&lt;title&gt;Building And Bridge Classification By Moment Invariants&lt;/title&gt;

Gilmore, John F.; Boyd, W. W.

doi:10.1117/12.932839

Cited by 6 publications

(2 citation statements)

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“…However, despite the fact that automatic ship classification brings out interesting scientific challenges, very few results are reported in the open literature [1][2][3][4][5][6][7][8][9][10][11][12][13] .From the literatures, we know that the methods of ship classification are mostly based on signals from radar, infrared or ultrasonic. However, the three methods need complex equipments, costly price.…”

Section: Introductionmentioning

confidence: 99%

Automatic ship target classification based on aerial images

Lan

Wan

2008

SPIE Proceedings

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As the important reconnaissance and offensive weapon in future battlefield, Micro Aerial Vehicle (MAV) is applied more and more widely in civil and military field. In the sea battlefield, ship classification applied to MAV could effectively realize signals collection, force protection and strike to ship targets. At present, methods of ship classification are mostly based on signals from radar, infrared or ultrasonic. However, because of large volume and complex equipments, these methods can't meet the requirement of MAV. Thus, ship classification based on visible sensor is chosen and it could solve volume and weight limits of MAV. In order to realize ship classification in MAV, ship classification based on aerial images is first proposed and an effective robust algorithm for classification based on modified Zernike moment invariants is proposed in this paper. The task of classification is that the ships are classified into two categories, aircraft carrier and chaser. The experimental results show that the correct classification rate is more than 92% and the algorithm proposed is effective to solve classification problem for ship targets in MAV.

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Section: Introductionmentioning

confidence: 99%

Automatic ship target classification based on aerial images

Lan

Wan

2008

SPIE Proceedings

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“…The low order moments can be used to obtain the mean, variance, skewness and kurtosis of the distribution of the luminance intensity in an image [1], [2] . Moments have been applied in many types of applications such as object classification [2], [3], [4], [5], [6], [7], text recognition [8], pattern recognition [9], [10], content-based image retrieval [11], [12], image reconstruction [13], and recently, image quality assessment [14].…”

Section: Introductionmentioning

confidence: 99%

Plain, edge, texture (PET) block classifier using Tchebichef moments and SVM

Lim

Thung

et al. 2013

2013 International Symposium on Intelligent Signal Processing and Communication Systems

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This paper presents an image block classification method using Tchebichef moments (TMs) and support vector machine (SVM). The test images are divided into non-overlapping 16 × 16 blocks and transformed into moment domain using Discrete Tchebichef Transform. These moment features are then used in the image content (block) classification. SVM is used for learning and classifying the blocks into three types: "plain", "edge" and "texture", based on their moment energy level. Experimental results show that the proposed method works well and the classification accuracy is 98.7%.

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Premaratne¹

2014

Human Computer Interaction Using Hand Gestures

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<title>Building And Bridge Classification By Moment Invariants</title>

Cited by 6 publications

References 0 publications

Automatic ship target classification based on aerial images

Automatic ship target classification based on aerial images

Plain, edge, texture (PET) block classifier using Tchebichef moments and SVM

Feature Extraction

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