IEEE Trans Image Process - Video texture synthesis with multi-frame LBP-TOP and diffeomorphic growth model.

Tópicos

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Resumo

Video texture synthesis is the process of providing a continuous and infinitely varying stream of frames, which plays an important role in computer vision and graphics. However, it still remains a challenging problem to generate high-quality synthesis results. Considering the two key factors that affect the synthesis performance, frame representation and blending artifacts, we improve the synthesis performance from two aspects: 1) Effective frame representation is designed to capture both the image appearance information in spatial domain and the longitudinal information in temporal domain. 2) Artifacts that degrade the synthesis quality are significantly suppressed on the basis of a diffeomorphic growth model. The proposed video texture synthesis approach has two major stages: video stitching stage and transition smoothing stage. In the first stage, a video texture synthesis model is proposed to generate an infinite video flow. To find similar frames for stitching video clips, we present a new spatial-temporal descriptor to provide an effective representation for different types of dynamic textures. In the second stage, a smoothing method is proposed to improve synthesis quality, especially in the aspect of temporal continuity. It aims to establish a diffeomorphic growth model to emulate local dynamics around stitched frames. The proposed approach is thoroughly tested on public databases and videos from the Internet, and is evaluated in both qualitative and quantitative ways.

Resumo Limpo

video textur synthesi process provid continu infinit vari stream frame play import role comput vision graphic howev still remain challeng problem generat highqual synthesi result consid two key factor affect synthesi perform frame represent blend artifact improv synthesi perform two aspect effect frame represent design captur imag appear inform spatial domain longitudin inform tempor domain artifact degrad synthesi qualiti signific suppress basi diffeomorph growth model propos video textur synthesi approach two major stage video stitch stage transit smooth stage first stage video textur synthesi model propos generat infinit video flow find similar frame stitch video clip present new spatialtempor descriptor provid effect represent differ type dynam textur second stage smooth method propos improv synthesi qualiti especi aspect tempor continu aim establish diffeomorph growth model emul local dynam around stitch frame propos approach thorough test public databas video internet evalu qualit quantit way

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