Titlebook: Computational Intelligence for Multimedia Understanding; International Worksh Emanuele Salerno,A. Enis ?etin,Ovidio Salvetti Conference pro

發(fā)出眩目光芒

capsule

Directionally Selective Fractional Wavelet Transform Using a 2-D Non-separable Unbalanced Lifting St decomposition is based on two-channel unbalanced lifting structures whereby it is possible to decompose a given discrete-time signal .[.] sampled with period . into two sub-signals ..[.] and ..[.] whose average sampling periods are . and ., respectively. Fractions p and q are rational numbers sati

鳥(niǎo)籠

Cpr951

Texture Recognition Using Robust Markovian Features, of the experimental textural databases ALOT, Bonn BTF, UEA Uncalibrated, and KTH-TIPS2 aims at specific part of realistic acquisition conditions of surface materials represented as multispectral textures. The extensive experimental evaluation proves the outstanding reliable and robust performance o

磨坊

A Plausible Texture Enlargement and Editing Compound Markovian Model, function, which is currently the most advanced representation of visual properties of surface materials. The presented compound Markov random field model combines a non-parametric control random field with analytically solvable wide-sense Markov representation for single regions and thus allows ver

險(xiǎn)代理人

Bidirectional Texture Function Simultaneous Autoregressive Model,ring appearance due to varying illumination and viewing conditions. Corresponding huge BTF measurements require a mathematical representation allowing simultaneously extremal compression as well as high visual fidelity. We present a novel Markovian BTF model based on a set of underlying simultaneous

Genistein

Analysis of Human Gaze Interactions with Texture and Shape,ntrolled psychophysical experiment with stimuli featuring different combinations of shape, illumination directions and surface texture. Appearance of five tested materials was represented by measured view and illumination dependent Bidirectional Texture Functions. Twelve subjects participated in vis