&lt;title&gt;Ship silhouette recognition using principal components analysis&lt;/title&gt;

Gouaillier, Valerie; Gagnon, Langis

doi:10.1117/12.279572

Cited by 27 publications

(13 citation statements)

References 1 publication

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“…Gibbins et al (1999) classified radar images of ships in using global features but their accuracy is so far unimpressive. Gouaillier and Gagnon (1997) classified radar images using principal-component global features and similarity matching to prototypical ships for each ship type to obtain promising results. Alippi (1995) used neural networks applied directly to the image pixels.…”

Section: Introductionmentioning

confidence: 99%

Robust Recognition of Ship Types from an Infrared Silhouette

Alves¹,

Herman²,

Rowe

2004

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Accurate identification of unknown contacts crucial in military intelligence. Automated systems that quickly and accurately determine the identity of a contact could be a benefit in backing up electronic-signals identification methods. This work reports two experimental systems for ship classification from infrared FLIR images. In an edge-histogram approach, we used the histogram of the binned distribution of observed straight edge segments of the ship image. Some simple tests had a classification success rate of 80% on silhouettes. In a more comprehensive neuralnetwork approach, we calculated scale-invariant moments of a silhouette and used them as input to a neural network. We trained the network on several thousand perspectives of a wire-frame model of the outline of each of five ship classes. We obtained 70% accuracy with detailed tested on real infrared images but performance was more robust than with the edge-histogram approach.

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Section: Introductionmentioning

confidence: 99%

Robust Recognition of Ship Types from an Infrared Silhouette

Alves¹,

Herman²,

Rowe

2004

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“…The question of whether image processing techniques can determine whether a given photo is a good one of the ship being targeted is a relatively difficult challenge, which would require the ability to recognize pixels associated with the ship in the image [4], [5], [6] . Far simpler is the related question of whether the photograph itself is of good quality (not blurred, high contrast, correctly exposed and so on).…”

Section: Discussionmentioning

confidence: 99%

Automated ship image acquisition

Hammond

2008

SPIE Proceedings

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The experimental Automated Ship Image Acquisition System (ASIA) collects high-resolution ship photographs at a shore-based laboratory, with minimal human intervention. The system uses Automatic Identification System (AIS) data to direct a high-resolution SLR digital camera to ship targets and to identify the ships in the resulting photographs. The photo database is then searchable using the rich data fields from AIS, which include the name, type, call sign and various vessel identification numbers. The high-resolution images from ASIA are intended to provide information that can corroborate AIS reports (e.g., extract identification from the name on the hull) or provide information that has been omitted from the AIS reports (e.g., missing or incorrect hull dimensions, cargo, etc). Once assembled into a searchable image database, the images can be used for a wide variety of marine safety and security applications. This paper documents the author's experience with the practicality of composing photographs based on AIS reports alone, describing a number of ways in which this can go wrong, from errors in the AIS reports, to fixed and mobile obstructions and multiple ships in the shot. The frequency with which various errors occurred in automatically-composed photographs collected in Halifax harbour in winter time were determined by manual examination of the images. 45% of the images examined were considered of a quality sufficient to read identification markings, numbers and text off the entire ship. One of the main technical challenges for ASIA lies in automatically differentiating good and bad photographs, so that few bad ones would be shown to human users. Initial attempts at automatic photo rating showed 75% agreement with manual assessments.

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“…Gouaillier recognize the ships using principal component analysis (PCA) method [1] . Serretta achieves the implementation and use of the mellin transform and neural networks for radar ship target recognition [2] .…”

Section: Introductionmentioning

confidence: 99%

Simulated ship recognition using two-dimensional PCA

et al. 2007

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This paper proposes a fast and robust algorithm for classification and recognition of ships based on the two-dimensional Principal Component Analysis (2DPCA) method. The three-dimensional ship models achieve by modeling software of MultiGen, and then they are projected by Vega simulating software for two-dimensional ship silhouettes. The 2DPCA method as against conventional PCA method for simulated ship recognition using training and testing experiments, as the training and testing sample size is large, and there are great variations in different azimuth and elevation for ship viewpoints. The experiment of ship recognition using the global feature of ships is not satisfied with us, so we proposed an improved 2DPCA method based on the local feature of ships. Some recognition results from simulated data are presented, it shows that the improved 2DPCA method outperform PCA in ship recognition and also superior to PCA in terms of computational efficiency for feature extraction. So our method is more preferable for ship classification and recognition.

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<title>Ship silhouette recognition using principal components analysis</title>

Cited by 27 publications

References 1 publication

Robust Recognition of Ship Types from an Infrared Silhouette

Robust Recognition of Ship Types from an Infrared Silhouette

Automated ship image acquisition

Simulated ship recognition using two-dimensional PCA

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