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M.P. Eckert, G. Buchsbaum, A.B. Watson, "Separability of Spatiotemporal Spectra of Image Sequences," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 12, pp. 12101213, December, 1992.  
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@article{ 10.1109/34.177387, author = {M.P. Eckert and G. Buchsbaum and A.B. Watson}, title = {Separability of Spatiotemporal Spectra of Image Sequences}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {12}, issn = {01628828}, year = {1992}, pages = {12101213}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.177387}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Separability of Spatiotemporal Spectra of Image Sequences IS  12 SN  01628828 SP1210 EP1213 EPD  12101213 A1  M.P. Eckert, A1  G. Buchsbaum, A1  A.B. Watson, PY  1992 KW  image sequences; spatiotemporal power spectrum; exponential correlation model; singular value decomposition; spatiotemporal separability; image sequences; spectral analysis VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
The authors calculate the spatiotemporal power spectrum of 14 image sequences in order to determine the degree to which the spectra are separable in space and time and to assess the validity of the commonly used exponential correlation model. They expand the spectrum by a singular value decomposition into a sum of separable terms and define an index of spatiotemporal separability. as the fraction of the signal energy that can be represented by the first (largest) separable term. All spectra were found to be highly separable with an index of separability above 0.98. The power spectra of the sequences were well fit by a separable model, which corresponds to a product of exponential autocorrelation functions separable in space and time.
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