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About 10,000 papers for “threshold algorithm”

Threshold extended ID3 algorithm

A. B. Rajesh Kumar, C. Ramesh, E. Madhusudhan et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012

Information exchange over insecure networks needs to provide authentication and confidentiality to the database in significant problem in datamining. In this paper we propose a novel authenticated multiparty ID3 Algorithm used to construct multiparty secret sharing decision tree for implementation in medical transactions.

Cited by 6

Adaptive threshold algorithm functioning.

Denis Pavliha (440915), Maja M. Mušič (440916), Gregor Serša (4743132) et al. · Figshare · 2015

<p>Demonstrated is the functioning of the algorithm comparing a slice while sweeping the intensity threshold value; presented are three examples where the intensity threshold value is set to 15.000 bits and similarity is 0.12 (A), 25.000 bits and similarity is 0.81 (B) and 35.000 bits and similarity is 0.95 (C); the comparison is done to the previous already-segmented slice (D).</p>

Cited by 0Open access

Layered FDR Threshold Filtering Algorithm

Huang Yong-dong · 2011

Based on the FDR threshold filtering algorithm,a layered FDR threshold filtering algorithm is put forward.Through respectively making multiple hypotheses testing to the low frequency coefficients at the last level and the high frequency coefficients at other levels,multiple threshold values are obtained.The gained threshold values are taken as the criterion at the individual level.Simulation results for signals of heavysine,bumps,doppler,blocks show the proposed threshold algorithm is better than universal threshold,FDR and free distributed FDR threshold filtering algorithm in signal to noise ratio,relative error and so on.It has higher practical value.

Cited by 0

A Parallel Threshold Selection Algorithm

J. Illingworth, Josef Kittler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1986

The RATS algorithm is a gray level threshold selection method which uses simple image statistics consisting of sums of products of gray level and gradient information accummulated over appropriate image areas. The method calculates a threshold in a very direct manner and avoids the often unreliable analysis of a gray level histogram. The contribution of individual pixels are largely local and independent and the method is therefore particularly well suited for implementation on a parallel hierarchic processor array such as a quadtree pyramid. Simple control strategies can be devised to select the most appropriate thresholds for individual pixels from thresholds at a series of different spatial scales. In this paper we consider how a SIMD processor array can be used to efficiently implement the RATS algorithm.

Cited by 4

Automatic DarkAdaptation Threshold Detection Algorithm

Dario F. G. de Azevedo, Sérgio Helegda, F. Glock et al. · 2005

This paper describes an algorithm used to automatically determine the threshold sensitivity in a new dark adaptometer. The new instrument is controlled by a personal computer and can be used in the investigation of several retinal diseases. The stimulus field is delivered to the eye through the modified optics of a fundus camera. An automated light stimulus source was developed to operate together with this fundus camera. New control parameters were developed in this instrument to improve the traditional Goldmann-Weekers dark adaptometer.

Cited by 1

Threshold Selection Using Competitive Algorithm

Jinguo He · 2010

Automatic threshold selection is significant in image segmentation, understanding and recognition. Finding global minimum of the histogram is difficult because many algorithms fail to end in a local minimum. A new competitive algorithm is proposed to automatically select threshold based on histogram analysis. Given a random initial value, the new algorithm begins to renew the value towards global minimum without stopping at local minimum by a competitive scheme. With the new algorithm, the briefness, low computation, and stability characteristic of threshold segmentation is reserved.

Cited by 0

Clustering Method Combining Threshold Algorithm with Ant Colony Algorithm

Zhang Zhao-tao · 2006

To improve the quality of clusting analysis,a novel clustering method combining the threshold algorithm with the ant colony algorithm was proposed.With this method,the center and number of clustering are determined by using the clustering algorithm based on threshold,and then the above clustering results are optimized by the K-means algorithm combining with transition probability based on the ant colony algorithm.The experimental results show that the proposed clustering method has a higher F-measure than the K-means and other algorithms.

Cited by 1

Comparison of Swedish Interactive Threshold Algorithm and Full Threshold Algorithm for Glaucomatous Visual Field Loss

Yuki Aoki, Genichiro Takahashi, Kenji Kitahara · European Journal of Ophthalmology · 2007

Glaucomatous defects were measured as being significantly shallower and larger with SS than with FT. In addition, the prevalence of visual field defect was higher with SS according to some of the criteria for glaucomatous visual field defects. These results might be related to the fact that SS strategy has a lower variability and to the Bayesian statistical properties of the SITA algorithm.

Cited by 16

Fast algorithm of WKS entropy threshold

Yang Shu · Shenyang Gongye Daxue xuebao · 2006

According to the characteristic that the Shannon entropy function achieves the maximum value in the equal probability field,a fast algorithm of WKS entropy threshold,which featured by concise objective function and definite meaning,is presented.The algorithm uses subtraction instead of logarithm and multiplication to get WKS entropy threshold.The segmentation effect of the proposed algorithm is proved theoretically to be equal to that of WKS entropy threshold.The experimental results show that operation speed gets increased.The algorithm can be considered as a faster operation method of WKS threshold selection, keeping the same segmentation effect as the WKS entropy threshold.

Cited by 0

Improved Differential Threshold ECG Detection Algorithm

Yang Liu · 2011

This paper introduces a algorithm of ECG detection based on differential slope and the energy window,for the limitations caused by the fixed threshold of differential slope method.It can select the threshold adaptively and dynamically on the characteristics of the current signal,to improve the adaptability and accuracy of the algorithm.The data from the MIT-BIH QT database and some clinical data is validated to test and verify the improved algorithm.Experimental results show that the improved algorithm improves the detection accuracy of the waves,the errors of the wave position are reduced under 30 milliseconds or less,and with a strong anti-interference ability for large P(T) and double P(T) waves.

Cited by 0

Adaptive multi-threshold image segmentation algorithm

LONG Jianwu, LI Jihao, ZENG Shuifei · SHILAP Revista de lepidopterología · 2025

Aiming at the issues that most existing multi-threshold segmentation methods struggle to accurately locate the optimal threshold combination and face soaring computational complexity with more thresholds, an adaptive multi-threshold image segmentation algorithm (AMTISA) was proposed. First, histograms were smoothed with bilateral filtering and a valley-bottom screening strategy was used to shrink the threshold search space. Then, the multi-threshold search was turned into a matrix extreme value search using dynamic programming. By using quadrilateral inequality properties, a divide-and-conquer approach found the cost matrix’s maximum value, boosting search efficiency. A target function based on histogram valley features was constructed to automatically determine the optimal number of segmentation classes. The optimal numbers from RGB channels merged for best color image segmentation threshold. Finally, experiments on BSDS500 and MSRC datasets confirm the algorithm’s effectiveness and adaptability.

Cited by 0Open access

Threshold extension of the MUSIC algorithm

Yuming Zhao, Zhaoda Zhu · 2002

A noncoherent pulsed radar with a linear uniform array is assumed. An attempt is made to use reference noise samples to improve the performance of estimating the direction of targets. Three methods, MMUSIC, QMUSIC, and QQMUSIC, are presented for extending the threshold characteristics of the multiple signal characterization (MUSIC) algorithm. Computer simulation results show that about a 3-dB threshold extension over the MUSIC method can be obtained in the case of low SNR (signal-to-noise ratio) and a small number of snapshots against a background of white noise. The computational complexity required for these three methods over MUSIC is negligible.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 0

Multi-threshold fuzzy clustering sorting algorithm

Jiawei Wang, Changbo Hou, Fuxin Qu · 2017 Progress In Electromagnetics Research Symposium - Spring (PIERS) · 2017

Radar signal sorting plays an important part in the electronic countermeasure field, which is used to extract and analyze parameters information of radar pulse signals. This paper introduces Multi-threshold Fuzzy Clustering Sorting Algorithm based on Fuzzy Clustering Sorting Algorithm, which focuses on the similarity of the radar pulses signal parameters to complete the process of sorting. The choice of the threshold value is the core part of the algorithm. The high threshold lets the pulses from the identical radar which have the large difference of parameter information classify the different sorts. On the contrary, the low threshold lets the pulses from the different radar units which have the similar parameter information classify the identical sort. To salve the problem that the choice of the threshold can't be decided, in the modified algorithm, the concept of using the auto-adaptive threshold to realize radar signal sorting is brought forward, which has more practical meaning. At the same time, the thought of calculating the level difference pulse signal arrival time is put forward, which is used to judge whether the radar pulses come from the same radar transmitter after the choice of threshold. When the pulses of calculating the arrival time of the level difference don't belong to the same radar signal, so feedback the threshold value immediately and reduce the threshold value automatically until the completion of the pulse radar signal sorting. This method shortens the time of the sorting process and validates the result of sorting greatly. The simulation results show the effectiveness of the algorithm.

Cited by 12

Lattice-based Threshold Secret Sharing Algorithm

ZHANG Hongjun,LIU Ke,MOU Zhansheng · SHILAP Revista de lepidopterología · 2016

The secret sharing algorithm based on the traditional mathematical assumption is difficult to resist the attack of the quantum algorithm.Therefore,a lattice-based threshold secret sharing algorithm is proposed.The basic process of the secret sharing strategy is analyzed,which is divided into public key generation,share generation and secret reconstruction.The threshold secret sharing algorithm based on lattice is discussed.The algorithm can be reduced to the closest vector problem,and its security is analyzed.Calculation results prove that the algorithm is correct and safe.

Cited by 0Open access