Browsing by Subject "Spatio-temporal filtering"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access Rapid classification of specular and diffuse reflection from image velocities(Elsevier BV, 2011-09) Doerschner, K.; Kersten, D.; Schrater, P. R.We propose a method for rapidly classifying surface reflectance directly from the output of spatiotemporal filters applied to an image sequence of rotating objects. Using image data from only a single frame, we compute histograms of image velocities and classify these as being generated by a specular or a diffusely reflecting object. Exploiting characteristics of material-specific image velocities we show that our classification approach can predict the reflectance of novel 3D objects, as well as human perception.Item Open Access Rapid classification of surface reflectance from image velocities(Springer, Berlin, Heidelberg, 2009) Doerschner, Katja; Kersten, D.; Schrater P.We propose a method for rapidly classifying surface reflectance directly from the output of spatio-temporal filters applied to an image sequence of rotating objects. Using image data from only a single frame, we compute histograms of image velocities and classify these as being generated by a specular or a diffusely reflecting object. Exploiting characteristics of material-specific image velocities we show that our classification approach can predict the reflectance of novel 3D objects, as well as human perception. © 2009 Springer Berlin Heidelberg.Item Open Access Sparse binarised statistical dynamic features for spatio-temporal texture analysis(Springer, 2019) Arashloo, Shervin RahimzadehThe paper presents a new spatio-temporal learning-based descriptor called binarised statistical dynamic features (BSDF) for representation and classification of dynamic texture. The BSDF descriptor operates by applying three-dimensional spatio-temporal filters on local voxels of an image sequence where the filters are learned via an independent component analysis, maximising independence over spatial and temporal domains concurrently. The BSDF representation is formed by binarising filter responses which are then converted into codewords and summarised using histograms. A robust representation of the BSDF descriptor is finally obtained via a sparse representation approach yielding very discriminative features for classification. The effects of different hyper-parameters on performance including the number of filters, the number of scales, temporal depth, number of samples drawn are also investigated. The proposed approach is evaluated on the most commonly used dynamic texture databases and shown to perform very well compared to the existing methods.