Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vectorized image segmentation via trixel agglomeration
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
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An automatic approach for delineation of areas representing different acoustic facies is presented. A backscatter mosaic (a gray-scale image) is oversegmented, honoring all possible boundaries, both real and false (acquisition and construction artifacts), in order to separate relatively homogeneous and contiguous groups of pixels of the mosaic, which are called segments. The size of the segments is chosen such that each one is considered to represent a single acoustic facies. These segments are then joined together to end up with a predefined number of ''acoustic themes,'' in a process called coalescence. The difference between ''facies'' and ''theme'' is that the former represents an abstract type of seafloor, and the latter, an area (or areas) selected manually or automatically with common seafloor properties. Ideally, one theme would correspond to a single facies. Themes that are used for assignment to segments are chosen prior to coalescence, either manually or by one of the three automatic methods proposed in the paper. The process of coalescence of segments is based on all acoustic data associated with each segment (not just data used for mosaic construction), their proximity and shape. Results of segmentation are unbiased but depend on a specific goal that the user is trying to achieve.