Benjamin Lehmann

Extended active contours approach for mine detection in synthetic aperture sonar images

In this paper the problem of contour extraction in sonar images is addressed. We talk in this context about naval mines placed on the seafloor which are still a vast restraint in civil and military shipping. This potential risk is typically encountered by advanced sonar signal processing techniques and a huge amount of human interactions. To reduce at least the human interactions an automatic procedure is desired. Therefore we introduce a novel automatic target extraction algorithm based on active contours employing a specific shadow locating energy motivated by our experiments. Additionally we use a K-means based thresholding process and a Kolmogorov Smirnov (KS) test for improving the initial guess and therefore optimizing the overall performance.

Segmentation and classification using active contours based superellipse fitting on side scan sonar images for marine demining

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.

Optimal parameters for bilateral filtering and SAS image denoising

Bilateral filtering is a robust denoising method widely used for image preprocessing. Due to the varying properties of the noise, the selection of appropriate parameters for bilateral filtering becomes an important task. In this paper we address this problem by analyzing the performance of bilateral filtering using as a quality measure the VRMSE. First, we propose general rules for choosing suitable bilateral filtering parameters. These rules are then refined to an approach that allows one to determine optimal filter parameters if the standard deviation of the additive noise and a ground truth are known. Finally, we apply this approach to SAS images where the problem of a missing ground truth has been compensated by exploiting the similarity within a set of SAS images.

Seafloor classification for mine countermeasures operations using synthetic aperture sonar images

The performance of an automatic target recognition (ATR) system in the context of naval mine detection is severely affected by the underwater environment. Especially in regions with the presence of sand ripples or mine-sized stones the number of false alarms can become unacceptable high, if the detection algorithm does not account for the type of seafloor. Therefore, a robust way of discriminating between the three most important types of seafloor in the context of mine countermeasures (MCM), flat bottom, rocky bottom and sand ripples, is needed. In this paper five handcrafted features for seafloor classification from the literature are analysed and compared to the performance of a convolutional neural network (CNN) that learns the relevant features from the training data. The evaluation data was collected with an autonomous underwater vehicle (AUV) equipped with an synthetic aperture sonar. Experiments showed that the CNN approach outperformed a support vector machines classifier with the handcrafted features, albeit slightly, with a classification rate of 98.7% over 9420 examples.

Coupled curve evolution equations for ternary images in sidescan-sonar images guided by Lamé curves for object recognition

This paper proposes a new level set segmentation method which is guided by a parametric prior force based on Lamé curves. The use of prior knowledge is advantageous in order to improve the segmentation results in terms of matching expected object types because one can in general state that for different applications some shapes are more likely than others. By avoiding complex shape training processes the level set idea is extended with a parametric prior shape which forces the level set evolution to propagate towards the desired objects by not overpowering the image properties. Also a cross talk evolution is discussed for ternary images to handle correlations between adjacent or correlated objects.