Abstract: Methods for dimensionality reduction of large data volumes, in particular hyper-spectral data cubes, include providing a dataset ? of data points given as vectors, building a weighted graph G on ? with a weight function w?, wherein w? corresponds to a local coordinate-wise similarity between the coordinates in ?; obtaining eigenvectors of a matrix derived from graph G and weight function w?, and projecting the data points in ? onto the eigenvectors to obtain a set of projection values ?B for each data point, whereby ?B represents coordinates in a reduced space. In one embodiment, the matrix is constructed through the dividing each element of w? by a square sum of its row multiplied by a square sum of its column. In another embodiment the matrix is constructed through a random walk on graph G via a Markov transition matrix P, which is derived from w?. The reduced space coordinates are advantageously used to rapidly and efficiently perform segmentation and clustering.

Abstract: An apparatus and method for multispectral imaging comprising an array of filters in a mosaic pattern, a sensor array and an acquisition and processing module. The sensor array being disposed to receive an image that has been filtered by the array of filters. The acquisition and processing module processes the output of the sensor array (or mosaic acquired data) to provide a processed image. The acquisition and processing module processes the mosaic acquired data by performing first interpolation on the mosaic acquired data by the sensor array to provide a first approximation and performing second interpolation on the values of the first approximation to provide a second approximation.

Abstract: Methods for dimensionality reduction of large data volumes, in particular hyper-spectral data cubes, include providing a dataset ? of data points given as vectors, building a weighted graph G on ? with a weight function w?, wherein w? corresponds to a local coordinate-wise similarity between the coordinates in ?; obtaining eigenvectors of a matrix derived from graph G and weight function w?, and projecting the data points in ? onto the eigenvectors to obtain a set of projection values ?B for each data point, whereby ?B represents coordinates in a reduced space. In one embodiment, the matrix is constructed through the dividing each element of w? by a square sum of its row multiplied by a square sum of its column. In another embodiment the matrix is constructed through a random walk on graph G via a Markov transition matrix P, which is derived from w?. The reduced space coordinates are advantageously used to rapidly and efficiently perform segmentation and clustering.

Abstract: An apparatus and method for multispectral imaging comprising an array of filters in a mosaic pattern, a sensor array and an acquisition and processing module. The sensor array being disposed to receive an image that has been filtered by the array of filters. The acquisition and processing module processes the output of the sensor array (or mosaic acquired data) to provide a processed image. The acquisition and processing module processes the mosaic acquired data by performing first interpolation on the mosaic acquired data by the sensor array to provide a first approximation and performing second interpolation on the values of the first approximation to provide a second approximation.