• Title/Summary/Keyword: resampling method

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Calculation of Buildlng Heights from a Single Satellite Image (고해상도 단일 위성영상으로부터 건물높이값 추출)

  • 이병환;김정희;박경환
    • Spatial Information Research
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    • v.7 no.1
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    • pp.89-101
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    • 1999
  • This paper represents methods to calculate heights of buildings by estimating their shadow lengths in a single and panchromatic image of the KVR-1000 camera system Shadows are identified Com brightness intensity of each pixel, and their lengths are measured. Two methods are implemented to estimate heights from shadows. One method is to use a ratio of shadow s lengths with respect to heights of reference buildings measured on site. The other method uses sun elevation angles calculated from various camera s parameters at the exposure time. The estimated heights of 20 buildings are compared with heights measured on site, and the RMS errors for each method are 1.70m and 1.75m, respectively. When a resampling method to enhance identification of shadows is used and their lengths are accordingly re-calculated, the estimated errors for each method are significantly reduced to 1.17m and 1.16m, respectively. Meanwhile, effects of land slope on shadows can be hardly obtained unless detailed DTM(digital terrain model) are available, and they introduce additional errors up to 25m.

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Effects of Spatial Resolution on PSO Target Detection Results of Airplane and Ship (항공기와 선박의 PSO 표적탐지 결과에 공간해상도가 미치는 영향)

  • Yeom, Jun Ho;Kim, Byeong Hee;Kim, Yong Il
    • Journal of Korean Society for Geospatial Information Science
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    • v.22 no.1
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    • pp.23-29
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    • 2014
  • The emergence of high resolution satellite images and the evolution of spatial resolution facilitate various studies using high resolution satellite images. Above all, target detection algorithms are effective for monitoring of traffic flow and military surveillance and reconnaissance because vehicles, airplanes, and ships on broad area could be detected easily using high resolution satellite images. Recently, many satellites are launched from global countries and the diversity of satellite images are also increased. On the contrary, studies on comparison about the spatial resolution or target detection, especially, are insufficient in domestic and foreign countries. Therefore, in this study, effects of spatial resolution on target detection are analyzed using the PSO target detection algorithm. The resampling techniques such as nearest neighbor, bilinear, and cubic convolution are adopted to resize the original image into 0.5m, 1m, 2m, 4m spatial resolutions. Then, accuracy of target detection is assessed according to not only spatial resolution but also resampling method. As a result of the study, the resolution of 0.5m and nearest neighbor among the resampling methods have the best accuracy. Additionally, it is necessary to satisfy the criteria of 2m and 4m resolution for the detection of airplane and ship, respectively. The detection of airplane need more high spatial resolution than ship because of their complexity of shape. This research suggests the appropriate spatial resolution for the plane and ship target detection and contributes to the criteria of satellite sensor design.

Jensen's Alpha Estimation Models in Capital Asset Pricing Model

  • Phuoc, Le Tan
    • The Journal of Asian Finance, Economics and Business
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    • v.5 no.3
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    • pp.19-29
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    • 2018
  • This research examined the alternatives of Jensen's alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.

Modeling Clustered Interval-Censored Failure Time Data with Informative Cluster Size (군집의 크기가 생존시간에 영향을 미치는 군집 구간중도절단된 자료에 대한 준모수적 모형)

  • Kim, Jinheum;Kim, Youn Nam
    • The Korean Journal of Applied Statistics
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    • v.27 no.2
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    • pp.331-343
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    • 2014
  • We propose two estimating procedures to analyze clustered interval-censored data with an informative cluster size based on a marginal model and investigate their asymptotic properties. One is an extension of Cong et al. (2007) to interval-censored data and the other uses the within-cluster resampling method proposed by Hoffman et al. (2001). Simulation results imply that the proposed estimators have a better performance in terms of bias and coverage rate of true value than an estimator with no adjustment of informative cluster size when the cluster size is related with survival time. Finally, they are applied to lymphatic filariasis data adopted from Williamson et al. (2008).

DCT Based Watermarking Technique Using Region of Interest (관심영역을 이용한 DCT기반 워터마킹 기법)

  • Shin, Jae-Wook;Jeong, Dong-Seok
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.37 no.1
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    • pp.16-26
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    • 2000
  • The proposed method inserts a watermark information not mto a whole Image region but only into regions of interest(ROIs) To extract the ROIs, we divide an original Image into sub-blocks and use modified Shi-Kuo Chang's PIM(picture information measure) as the criteria to select the ROIs Considering the directional information and frequency bands, we insert the watermark information into sub-blocks m the DCT domain. The proposed method can reduce the distortion in comparison With the other methods which utilize the whole Image as an nor The proposed method makes much less damaged Images m comparison to the other methods And those Images processed by the proposed algorithm are more robust to the changes caused by signal processing operations such as resampling, clipping. noise, and so on Also due to the block-based watermark insertion, the proposed method has the robustness to the Image compression processes such as JPEG and MPEG.

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The Digital Orthophoto Production by the Automative Generation of DEM using Non-photogrammetric Scanner (비측정용 주사기를 사용한 수치표고모델의 자동생성에 의한 수치정사사진 제작)

  • Park, Woon-Yong;Yi, Gi-Chul;Lee, In-Soo;Kim, Jin-Su
    • Journal of the Korean Association of Geographic Information Studies
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    • v.3 no.2
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    • pp.24-36
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    • 2000
  • This study deals with the optimal method of orthophoto products using the non-photogrammetric scanners. we scanned positive film of aerial photographs at the different resolution and producted the orthophoto using the automatically generated DEM based on the Digital Photogrammetric Workstation, considering aerial image resolutions, DEM interval, resampling method and outpixel size. As a results, the acquired accuracy was worse in horizontal, but good in vertical. So It will be expected that orthophoto using non-photo grammetric scanner is good enough for the acquisition of GIS data and the calculation of soil volumes.

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On the Equality of Two Distributions Based on Nonparametric Kernel Density Estimator

  • Kim, Dae-Hak;Oh, Kwang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.247-255
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    • 2003
  • Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

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Development of Web-based Statistical System for Bootstrap on the Internet Environment (인터넷 환경에서 붓스트랩 통계 시스템의 개발)

  • 최성운;임인섭
    • Journal of the Korea Safety Management & Science
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    • v.6 no.2
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    • pp.241-250
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    • 2004
  • Recently, growth of internet causes rapid changes in many areas of statistics such as statistical computation and education. Especially, bootstrap is the most interesting statistical methods applying computer resampling simulation. In this study, we try to present how to use a method of bootstrap on the internet. We also develop to user a statistical system which is programed with java applet for user to handle easily.

Rank Reduction for Wideband Signals incident on a Uniform Linear Array

  • Hong, Wooyoung
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1992.06a
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    • pp.123-126
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    • 1992
  • A new class of data transformation matri is introduced for estimation of angles of arrivals by the rank reduction of multiple wideband sources. The proposed unitary focusing matri minimizes the average of the squared norm of focusing error over the angles of interest without a priori knowledge of source locations. The merit that result as a consequence is a lower resolution threshold. These matrices can be applied to the case of the multigroup sources. Simulations and the comparison of statistical performance are compared with the algorithms (especially, spatial resampling method) which does not require the pre-estimation.

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A Simple Bias-Correction Rule for the Apparent Prediction Error

  • Beong-Soo So
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.146-154
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    • 1995
  • By using simple Taylor expansion, we derive an easy bias-correction rule for the apparent prodiction error of the predictor defined by the general M-estimators with respect to an arbitrary measure of prediction error. Our method has a considerable computational advantage over the previous methods based on the resampling thchnique such as Cross-validaton and Boothtrap. Connections with AIC, Cross-Validation and Boothtrap are discussed too.

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