흡동기치료기구의 사용유무에 따른 PTV의 크기에 따라 최적 선량분포를 구사하였으며 각각의 DVH, TCP, NTCP 및 선량통계자료를 도출 비교 검토하였다. 결과 : X-선 simulation에서 폐암환자의 횡격막 이동은 약 1 cm에서 2.5 cm로서 평균 1.5 cm로 측정되었고 자유호흡시 PTV는 CTV (clinical target volume)에 약 2 cm 마진을 주었으며 호흡동기치료기구를 사용하였을 때는 0.5 cm 마진이 적당한 것으로 측정되었다. 종양의 PTV는 연장 마진의 거의 자승비로 증가하였으며 TCP의 값은 마진 범위
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70