Gerbil consists of an interactive visualization tool targeted at multispectral and hyperspectral image data, and a toolbox of common algorithms, e.g. for segmentation. Multispectral imaging has been gaining popularity and has been gradually applied to many fields besides remote sensing. However, due to the high dimensionality of the data, both human observers and computers have difficulty interpreting this wealth of information. Gerbil facilitates the visualization of the relationship between spectral and topological information in a novel fashion. It puts emphasis on the spectral gradient, which is shown to provide enhanced information for many reflectance analysis tasks. It also includes a rich toolbox for evaluation of image segmentation and other algorithms in the multispectral domain. The parallel coordinates visualization technique is combined with hashing for a highly interactive visual connection between spectral distribution, spectral gradient, and topology.
|Tags||Image Processing Visualization Scientific/Engineering Multispectral research|
|Operating Systems||POSIX Mac OS X Linux Windows|
|Implementation||C++ Qt 4 OpenCV|
Release Notes: This release adds more and better segmentation functionality (including unsupervised segmentation), compatibility with current libraries and compilers, and a variety of other improvements and bugfixes.
Release Notes: This release is an important step for Gerbil to be used in productive environments. Drawing speed is increased by magnitudes. Drawing quality has improved significantly, especially when multiple labels are used during the session. There's been an overall polish of the software, mostly in respect to its user interface.
Release Notes: Two important new functionalities are introduced in this release. First, gerbil now comes with a module for multispectral edge detection. Second, gerbil now provides a flexible commandline interface. It allows running algorithms in a batch, which is very valuable for benchmarking purposes.
Release Notes: This release makes gerbil more useful for common tasks and enlarges its scope. You can now import and export labeling images. Newly introduced handling of data normalization and other usability enhancements greatly facilitate the visual analysis of image data. Landsat images (.lan format) are now supported for reading. Many bugs were fixed, and the internal API is more powerful and better documented.
Release Notes: Compatibility with recent OpenCV and Boost libraries.