• The Journal of Information Systems
• /
• v.14 no.3
• /
• pp.117-122
• /
• 2005

This paper presents a method for detecting scene changes in video sequences, in which the $chi^{2}$-test is modified by imposing weights according to NTSC standard. To automatically determine threshold values for scene change detection, the proposed method utilizes the frame differences that are obtained by the weighted $chi^{2}$-test. In the first step, the mean and the standard deviation of the difference values are calculated, and then, we subtract the mean difference value from each difference value. In the next step, the same process is performed on the remained difference values, mean-subtracted frame differences, until the stopping criterion is satisfied. Finally, the threshold value for scene change detection is determined by the proposed automatic threshold estimation algorithm. The proposed method is tested on various video sources and, in the experimental results, it is shown that the proposed method is reliably estimates the thresholds and detects scene changes.

• Journal of Information Processing Systems
• /
• v.13 no.3
• /
• pp.491-499
• /
• 2017

In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.4
• /
• pp.213-219
• /
• 2021

The submarine's intercept pulse detection detects pulses radiated from enemy surface ships, submarines, and torpedoes, and performs an important function of providing maneuverability and survivability of submarine. Whether or not the intercept pulse is detected is determined by comparing the size of the received pulse with the threshold value by the operator. In the case of intercept pulses, the intensity of the pulses is frequently reduced under the influence of various environmental factors. In the situation, if detection is performed with a fixed threshold, a non-detection problem occurs and persists until the operator sets a low threshold. In this paper, we proposed method for automatically adjusting threshold to reduce the non-detection problem caused by a fixed threshold. Simulation were preformed on 4 cases with different pulse level fluctuation widths, and it was confirmed that the detection performance was improved by increasing the number of detections when a method for automatically adjusting threshold was applied to all cases. Through the proposed method, it is expected that the intercept pulse detection performance will be improved in the marine environment the large fluctuations in pulse level in the future.

• Journal of information and communication convergence engineering
• /
• v.9 no.6
• /
• pp.763-768
• /
• 2011

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

• Proceedings of the Korea Contents Association Conference
• /
• 2003.05a
• /
• pp.426-429
• /
• 2003

This paper proposes the threshold value selection for surface classification of 3-dimensional objects. Pre-existing method which uses the H-curvature and K-curvature has limitation in the practical threshold value selection. For this, this paper proposes the threshold value selection by the statistical method. Finally, the effectiveness of this paper demonstrated by experiment.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.10
• /
• pp.112-119
• /
• 1997

Zerotreeimage coding is known as a simple and effective image comprssion algorithm. It has the property that the compression is generated in order of improtance. Conventionally, a fixed threshold is applied to the entire wavelet coefficients regardless of frequency and local features of an image. In this paper, we propose a new zerotree coding scheme with adaptive threshold. The adaptive threshold is determined by human visual characteristics. It is shown that the image quality of the proposed method is better than that of the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.4A
• /
• pp.356-363
• /
• 2002

Video scene segmentation is fundamental role for content based video analysis and many kinds of scene segmentation schemes have been proposed in previous researches. However, there is a problem, which is to find optimal threshold value according to various kinds of movies and its content because only fixed single threshold value usually used for cut detection. In this paper, we proposed the variable dynamic threshold method, which change the threshold value by a probability distribution of cut detection interval and information of frame feature differences and cut detection interval in previous cut detection is used to determine the next cut detection. For this, we present a cut detection algorithm and a parameter generation method to change the threshold value in runtime. We also show the proposed method, which can minimize fault alarm rate than the existing methods efficiently by experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.7B
• /
• pp.892-896
• /
• 2011

This paper proposes a two-stage synchronization scheme using a serial search non-coherent correlator appropriate for the IEEE 802.15.4a system. The proposed method improved the synchronization performance by using multi-pulse signals unlike the conventional method using single-pulse signals. It also compensated for the degradation of performance at low SNR resulting from the use of fixed threshold by applying the adaptive threshold technique. The proposed method showed a detection probability that is higher by approximately 0.2-0.3 compared with the conventional method in the IEEE 802.15.4a channel model.

• Korean Journal of Remote Sensing
• /
• v.15 no.3
• /
• pp.239-251
• /
• 1999

The elimination process of cloud-contaminated pixels is one of important steps before obtaining the accurate parameters of land and ocean surface from AVHRR imagery. We developed a 6step threshold method to detect the cloud-contaminated pixels from NOAA-14/AVHRR datime imagery over land using different combination of channels. This algorithm has two phases : the first is to make a cloud-free characteristic data of land surface using compositing techniques from channel 1 and 5 imagery and a dynamic threshold of brightness temperature, and the second is to identify the each pixel as a cloud-free or cloudy one through 4-step threshold tests. The merits of this method are its simplicity in input data and automation in determining threshold values. The threshold of infrared data is calculated through the combination of brightness temperature of land surface obtained from AVHRR imagery, spatial variance of them and temporal variance of observed land surface temperature. The method detected the could-comtaminated pixels successfully embedded inthe NOAA-14/AVHRR daytime imagery for the August 1 to November 30, 1996 and March 1 to July 30, 1997. This method was evaluated through the comparison with ground-based cloud observations and with the enhanced visible and infrared imagery.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2651-2673
• /
• 2019

In order to detect Denial of Service (DoS) attacks, victim-side detection methods are used popularly such as static threshold-based method and machine learning-based method. However, as DoS attacking methods become more sophisticated, these methods reveal some natural disadvantages such as the late detection and the difficulty of tracing back attackers. Recently, in order to mitigate these drawbacks, source-side DoS detection methods have been researched. But, the source-side DoS detection methods have limitations if the volume of attack traffic is relatively very small and it is blended into legitimate traffic. Especially, with the subtle attack traffic, DoS detection methods may suffer from high false positive, considering legitimate traffic as attack traffic. In this paper, we propose an effective source-side DoS detection method with traffic seasonality aware adaptive threshold. The threshold of detecting DoS attack is adjusted adaptively to the fluctuated legitimate traffic in order to detect subtle attack traffic. Moreover, by understanding the seasonality of legitimate traffic, the threshold can be updated more carefully even though subtle attack happens and it helps to achieve low false positive. The extensive evaluation with the real traffic logs presents that the proposed method achieves very high detection rate over 90% with low false positive rate down to 5%.