Abstract: Described embodiments detect changes in a scene over time using first and second images of the scene. A non-coherent intensity change detector detects large-scale changes between the first image and the second image and generates a large-scale change value for pairs of corresponding pixel locations in the first and second images. If the large-scale change value for a given pair of pixel locations reaches a threshold, a coherent change detector is used to detect small-scale changes between the first and second images. A small-scale change value is generated for the given pairs of pixel locations in the images. A composite change value is generated by combining the large-scale change value and the small-scale change value for each pixel pair. The change thresholds are used to determine whether a change in the scene has occurred over the time period.

Abstract: Described embodiments detect changes in a scene over time using first and second images of the scene. A non-coherent intensity change detector detects large-scale changes between the first image and the second image and generates a large-scale change value for pairs of corresponding pixel locations in the first and second images. If the large-scale change value for a given pair of pixel locations reaches a threshold, a coherent change detector is used to detect small-scale changes between the first and second images. A small-scale change value is generated for the given pairs of pixel locations in the images. A composite change value is generated by combining the large-scale change value and the small-scale change value for each pixel pair. The change thresholds are used to determine whether a change in the scene has occurred over the time period.

Abstract: The present invention relates to a machine implemented method for spectral analysis that determines a measure of cross coherence between application of two spectral estimation filters to data; and identifies a spectral feature of the measure of cross coherence. One example embodiment of the present invention provides a complete statistical summary of the joint dependence of the Bartlett and Capon power spectral statistics, showing that the coupling is expressible via a 2×2 complex Wishart matrix, where the degree coupling is determined by a single measure of cross coherence defined herein. This measure of coherence leads to a new two-dimensional algorithm capable of yielding significantly better resolution than the Capon algorithm, often commensurate with but at times exceeding finite sample based MUSIC.

Abstract: The present invention relates to a machine implemented method for spectral analysis that determines a measure of cross coherence between application of two spectral estimation filters to data; and identifies a spectral feature of the measure of cross coherence. One example embodiment of the present invention provides a complete statistical summary of the joint dependence of the Bartlett and Capon power spectral statistics, showing that the coupling is expressible via a 2×2 complex Wishart matrix, where the degree coupling is determined by a single measure of cross coherence defined herein. This measure of coherence leads to a new two-dimensional algorithm capable of yielding significantly better resolution than the Capon algorithm, often commensurate with but at times exceeding finite sample based MUSIC.