Superellipse Fitting for the Recovery and Classification of Mine-Like Shapes in Sidescan Sonar Images

Inst. of Robot., Univ. de Valencia, Valencia
IEEE Journal of Oceanic Engineering (Impact Factor: 1.18). 11/2008; 33(4):434 - 444. DOI: 10.1109/JOE.2008.2002962
Source: IEEE Xplore


Mine-like object classification from sidescan sonar images is of great interest for mine counter measure (MCM) operations. Because the shadow cast by an object is often the most distinct feature of a sidescan image, a standard procedure is to perform classification based on features extracted from the shadow. The classification can then be performed by extracting features from the shadow and comparing this to training data to determine the object. In this paper, a superellipse fitting approach to classifying mine-like objects in sidescan sonar images is presented. Superellipses provide a compact and efficient way of representing different mine-like shapes. Through variation of a simple parameter of the superellipse function different shapes such as ellipses, rhomboids, and rectangles can be easily generated. This paper proposes a classification of the shape based directly on a parameter of the superellipse known as the squareness parameter. The first step in this procedure extracts the contour of the shadow given by an unsupervised Markovian segmentation algorithm. Afterwards, a superellipse is fitted by minimizing the Euclidean distance between points on the shadow contour and the superellipse. As the term being minimized is nonlinear, a closed-form solution is not available. Hence, the parameters of the superellipse are estimated by the Nelder-Mead simplex technique. The method was then applied to sidescan data to assess its ability to recover and classify objects. This resulted in a recovery rate of 70% (34 of the 48 mine-like objects) and a classification rate of better than 80% (39 of the 48 mine-like objects).

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    • "The results will be assessed by using the extracted superellipse parameters for a classification. The best version is afterwards compared with the method by [24] where the segmentation and superellipse fitting is divided into two steps, i.e. the image is first divided into shadow and non-shadow areas with an algorithm that does not take into account the superellipse a-priori knowledge. "
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    ABSTRACT: This paper proposes a new method for segmenting and classifying seamines on Synthetic Aperture Sonar (SAS) side scan images. The method uses an active contours approach and superellipse a-priori knowledge to segment the image in object, object-shadow and background areas. In contrast to other methods using superellipse constraints, the shape prior is incorporated directly into the segmentation process. This kind of segmentation has the advantage that afterwards the extracted superellipse parameters that describe the object and the object-shadow can directly be used as feature for a classification - this work is hence also of potential interest for general object recognition tasks in other application domains. Several different perspectives of implementing this idea into a suitable algorithm are introduced and compared with each other. Thus, for the evaluation of each method the extracted superellipse features are used for a support vector machine classification. An one against all confusion matrix is generated on a test data set. This result is compared to a related state of the art algorithm. It is shown that our new method is able to correctly classify 170 of 210 objects in a very challenging real world data set and that it yields significant better results than the state of the art comparison.
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    • "나 Superelipse Fitting [4] "
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    • "The Lamé curve is extended by one skew factor and expressed in a parametric form as x1,Lc(t) = a cos ǫ t, x2,Lc(t) = b sin ǫ t + m x1,Lc(t), (3) where a and b are the x1-and x2-intercepts, t ∈ R, ǫ is known as the squareness of the Lamé curve, which controls curve deformations at the corners and m denotes the skew in x2-direction. In [9] Lamé curves are considered in a similar context to be fitted to extracted contours, where only symmetric objects are allowed. Our new approach accomplishes for each quadrant an independent fit, which allows also non-symmetric objects to appear, like more complicated objects or non-symmetric shapes occurred due to environmental effects (e.g. "
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