Optimal thresholding—A new approach

Snyder, Wesley E.; Bilbro, Griff L.; Logenthiran, Ambalavaner; Rajala, Sarah A.

doi:10.1016/0167-8655(90)90034-y

Cited by 77 publications

(33 citation statements)

References 6 publications

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“…3 depicts the inversed correlation coefficient between the estimated model of each aircraft, the mean model h i and the reference model h 0 . K-means algorithm 23 is then applied to determining the abnormal aircraft based on the correlation coefficient metric. The classification results obtained through K-means algorithm is shown in Fig.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

A data driven approach for detection and isolation of anomalies in a group of UAVs

Wang

2015

Chinese Journal of Aeronautics

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The use of groups of unmanned aerial vehicles (UAVs) has greatly expanded UAV's capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic) model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach. ª 2015 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.

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Section: Simulation Results and Analysismentioning

confidence: 99%

A data driven approach for detection and isolation of anomalies in a group of UAVs

Wang

2015

Chinese Journal of Aeronautics

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“…A variety of thresholding approaches have been proposed for image segmentation, including conventional methods (Guo & Pandit, 1998;Pal & Pal, 1993;Shaoo, Soltani, Wong, & Chen, 1988;Snyder, Bilbro, Logenthiran, & Rajala, 1990) and intelligent techniques such as in Chen and Wang (2005) and Chih-Chih (2006). Extending the algorithm to a multilevel approach may arise some inconveniences: (i) they may have no systematic and analytic solution when the number of classes to be detected increases and (ii) the number of classes is either difficult to be predicted or must be pre-defined.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-threshold segmentation approach based on differential evolution optimization

Cuevas

Zaldívar

Pérez‐Cisneros

2010

Expert Systems with Applications

131

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“…In literature relevant to Computer Vision, finding a threshold value is considered by some scientists as "Black Art" (Faugeras, 1993) while others proposed some techniques such as Fixed Thresholding (Savakis, 1998), Optimal Thresholding (Snyder et al, 1990), Otsu's Thresholding (Otsu, 1979). But none of these threshold finding techniques can be used as general techniques to find threshold value for all applications.…”

Section: Threshold Valuementioning

confidence: 99%

Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Al-Saqer¹

2011

American Journal of Agricultural and Biological Sciences

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Problem statement: Red palm weevil is the most destructive insect for palm trees all over the world. This research is part of developing an automated wireless red palm weevil detection and control system. The focus for this study was to develop red palm Weevil recognition system which can detect RPW in an image and can be used in wireless image sensor network which will be part of entire proposed system. Approach: Template based recognition techniques were used. Two general recognition methods i.e., Zernike and Regional Properties and an algorithm combining them were used. Besides that, a novel technique for detecting Rostrum of RPW named as 'Rostrum Analysis' was proposed and used for recognition, a conclusive algorithm based on all three techniques was also proposed, 319 test images of RPW and 93 images of other insects which found in RPW habitat were used. Results: It was found that both general techniques i.e., Regional Properties and Zernike Moments methods perform reasonably in recognizing RPW. The algorithm based on both these methods performs better than individual methods. The Rostrum Analysis outperforms better than both the earlier methods and proposed algorithm using all three analytical techniques gives best results among all discussed techniques in recognizing RPW as well as other insects. Conclusion: The most balanced and efficient recognition technique is to use the proposed conclusive algorithm which is combination of Regional Properties, Zernike Moments and Rostrum Analysis techniques. The maximum time for processing an image is 0.47 sec and the results obtained in recognizing the RPW and other insects are 97 and 88% respectively.

show abstract

Optimal thresholding—A new approach

Cited by 77 publications

References 6 publications

A data driven approach for detection and isolation of anomalies in a group of UAVs

A data driven approach for detection and isolation of anomalies in a group of UAVs

A novel multi-threshold segmentation approach based on differential evolution optimization

Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

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