• The Journal of the Korea Contents Association
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• v.2 no.4
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• pp.46-50
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• 2002

This paper suggests the adaptive binary method which applies advanced silt sum technique, so that threshold value can be changed heuristically according to the brightness of captured fingerprint image. Through this research, we tried to resolve threshold value setting issue by the local differences of brightness of fingerprint image in the binary image preprocessing. The experimental results show that our proposed preprocessing method demonstrates the better recognition accuracy and can be applied to minutiae extraction algorithm for fingerprint recognition system.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.100-104
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• 2013

In this paper, we present an optimal threshold setting method for flame detection of infrared thermal image. Conventional infrared flame detection methods used fixed intensity threshold to segment candidate flame regions and further processing is performed to decide correct flame detection. So flame region segmentation step using the threshold is important processing for fire detection algorithm. The threshold should be change in input image depends on camera types and operation conditions. We have analyzed the conventional thresholds composed of fixed-intensity, average, standard deviation, maximum value. Finally, we extracted that the optimal threshold value is more than summation of average and standard deviation, and less than maximum value. it will be enhance flame detection rate than conventional fixed-threshold method.

• Ocean Systems Engineering
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• v.6 no.3
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• pp.217-231
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• 2016

This paper presents a new approach for underwater image analysis using the bi-dimensional empirical mode decomposition (BEMD) technique and the phase congruency information. The BEMD algorithm, fully unsupervised, it is mainly applied to texture extraction and image filtering, which are widely recognized as a difficult and challenging machine vision problem. The phase information is the very stability feature of image. Recent developments in analysis methods on the phase congruency information have received large attention by the image researchers. In this paper, the proposed method is called the EP model that inherits the advantages of the first two algorithms, so this model is suitable for processing underwater image. Moreover, the receiver operating characteristic (ROC) curve is presented in this paper to solve the problem that the threshold is greatly affected by personal experience when underwater image edge detection is performed using the EP model. The EP images are computed using combinations of the Canny detector parameters, and the binaryzation image results are generated accordingly. The ideal EP edge feature extractive maps are estimated using correspondence threshold which is optimized by ROC analysis. The experimental results show that the proposed algorithm is able to avoid the operation error caused by manual setting of the detection threshold, and to adaptively set the image feature detection threshold. The proposed method has been proved to be accuracy and effectiveness by the underwater image processing examples.

• Journal of Digital Contents Society
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• v.16 no.6
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• pp.951-959
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• 2015

Scene text extraction is important because it offers some important information on different image based applications pouring in current smart generation. Edge-Enhanced MSER(Maximally Stable Extremal Regions) which enhances the boundaries using the canny operator after extracting the basic MSER shows excellent performance in terms of text extraction. But according to setting the threshold of the canny operator, the result images using Edge-Enhanced MSER are different, so there needs a method figuring out the threshold. In this paper, we propose a AEMSER(Adaptive Edge-enhanced MSER) that applies the method extracting the boundary using the middle value of histogram to Edge-Enhanced MSER to get the canny operator's threshold. The proposed method can acquire better result images than the existing methods because it extracts the area only for the obvious boundaries.

• Asian Journal of Atmospheric Environment
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• v.12 no.1
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• pp.1-16
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• 2018

Ground-level ozone ($O_3$) can be a menace for vegetation, especially in Asia where $O_3$ levels have been dramatically increased over the past decades. To ensure food security and maintain forest ecosystem services, such as nutrient cycling, carbon sequestration and functional diversity of soil biota, in the over-populated Asia, environmental standards are needed. To set proper standards, dose-response relationships should be established from which critical levels are derived. The predictor of the response in the dose-response relationship is an $O_3$ metric that indicates the dose level to which the plant has been exposed. This study aimed to review the relevant scientific literature and summarize the $O_3$ metrics used worldwide to provide insights for Asia. A variety of $O_3$ metrics have been used, for which we discuss their strengths and weaknesses. The most widely used metrics are based only on $O_3$ levels. Such metrics have been adopted by several regulatory agencies in the global. However, they are biologically irrelevant because they ignore the plant physiological capacity. Adopting AOT40 ($O_3$ mixing ratios Accumulated Over the Threshold of $40nmol\;mol^{-1}$) as the default index for setting critical levels in Asia would be a poor policy with severe consequences at national and Pan-Asian level. Asian studies should focus on flux-based $O_3$ metrics to provide relevant bases for developing proper standards. However, given the technical requirements in calculating flux-based $O_3$ metrics, which can be an important limitation in developing countries, no-threshold cumulative exposure indices like AOT0 should always accompany flux-based indices.

• Progress in Medical Physics
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• v.28 no.3
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• pp.77-82
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• 2017

We proposed the objective ROC analysis method based on the setting of threshold value for evaluation of single photon emission computed tomography (SPECT) image. This proposed ROC analysis method uses the quantification computational threshold value to each signal on the SPECT image. The SPECT images for this study were acquired by using Monte Carlo n-particle extended simulation code (MCNPX, Ver. 2.6.0, Los Alamos National Laboratory, USA). The basic SPECT detectors and specific water phantom were realized in the simulation, and we could get the simulation results by the simulation operation. We tried to analyze the reconstructed images using threshold value application based objective ROC method. We can get the accuracy information of reconstructed region in the image. This proposed ROC technique can be helpful when we have to evaluate the weak signal for the NM image. In this study, the proposed threshold value based computational ROC analysis method can provide better objectivity than the conventional ROC analysis method.

• Smart Media Journal
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• v.13 no.4
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• pp.48-56
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• 2024

In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detection. The first method is an outlier detection using Mean Squared Error (MSE), and the second is an outlier detection using a Dynamic Threshold, which analyzes variability against the average value of previous data to identify outliers. The MSE-based method demonstrated a 94.98% accuracy rate, while the Dynamic Threshold method, which uses standard deviation, showed superior performance with 99.66% accuracy.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.1-5
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• 2013

CR(Cognitive Radio) technology is maximized frequency reuse using unused frequency band. Conventional cooperative spectrum sensing has fixed false alarms. so all cooperative user has equal threshold. This problem is caused degradation of detection probability. so we propose and analysis the system model that false alarm differential setting through CSI(channel state information) for resonable threshold. Simulation results show improvement of detection probability.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.3
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• pp.152-156
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• 2010

Non-line-of-sight (NLOS) propagation is one of the challenges in radio positioning. Distinguishing the transmission status of the communication as line-of-sight (LOS) or NLOS is of great importance for the wireless communication systems. This paper focuses on the identification of NLOS based on time-of-arrival (TOA) distance estimates and the received signal strength (RSS) measurements. We set a path loss threshold based on the joint TOA and RSS based NLOS detection method to determine LOS or NLOS. Simulation results show that the proposed method ensures the correct of detection for the LOS condition and can improve the NLOS identification for the weak noise and long distance.