IEEE Trans Image Process - Rotation-invariant image and video description with local binary pattern features.

Tópicos

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Resumo

In this paper, we propose a novel approach to compute rotation-invariant features from histograms of local noninvariant patterns. We apply this approach to both static and dynamic local binary pattern (LBP) descriptors. For static-texture description, we present LBP histogram Fourier (LBP-HF) features, and for dynamic-texture recognition, we present two rotation-invariant descriptors computed from the LBPs from three orthogonal planes (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel rotation-invariant image descriptor computed from discrete Fourier transforms of LBP histograms. The approach can be also generalized to embed any uniform features into this framework, and combining the supplementary information, e.g., sign and magnitude components of the LBP, together can improve the description ability. Moreover, two variants of rotation-invariant descriptors are proposed to the LBP-TOP, which is an effective descriptor for dynamic-texture recognition, as shown by its recent success in different application problems, but it is not rotation invariant. In the experiments, it is shown that the LBP-HF and its extensions outperform noninvariant and earlier versions of the rotation-invariant LBP in the rotation-invariant texture classification. In experiments on two dynamic-texture databases with rotations or view variations, the proposed video features can effectively deal with rotation variations of dynamic textures (DTs). They also are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-invariant recognition of DTs.

Resumo Limpo

paper propos novel approach comput rotationinvari featur histogram local noninvari pattern appli approach static dynam local binari pattern lbp descriptor statictextur descript present lbp histogram fourier lbphf featur dynamictextur recognit present two rotationinvari descriptor comput lbps three orthogon plane lbptop featur spatiotempor domain lbphf novel rotationinvari imag descriptor comput discret fourier transform lbp histogram approach can also general emb uniform featur framework combin supplementari inform eg sign magnitud compon lbp togeth can improv descript abil moreov two variant rotationinvari descriptor propos lbptop effect descriptor dynamictextur recognit shown recent success differ applic problem rotat invari experi shown lbphf extens outperform noninvari earlier version rotationinvari lbp rotationinvari textur classif experi two dynamictextur databas rotat view variat propos video featur can effect deal rotat variat dynam textur dts also robust respect chang viewpoint outperform recent method propos viewinvari recognit dts

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