A general paradigm for lifting binary morphological algorithms to fuzzy algorithms is employed to construct fuzzy versions of classical binary morphological operations. The lifting procedure is based upon an epistemological interpretation of both image and filter fuzzification. Algorithms are designed via the paradigm for various fuzzifications and their performances are analyzed to provide insight into the kind of liftings that produce suitable results. Algorithms are discussed for three image processing tasks: shape detection, edge detection, and clutter removal. Detailed analyses are given for the effect of noise and its mitigation owing to fuzzy approaches. It is demonstrated how the fuzzy hit-or-miss transform can be used in conjunction with a decision procedure to achieve word recognition.
The automated detection of sea mines remains an increasingly important humanitarian and military task. In recent years, research efforts have been concentrated on developing algorithms that detect mines in complicated littoral environments. Acquired high-resolution side-looking sonar images are often heavily infested with artifacts from natural and man-made clutter. As a consequence, automated detection algorithms, designed for high probability of detection, suffer from a large number of false alarms. To remedy this situation, sophisticated feature extraction and pattern classification techniques are commonly used after detection.In this paper, we propose a nonlinear detection algorithm, based on mathematical morphology, for the robust detection of sea mines. The proposed algorithm is fast and performs well under a variety of sonar modalities and operating conditions. Our approach is based on enhancing potential mine signatures by extracting highlight peaks of appropriate shape and size and by boosting the amplitude of the peaks associated with a potential shadow prior to detection. Signal amplitudes over highlight peaks are extracted using a flat morphological top-hat by reconstruction operator. The contribution of a potential shadow to the detection image is incorporated by increasing the associated highlight amplitude by an amount proportional to the relative contrast between highlight and shadow signatures. The detection image is then thresholded at mid-gray level. The largest p targets from the resulting binary image are then labelled as potential targets. The number of false alarms in the detection image is subsequently reduced to an acceptable level by a feature extraction and classification module.The detection algorithm is tested on two side-scan sonar databases provided by the Coastal Systems Station, Panama City, Florida: SONAR-0 and SONAR-3.
This paper presents a comprehensive discussion on the segmentation of mammograms using morphological texture features. These features are derived from morphological granulometries with various structuring elements. Each structuring element captures a speci c texture content. The segmentation is carried out in an unsupervised manner by applying the KL transform feature reduction and Voronoi clustering on the extracted morphological texture features. The evaluation of the segmentation outcome by a trained radiologist is provided.
An unsupervised iterative scheme is proposed for land mine detection in heavily cluttered scenes. This scheme is based on iterating hybrid multispectral filters that consist of a decorrelating linear transform coupled with a nonlinear morphological detector. Detections extracted from the first pass are used to improve results in subsequent iterations. The procedure stops after a predetermined number of iterations. The proposed scheme addresses several weaknesses associated with previous adaptations of morphological approaches to land mine detection. Improvement in detection performance, robustness with respect to clutter inhomogeneities, a completely unsupervised operation, and computational efficiency are the main highlights of the method. Experimental results reveal excellent performance.
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