Recognition of insulator based on developed MPEG-7 texture feature

Li, Weiguo; Gao-sheng, YE; Huang, Feng; Wang, Shikun; Chang, Wenzhi

doi:10.1109/cisp.2010.5648283

Cited by 26 publications

(23 citation statements)

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“…We compare the proposed algorithm to the method-based on BOW and SIFT (SIFT-BOW), the method-based CSWT [2], the method-based MPEG-7 EHD technique (MPEG7-EHD) [3], and the method based on discriminative training of local gradient-based descriptors and a subsequent voting scheme Elliptical GLOH-like descriptor and Local Outlier Factor (EGL-LOF) [4]. For convenience, the proposed algorithm is represented as MSMF-SOF.…”

Section: B Detection Resultsmentioning

confidence: 99%

A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images

Liao

2015

IEEE Geosci. Remote Sensing Lett.

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The detection of targets with complex backgrounds in aerial images is a challenging task. In this letter, we propose a robust insulator detection algorithm based on local features and spatial orders for aerial images. First, we detect local features and introduce a multiscale and multifeature descriptor to represent the local features. Then, we get several spatial orders features by training these local features, it improves the robustness of the algorithm. Finally, through a coarse-to-fine matching strategy, we eliminate background noise and determine the region of insulators. We test our method on a diverse aerial image set. The experimental results demonstrate the precision and robustness of our detection method, and indicate the possible use of our method in practical applications.Index Terms-Aerial image, insulator detection, local feature, point matching, spatial orders. 1545-598X

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Section: B Detection Resultsmentioning

confidence: 99%

A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images

Liao

2015

IEEE Geosci. Remote Sensing Lett.

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“…The remaining (8-k) bits of 8-bit LDP pattern is set to 0. Finally, the LDP code is derived by (1). Fig.…”

Section: A Local Directional Pattern (Ldp)mentioning

confidence: 99%

“…Among the number of components in power line distribution systems, insulator is one of the key components for stable power supply. According to the statistical data of the national power company, the breakdown of insulator is the most frequent cause of the failure of power system [1]. Therefore, rigorous insulator inspection for defect identification is necessary to ensure an uninterrupted power distribution.…”

Section: Introductionmentioning

confidence: 99%

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Insulator Detection and Defect Classification using Rotation Invariant Local Directional Pattern

Jabid¹,

Ahsan²

2018

ijacsa

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Detecting power line insulator automatically and analyzing their defects are vital processes in maintaining power distribution systems. In this work, a rotation invariant texture pattern named rotation invariant local directional pattern (RI-LDP) is proposed for representing insulator image. For this at first, local directional pattern (LDP) is applied on image which can encode local texture pattern into an eight bit binary code by analyzing magnitude of edge response in eight different directions. Finally this LDP code is made robust to rotation by meticulously rearranging the generated another binary code which named as rotation invariant local directional pattern (RI-LDP). Insulator detection is carried out where this RI-LDP based histogram act as a feature vector and support vector machine (SVM) plays the role of the classifier. The detected insulator image region is further analyzed for possible defect identification. For this, an automatic extraction method of the individual insulator caps is proposed. The defect in segmented insulators is analyzed using LDP texture feature on individual cap region. We evaluated the proposed method using two sets of 493 real-world insulator images captured from a ground vehicle. The proposed insulator detector shows comparable performance to state-of-the-arts and our defect analysis method outperforms existing methods.

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“…But it may be unable to identify damaged insulator. Literature [4] proposes a method of insulator's feature extraction based on MPEG-7 edge directions. This method can recognize insulator effectively in the complex background.…”

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confidence: 99%

The Recognition and Localization of Insulators Based on SIFT and RANSAC ;

Zhao¹,

Liu²,

Yapeng³

2013

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

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Abstr act. The recognition and localization of insulators is a crucial process of insulators' failure detection, especially under the complex background. With unknown insulators in test image, a method of the recognition and localization of insulators based on SIFT and RANSAC is proposed as following: SIFT (Scale Invariant Feature Transform) is used to extract a set of feature points of each insulator template image and test image. Then use RANSAC (RANdom SAmple Consensus) to remove the outliers. And each template's matched inliers are accurate points which are used to compute affine transform matrix H and localize the template images in test image. Thus we can detect all the template images in test image correctly. Experimental results show that the proposed approach can obtain high precision in the recognition and localization of insulators under the complex background.

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Recognition of insulator based on developed MPEG-7 texture feature

Cited by 26 publications

References 0 publications

A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images

A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images

Insulator Detection and Defect Classification using Rotation Invariant Local Directional Pattern

The Recognition and Localization of Insulators Based on SIFT and RANSAC ;

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