5,402 research outputs found
Automated, objective texture segmentation of multibeam echosounder data - Seafloor survey and substrate maps from James Island to Ozette Lake, Washington Outer Coast
Without knowledge of basic seafloor characteristics, the ability to address any number of critical marine and/or coastal management issues is diminished. For example,
management and conservation of essential fish habitat (EFH), a requirement mandated by federally guided fishery management plans (FMPs), requires among other things a
description of habitats for federally managed species. Although the list of attributes important to habitat are numerous, the ability to efficiently and effectively describe many, and especially at the scales required, does not exist with the tools currently available. However, several characteristics of seafloor morphology are readily obtainable at multiple scales and can serve as useful descriptors of habitat. Recent advancements in acoustic technology, such as multibeam echosounding (MBES), can provide remote indication of surficial sediment properties such as texture, hardness, or roughness, and further permit highly detailed renderings of seafloor morphology. With acoustic-based surveys providing a relatively efficient method for data acquisition, there exists a need for
efficient and reproducible automated segmentation routines to process the data. Using MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), and
through a contracted seafloor survey, we expanded on the techniques of Cutter et al. (2003) to describe an objective repeatable process that uses parameterized local Fourier
histogram (LFH) texture features to automate segmentation of surficial sediments from acoustic imagery using a maximum likelihood decision rule. Sonar signatures and
classification performance were evaluated using video imagery obtained from a towed camera sled. Segmented raster images were converted to polygon features and attributed
using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999) for use in a geographical information system (GIS). (PDF contains 41 pages.
Popular Ensemble Methods: An Empirical Study
An ensemble consists of a set of individually trained classifiers (such as
neural networks or decision trees) whose predictions are combined when
classifying novel instances. Previous research has shown that an ensemble is
often more accurate than any of the single classifiers in the ensemble. Bagging
(Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two
relatively new but popular methods for producing ensembles. In this paper we
evaluate these methods on 23 data sets using both neural networks and decision
trees as our classification algorithm. Our results clearly indicate a number of
conclusions. First, while Bagging is almost always more accurate than a single
classifier, it is sometimes much less accurate than Boosting. On the other
hand, Boosting can create ensembles that are less accurate than a single
classifier -- especially when using neural networks. Analysis indicates that
the performance of the Boosting methods is dependent on the characteristics of
the data set being examined. In fact, further results show that Boosting
ensembles may overfit noisy data sets, thus decreasing its performance.
Finally, consistent with previous studies, our work suggests that most of the
gain in an ensemble's performance comes in the first few classifiers combined;
however, relatively large gains can be seen up to 25 classifiers when Boosting
decision trees
Learning to automatically detect features for mobile robots using second-order Hidden Markov Models
In this paper, we propose a new method based on Hidden Markov Models to
interpret temporal sequences of sensor data from mobile robots to automatically
detect features. Hidden Markov Models have been used for a long time in pattern
recognition, especially in speech recognition. Their main advantages over other
methods (such as neural networks) are their ability to model noisy temporal
signals of variable length. We show in this paper that this approach is well
suited for interpretation of temporal sequences of mobile-robot sensor data. We
present two distinct experiments and results: the first one in an indoor
environment where a mobile robot learns to detect features like open doors or
T-intersections, the second one in an outdoor environment where a different
mobile robot has to identify situations like climbing a hill or crossing a
rock.Comment: 200
Monocular Vision as a Range Sensor
One of the most important abilities for a mobile robot is detecting obstacles in order to avoid collisions. Building a map of these obstacles is the next logical step. Most robots to date have used sensors such as passive or active infrared, sonar or laser range finders to locate obstacles in their path. In contrast, this work uses a single colour camera as the only sensor, and consequently the robot must obtain range information from the camera images. We propose simple methods for determining the range to the nearest obstacle in any direction in the robotâs field of view, referred to as the Radial Obstacle Profile. The ROP can then be used to determine the amount of rotation between two successive images, which is important for constructing a 360Âș view of the surrounding environment as part of map construction
Segmentation invariante en rasance des images sonar latéral par une approche neuronale compétitive
The sidescan sonar records the energy of an emitted acoustical wave backscattered by the seabed for a large range of grazing angles. The statistical analysis of the recorded signals points out a dependence according grazing angles, which penalizes the segmentation of the seabed into homogeneous regions. To improve this segmentation, classical approaches consist in compensating artifacts due to the sonar image formation (geometry of acquisition, gains, etc.) considering a flat seabed and using either Lambertâs law or an empirical law estimated from the sonar data. The approach chosen in this study proposes to split the sonar image into stripes in the swath direction; the stripe width being limited so that the statistical analysis of pixel values can be considered as independent of grazing angles. Two types of texture analysis are used for each stripe of the image. The first technique is based on the Grey-Level Co-occurrence Matrix (GLCM) and various Haralick attributes derived from. The second type of analysis is the estimation of spectral attributes. The starting stripe at mid sonar slant range is segmented with an unsupervised competitive neural network based on the adaptation of Self- Organizing Feature Maps (SOFM) algorithm. Then, from the knowledge acquired on the segmentation of this first stripe, the classifier adapts its segmentation to the neighboring stripes, allowing slight changes of statistics from one stripe to the other. The operation is repeated until the beginning and the end of the slant range are reached. The study made in this work is validated on real data acquired by the sidescan sonar Klein 5000. Segmentation performances of the proposed algorithm are compared with those of conventional approaches.Un sonar latĂ©ral de cartographie enregistre les signaux qui ont Ă©tĂ© rĂ©trodiffusĂ©s par le fond marin sur une large fauchĂ©e. Les signaux sont ainsi rĂ©vĂ©lateurs de lâinteraction entre lâonde acoustique Ă©mise et le fond de la mer pour une large plage de variation de lâangle de rasance. Lâanalyse des statistiques de ces signaux rĂ©trodiffusĂ©s montre une dĂ©pendance Ă ces angles de rasance, ce qui pĂ©nalise fortement la segmentation des images en rĂ©gions homogĂšnes. Pour amĂ©liorer cette segmentation, lâapproche classique consiste Ă corriger les artefacts dus Ă la formation de lâimage sonar (gĂ©omĂ©trie dâacquisition, gains variables, etc.) en considĂ©rant un fond marin plat et en estimant des lois physiques (Lambert, Jackson, etc.) ou des modĂšles empiriques. Lâapproche choisie dans ce travail propose de diviser lâimage sonar en bandes dans le sens de la portĂ©e ; la largeur de ces bandes Ă©tant suffisamment faible afin que lâanalyse statistique de la rĂ©trodiffusion puisse ĂȘtre considĂ©rĂ©e indĂ©pendante de lâangle de rasance. Deux types dâanalyse de texture sont utilisĂ©s sur chaque bande de lâimage. La premiĂšre technique est basĂ©e sur lâestimation dâune matrice des cooccurrences et de diffĂ©rents attributs dâHaralick. Le deuxiĂšme type dâanalyse est lâestimation dâattributs spectraux. La bande centrale localisĂ©e Ă la moitiĂ© de la portĂ©e du sonar est segmentĂ©e en premier par un rĂ©seau de neurones compĂ©titifs basĂ© sur lâalgorithme SOFM (Self-Organizing Feature Maps) de Kohonen. Ensuite, la segmentation est rĂ©alisĂ©e successivement sur les bandes adjacentes, jusquâaux limites basse et haute de la portĂ©e sonar. A partir des connaissances acquises sur la segmentation de cette premiĂšre bande, le classifieur adapte sa segmentation aux bandes voisines. Cette nouvelle mĂ©thode de segmentation est Ă©valuĂ©e sur des donnĂ©es rĂ©elles acquises par le sonar latĂ©ral Klein 5000. Les performances de segmentation de lâalgorithme proposĂ© sont comparĂ©es avec celles obtenues par des techniques classiques
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