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EXAMPLES APPLICATIONS FAQ INDEX INTEGRATORS LINKS SALES
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Other WAY-2C Applications |
Multi-Spectral and/or Multi-Sensor Fusion
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The normal human vision system has the remarkable ability to
classify objects based on the distribution in space of
the red, green, and blue color components of light emanating from the objects.
This ability can be exploited to classify based on the distribution of
any three variables by creating a "false color" image in which
each variable is assigned to a different color component. For example, this false color image results from the combination of images from two different sensors in a simulated baggage inspection system. Suspicious organic materials appear reddish brown, automatically detectable by WAY-2C. The interpretated image highlights such regions in red. It can also trigger an alarm signal when a specified color class is detected. WAY-2C's proven "minimum description" classification method makes it easy to automate image classification based on the spatial distribution of up to three variables. The variables can represent virtually any properties, whether from multi-spectral or multi-sensor imaging devices, or even maps of such properties as topography, temperature, strain and/or density .
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Relevance Spectroscopy -Hyperspectral Image Analysis
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We have developed an automated method of rapidly determining the optimum combination of available sensor variables to differentiate any particular set of target classes. This method, which we refer to as relevance spectroscopy has been demonstrated by experimental studies on a variety of multispectral and hyperspectral aircraft and satellite images. The resulting combination is suitable for analysis by traditional and/or our minimum description methods. The upper image was created from a set of three monochrome images automatically chosen from a set of over two hundred images, each representing a different spectral band. In this case the band combination was chosen to optimize differentiation of certain water and shoreline classes while surpressing differentiation of land vegetation classes. Training regions for each class are outlined by the red, yellow, purple, and blue rectangles with each color representing a different class. Note how land vegetat ion regions are combined into a single class whose training regions are outlined by the green rectangles. The resulting WAY-2C interpretation is shown in the lower image. If the objective had been to distinguish a variety of land-based vegetation classes the system would have automatically selected an entirely different set of spectral bands. |
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Surveillance
WAY-2C's ability to be instantly trained to meet new conditions, combined with its ability to recognize anomalous color distributions in all or selected parts of an image, makes it ideal for automatic anomaly detection in video surveillance systems.Thermal Transient Analysis
By assigning thermal images from a single scene obtained at different times to different image planes, WAY-2C can classify regions of the resulting "color" images on the basis of their thermal properties.Live Clams and Mud Clams
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One of these clams is alive, the other is a "mudder", dead and partially filled with mud. Both have about the same density.
Can you tell them apart? Not from this image! However, we have invented a proprietary method for differentiating them. If you process millions of clams or similar shellfish per day, and are interested in automatically detecting and rejecting the dead ones, please
contact us.
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Best Focus and Other Things
The principles behind WAY-2C are not limited to image analysis. In fact, we first demonstrated the approach for automatic classification of sea-floor topography from sonar data, and for language identification from text files. Since then we have developed techniques for automatically determining best focus, as well as a variety of powerful methods for automatic analysis and classification of images and other data sets.To inquire about any of these, contact support@way2c.com .