Spatio–Temporal Regularity Flow (SPREF): Its Estimation and Applications

@article{citeulike:2398887, abstract = {Feature selection and extraction is a key operation in video analysis for achieving a higher level of abstraction. In this paper, we introduce a general framework to extract a new spatio-temporal feature that represents the directions in which a video is regular, i.e., the pixel appearances change the least. We propose to model the directions of regular variations with a 3-D vector field, which is referred to as spatio-temporal regularity flow (SPREF). SPREF vectors are designed to have three cross-sectional parallel components F_x, F_y, and F_t for convenient use in different applications. They are estimated using all the frames simultaneously by minimizing an energy functional formulated according to its definition. In this paper, we first introduce translational SPREF (T-SPREF) and then extend our framework to affine SPREF (A-SPREF). The successful use of SPREF in a few applications, including object removal, video inpainting, and video compression, is also demonstrated}, author = {Alatas, Orkun and Yan, Pingkun and Shah, Mubarak }, booktitle = {Circuits and Systems for Video Technology, IEEE Transactions on}, citeulike-article-id = {2398887}, doi = {http://dx.doi.org/10.1109/TCSVT.2007.893832}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, keywords = {3d, affine, analysis, coding, components, compression, cross-sectional, data, extraction, feature, field, flow, inpainting, modeling, object, parallel, parallelism, regularity, removal, selection, spatio-temporal, spref, translational, vector, video}, month = {May}, number = {5}, pages = {584--589}, posted-at = {2008-02-19 13:46:34}, priority = {2}, title = {Spatio–Temporal Regularity Flow (SPREF): Its Estimation and Applications}, url = {http://dx.doi.org/10.1109/TCSVT.2007.893832}, volume = {17}, year = {2007} }

TY - JOUR ID - citeulike:2398887 L3 - citeulike-article-id:2398887 TI - Spatio–Temporal Regularity Flow (SPREF): Its Estimation and Applications T2 - Circuits and Systems for Video Technology, IEEE Transactions on JF - IEEE Transactions on Circuits and Systems for Video Technology VL - 17 IS - 5 SP - 584 EP - 589 N2 - Feature selection and extraction is a key operation in video analysis for achieving a higher level of abstraction. In this paper, we introduce a general framework to extract a new spatio-temporal feature that represents the directions in which a video is regular, i.e., the pixel appearances change the least. We propose to model the directions of regular variations with a 3-D vector field, which is referred to as spatio-temporal regularity flow (SPREF). SPREF vectors are designed to have three cross-sectional parallel components F_x, F_y, and F_t for convenient use in different applications. They are estimated using all the frames simultaneously by minimizing an energy functional formulated according to its definition. In this paper, we first introduce translational SPREF (T-SPREF) and then extend our framework to affine SPREF (A-SPREF). The successful use of SPREF in a few applications, including object removal, video inpainting, and video compression, is also demonstrated KW - 3d KW - affine KW - analysis KW - coding KW - components KW - compression KW - cross-sectional KW - data KW - extraction KW - feature KW - field KW - flow KW - inpainting KW - modeling KW - object KW - parallel KW - parallelism KW - regularity KW - removal KW - selection KW - spatio-temporal KW - spref KW - translational KW - vector KW - video AU - Alatas, Orkun AU - Yan, Pingkun AU - Shah, Mubarak PY - 2007/05// UR - http://dx.doi.org/10.1109/TCSVT.2007.893832 ER -