• 정보처리학회논문지B
• /
• 제18B권6호
• /
• pp.365-374
• /
• 2011

최근 인터넷, IPTV/SMART TV, 소셜 네트워크 (social network)와 같은 정보 유통 채널의 다양화로 유해 비디오 분류 및 차단 기술 연구에 대한 요구가 높아가고 있으나, 현재까지는 비디오에 대한 유해성을 판단하는 연구는 부족한 실정이다. 기존 유해 이미지 분류 연구에서는 이미지에서의 피부 영역의 비율이나 Bag of Visual Words (BoVW)와 같은 공간적 특징들 (spatial features)을 이용하고 있다. 그러나, 비디오에서는 공간적 특징 이외에도 모션 반복성 특징이나 시간적 상관성 (temporal correlation)과 같은 시간적 특징들 (temporal features)을 추가적으로 이용하여 유해성을 판단할 수 있다. 기존의 유해 비디오 분류 연구에서는 공간적 특징과 시간적 특징들에서 하나의 특징만을 사용하거나 두 개의 특징들을 단순히 결정 단계에서 데이터 융합하여 사용하고 있다. 일반적으로 결정 단계 데이터 융합 방법은 특징 단계 데이터 융합 방법보다 높은 성능을 가지지 못한다. 본 논문에서는 기존의 유해 비디오 분류 연구에서 사용되고 있는 공간적 특징과 시간적 특징들을 특징 단계 융합 방법을 이용하여 융합하여 유해 비디오를 분류하는 방법을 제안한다. 실험에서는 사용되는 특징이 늘어남에 따른 분류 성능 변화와 데이터 융합 방법의 변화에 따른 분류 성능 변화를 보였다. 공간적 특징만을 이용하였을 때에는 92.25%의 유해 비디오 분류 성능을 보이는데 반해, 모션 반복성 특징을 이용하고 특징 단계 데이터 융합 방법을 이용하게 되면 96%의 향상된 분류 성능을 보였다.

• 대한원격탐사학회지
• /
• 제31권3호
• /
• pp.205-214
• /
• 2015

이 연구에서는 자료변환기법을 이용해 추출된 여러 특징과 다양한 분류방법론을 결합하여 다중시기 SAR 자료를 위한 새로운 토지피복 분류기법을 제안하였다. 먼저, 다중시기 SAR 자료로부터 원본자료와는 다른 새로운 정보를 추출하기 위해 주성분분석과 3차원 웨이블렛 변환을 이용한 자료변환을 수행하였다. 그리고 나서 최대우도법 분류자, 신경망, support vector machine을 포함한 세 가지 다른 분류자를 변환된 특징자료들과 원본 후방산란계수 자료를 포함한 세가지 자료에 적용하여 다양한 초기 분류 결과를 얻도록 한다. 이후 다수결규칙을 통해 모든 초기결과를 결합하여 최종 분류 결과를 생성하게 된다. 다중시기 ENVISAT ASAR 자료를 이용한 사례연구에서 모든 초기 결과는 사용한 특징자료와 분류자의 종류에 따라 매우 다양한 분류정확도를 보였다. 이러한 9개의 초기 분류 결과를 결합한 최종 분류 결과는 가장 높은 분류 정확도를 보여주고 있는데, 이는 각 초기 분류 결과가 토지피복을 결정하기 위한 상호 보완적인 정보를 제공하기 때문이다. 이 연구에서의 분류정확도 향상은 주로 자료변환을 통해 얻어진 각기 다른 특징자료와 다른 분류자를 결합에 의한 다양성 확보에서 기인한다. 그러므로 이 연구에서 제안한 토지피복 분류방법론은 다중시기 SAR자료의 분류에 효과적으로 적용가능하며, 또한 다중센서 원격탐사 자료융합으로 확장이 가능하다.

• 대한원격탐사학회지
• /
• 제23권3호
• /
• pp.181-188
• /
• 2007

The objective of this research was to investigate the optimal land cover classification algorithm for the monitoring of North Korea with MODIS multi-temporal data based on monthly phenological characteristics. Three frequently used land cover classification algorithms, ISODATA1), SMA2), and SOM3) were employed for this study; the land cover categories were forest, grass, agricultural, wetland, barren, built-up, and water body. The outcomes of the study can be summarized as follows. First, the overall classification accuracy of ISODATA, SMA, and SOM was 69.03%, 64.28%, and 73.57%, respectively. Second, ISODATA and SMA resulted in a higher classification accuracy of forest and agricultural categories, but SOM performed better for the built-up area, bare soil, grassland, and water. A possible explanation for this difference would be related to the difference of sensitivity against the vegetation activity. This would be related to the capability of SOM to express all of their values without any loss of data by maintaining the topology between pixels of primitive data after classification, while ISODATA and SMA retain limited amount of data after normalization process. Third, we can conclude that SOM is the best algorithm for monitoring the land cover change of North Korea.

• 대한원격탐사학회지
• /
• 제21권1호
• /
• pp.51-57
• /
• 2005

The currently operating NASA/GFZ Gravity Recovery and Climate Experiment (GRACE) mission is designed to measure small mass changes over a large spatial scale, including the mapping of continental water storage changes and other geophysical signals in the form of monthly temporal gravity field. The European Space Agency's Gravity field and steady state Ocean Circulation Explorer (GOCE) space gravity gradiometer (SGG) mission is anticipated to determine the mean Earth gravity field with an unprecedented geoid accuracy of several cm (rms) with wavelength of 130km or longer. In this paper, we present a summary of present GRACE studies for the recovery of hydrological signals in the Amazon basin using alternative processing and filtering techniques, and local inversion to enhance the temporal and spatial resolutions by two-folds or better. Simulation studies for the potential GRACE detection of slow deformations due to Nazca-South America plate convergence and glacial isostatic adjustment (GIA) signals show that these signals are at present difficult to detect without long-term data averaging and further improvement of GRACE measurement accuracy.

• Ocean Science Journal
• /
• 제40권4호
• /
• pp.201-208
• /
• 2005

An accurate particle tracking method for a finite difference method model is developed using a constant acceleration method. Being assumed constant temporal and spatial gradients, the new method permits temporal-spatial variability of particle velocity. Test results in a solid rotating flow show that the new method has second-order accuracy. The performance of the new method is compared with that of other methods; the first-order Euler forward method, and the second-order Euler predictor-corrector method. The new method is the most efficient method among the three. It is more accurate and efficient than the other two.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2019년도 춘계학술발표대회
• /
• pp.455-457
• /
• 2019

In this paper we study the effect of decomposed spatiotemporal convolutions for action recognition in videos. Our motivation emerges from the empirical observation that spatial convolution applied on solo frames of the video provide good performance in action recognition. In this research we empirically show the accuracy of factorized convolution on individual frames of video for action classification. We take 3D ResNet-18 as base line model for our experiment, factorize its 3D convolution to 2D (Spatial) and 1D (Temporal) convolution. We train the model from scratch using Kinetics video dataset. We then fine-tune the model on UCF-101 dataset and evaluate the performance. Our results show good accuracy similar to that of the state of the art algorithms on Kinetics and UCF-101 datasets.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2015년도 학술발표회
• /
• pp.180-180
• /
• 2015

Weather radar has been widely used in measuring precipitation and discharge and predicting flood risks. The radar rainfall estimate has one of the essential problems in terms of uncertainty and accuracy. Previous study analyzed radar errors to reduce its uncertainty or to improve its accuracy. Furthermore, a recent analyzed the effect of radar error on rainfall-runoff using spatial error model (SEM). SEM appropriately reproduced radar error including spatial correlation. Since the SEM does not take the time dependence into account, its time variability was not properly investigated. Therefore, in the current study, we extend the SEM including time dependence as well as spatial dependence, named after Spatial-Temporal Error Model (STEM). Radar rainfall events generated with STEM were tested so that the peak runoff from the response of a basin could be investigated according to dependent error. The Nam River basin, South Korea, was employed to illustrate the effects of STEM on runoff peak flow.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권8호
• /
• pp.2053-2067
• /
• 2023

This paper proposes two video quality assessment methods based on deep neural network. (i)The first method uses the IQF-CNN (convolution neural network based on image quality features) to build image quality assessment method. The LIVE image database is used to test this method, the experiment show that it is effective. Therefore, this method is extended to the video quality assessment. At first every image frame of video is predicted, next the relationship between different image frames are analyzed by the hysteresis function and different window function to improve the accuracy of video quality assessment. (ii)The second method proposes a video quality assessment method based on convolution neural network (CNN) and gated circular unit network (GRU). First, the spatial features of video frames are extracted using CNN network, next the temporal features of the video frame using GRU network. Finally the extracted temporal and spatial features are analyzed by full connection layer of CNN network to obtain the video quality assessment score. All the above proposed methods are verified on the video databases, and compared with other methods.

• 한국전산유체공학회:학술대회논문집
• /
• 한국전산유체공학회 2008년도 학술대회
• /
• pp.42-49
• /
• 2008

A wavelet method is presented in order to improve the computational efficiency of two dimensional unsteady flow problems while maintaining the order of accuracy of conventional CFD schemes. First, by using the interpolating wavelet transformation including decomposition and thresholding, an adaptive dataset to a solution is constructed. Then, inviscid and viscous fluxes are calculated only at the points within an adaptive dataset, which enhances the computational efficiency. Second, thresholding step is modified to maintain the spatial and temporal accuracy of conventional CFD schemes automatically by selecting the threshold value between user-defined value and the magnitude of spatial or temporal truncation error. The wavelet method suggested in this study is successfully applied to various unsteady flow problems and it is shown that the computational efficiency is enhanced with maintaining the computational accuracy of CFD schemes.

• 한국전산유체공학회:학술대회논문집
• /
• 한국전산유체공학회 2008년 추계학술대회논문집
• /
• pp.42-49
• /
• 2008

A wavelet method is presented in order to improve the computational efficiency of two dimensional unsteady flow problems while maintaining the order of accuracy of conventional CFD schemes. First, by using the interpolating wavelet transformation including decomposition and thresholding, an adaptive dataset to a solution is constructed. Then, inviscid and viscous fluxes are calculated only at the points within an adaptive dataset, which enhances the computational efficiency. Second, thresholding step is modified to maintain the spatial and temporal accuracy of conventional CFD schemes automatically by selecting the threshold value between user-defined value and the magnitude of spatial or temporal truncation error. The wavelet method suggested in this study is successfully applied to various unsteady flow problems and it is shown that the computational efficiency is enhanced with maintaining the computational accuracy of CFD schemes.