Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
On active contour models and balloons
CVGIP: Image Understanding
Active shape models—their training and application
Computer Vision and Image Understanding
Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Monitoring event-based suspended sediment concentration by artificial neural network models
WSEAS Transactions on Computers
A new version of Flusser moment set for pattern feature extraction
WSEAS Transactions on Information Science and Applications
Transducers and Arrays for Underwater Sound
Transducers and Arrays for Underwater Sound
On the selection of an optimal wavelet basis for texture characterization
IEEE Transactions on Image Processing
Automated non-intrusive cargo inspection system using gamma-ray imaging (ROBOSCAN 1M)
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
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Automatic sea floor characterization is mainly based on the signal or image processing of the data acquired using an active acoustic system called sediment sonar. Each processing method suits a specific type of sonar, such as the monobeam, the multibeam, or the side-scan sonar. Most types of sonar offer a two dimensional view of the sea floor surface. Therefore, a high resolution image results which can be further analyzed. The inconvenient is that the sonar cannot view inside of the sea floor for a deeper analysis. Therefore, lower frequency acoustic systems are used for in-depth sea floor penetration (boomer, sparker, airguns or sub-bottom profilers). In this case, a mono dimensional signal results. Previous studies on the low-frequency systems are mainly based on the visual inspection by a geological human expert. To automatize this process, we propose the use of feature sets based on the transposed expert fuzzy reasoning. Two features are extracted, the first based on the sea floor contour and the second based on the sub-bottom sediment texture.