• Title/Summary/Keyword: Parametric and Nonparametric Algorithm

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Image Feature Detection and Contrast Enhancement Algorithms Based on Statistical Tests

  • Kim, Yeong-Hwa;Nam, Ji-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.2
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    • pp.385-399
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    • 2007
  • In many image processing applications, a random noise makes some trouble since most video enhancement functions produce visual artifacts if a priori of the noise is incorrect. The basic difficulty is that the noise and the signal are difficult to be distinguished. Typical unsharp masking (UM) enhances the visual appearances of images, but it also amplifies the noise components of the image. Hence, the applications of a UM are limited when noises are presented. This paper proposed statistical algorithms based on parametric and nonparametric tests to adaptively enhance the image feature and the noise combining while applying UM. With the proposed algorithm, it is made possible to enhance the local contrast of an image without amplifying the noise.

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Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns

  • Zhang, Wengang;Goh, Anthony T.C.
    • Geomechanics and Engineering
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    • v.7 no.4
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    • pp.431-458
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    • 2014
  • Construction of a new cavern close to an existing cavern will result in a modification of the state of stresses in a zone around the existing cavern as interaction between the twin caverns takes place. Extensive plane strain finite difference analyses were carried out to examine the deformations induced by excavation of underground twin caverns. From the numerical results, a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) has been used to relate the maximum key point displacement and the percent strain to various parameters including the rock quality, the cavern geometry and the in situ stress. Probabilistic assessments on the serviceability limit state of twin caverns can be performed using the First-order reliability spreadsheet method (FORM) based on the built MARS model. Parametric studies indicate that the probability of failure $P_f$ increases as the coefficient of variation of Q increases, and $P_f$ decreases with the widening of the pillar.

Deep Image Annotation and Classification by Fusing Multi-Modal Semantic Topics

  • Chen, YongHeng;Zhang, Fuquan;Zuo, WanLi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.1
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    • pp.392-412
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    • 2018
  • Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

Image noise reduction algorithms using nonparametric method (비모수 방법을 사용한 영상 잡음 제거 알고리즘)

  • Woo, Ho-young;Kim, Yeong-hwa
    • The Korean Journal of Applied Statistics
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    • v.32 no.5
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    • pp.721-740
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    • 2019
  • Noise reduction is an important field in image processing and requires a statistical approach. However, it is difficult to assume a specific distribution of noise, and a spatial filter that reflects regional characteristics is a small sample and cannot be accessed in a parametric manner. The first order image differential and the second order image differential show a clear difference according to the noise level included in the image and can be more clearly understood using the canyon edge detector. The Fligner-Killeen test was performed and the bootstrap method was used to statistically check the noise level. The estimated noise level was set between 0 and 1 using the cumulative distribution function of the beta distribution. In this paper, we propose a nonparametric noise reduction algorithm that accounts for the noise level included in the image.

A High Speed Road Lane Detection based on Optimal Extraction of ROI-LB (관심영역(ROI-LB)의 최적 추출에 의한 차선검출의 고속화)

  • Cheong, Cha-Keon
    • Journal of Broadcast Engineering
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    • v.14 no.2
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    • pp.253-264
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    • 2009
  • This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.

Analysis of promising countries for export using parametric and non-parametric methods based on ERGM: Focusing on the case of information communication and home appliance industries (ERGM 기반의 모수적 및 비모수적 방법을 활용한 수출 유망국가 분석: 정보통신 및 가전 산업 사례를 중심으로)

  • Jun, Seung-pyo;Seo, Jinny;Yoo, Jae-Young
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.175-196
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    • 2022
  • Information and communication and home appliance industries, which were one of South Korea's main industries, are gradually losing their export share as their export competitiveness is weakening. This study objectively analyzed export competitiveness and suggested export-promising countries in order to help South Korea's information communication and home appliance industries improve exports. In this study, network properties, centrality, and structural hole analysis were performed during network analysis to evaluate export competitiveness. In order to select promising export countries, we proposed a new variable that can take into account the characteristics of an already established International Trade Network (ITN), that is, the Global Value Chain (GVC), in addition to the existing economic factors. The conditional log-odds for individual links derived from the Exponential Random Graph Model (ERGM) in the analysis of the cross-border trade network were assumed as a proxy variable that can indicate the export potential. In consideration of the possibility of ERGM linkage, a parametric approach and a non-parametric approach were used to recommend export-promising countries, respectively. In the parametric method, a regression analysis model was developed to predict the export value of the information and communication and home appliance industries in South Korea by additionally considering the link-specific characteristics of the network derived from the ERGM to the existing economic factors. Also, in the non-parametric approach, an abnormality detection algorithm based on the clustering method was used, and a promising export country was proposed as a method of finding outliers that deviate from two peers. According to the research results, the structural characteristic of the export network of the industry was a network with high transferability. Also, according to the centrality analysis result, South Korea's influence on exports was weak compared to its size, and the structural hole analysis result showed that export efficiency was weak. According to the model for recommending promising exporting countries proposed by this study, in parametric analysis, Iran, Ireland, North Macedonia, Angola, and Pakistan were promising exporting countries, and in nonparametric analysis, Qatar, Luxembourg, Ireland, North Macedonia and Pakistan were analyzed as promising exporting countries. There were differences in some countries in the two models. The results of this study revealed that the export competitiveness of South Korea's information and communication and home appliance industries in GVC was not high compared to the size of exports, and thus showed that exports could be further reduced. In addition, this study is meaningful in that it proposed a method to find promising export countries by considering GVC networks with other countries as a way to increase export competitiveness. This study showed that, from a policy point of view, the international trade network of the information communication and home appliance industries has an important mutual relationship, and although transferability is high, it may not be easily expanded to a three-party relationship. In addition, it was confirmed that South Korea's export competitiveness or status was lower than the export size ranking. This paper suggested that in order to improve the low out-degree centrality, it is necessary to increase exports to Italy or Poland, which had significantly higher in-degrees. In addition, we argued that in order to improve the centrality of out-closeness, it is necessary to increase exports to countries with particularly high in-closeness. In particular, it was analyzed that Morocco, UAE, Argentina, Russia, and Canada should pay attention as export countries. This study also provided practical implications for companies expecting to expand exports. The results of this study argue that companies expecting export expansion need to pay attention to countries with a relatively high potential for export expansion compared to the existing export volume by country. In particular, for companies that export daily necessities, countries that should pay attention to the population are presented, and for companies that export high-end or durable products, countries with high GDP, or purchasing power, relatively low exports are presented. Since the process and results of this study can be easily extended and applied to other industries, it is also expected to develop services that utilize the results of this study in the public sector.