In this article, a new virtual view generation method with spatial and temporal texture synthesis is proposed.
A depth-image-based rendering (DIBR) method with spatial and temporal texture synthesis is presented in this article.
A backward-in-time probabilistic method with spatial filter averaging is presented to solve linear second-order partial differential equations of the parabolic type.
The framework of proposed DIBR method with spatial and temporal texture synthesis is shown in Figure 2. The proposed method is divided into four main stages, i.e., stationary scene extraction, backward DIBR, merging operation, and oriented exemplar-based inpainting.
The predictions are compared with experimental data and with results obtained using alternative prediction methods such as the transfer matrix method with spatial windowing, the hybrid wave based-transfer matrix method, and the hybrid finite element-statistical energy analysis method.
The information was extracted from the features close to the background or saturated and normalization was performed by the rank invariant and lowest fitness method with spatial normalization.
First, based on the analysis of representative spatial information characterization methods of existing local shape descriptors, six typical characterization methods with different spatial dimensions and partition principles of spatial information are presented.
With these probes, near-field microscopy appears poised to fulfill its promise by combining the power of optical characterization methods with nanometric spatial resolution.
The second question, regarding the brain topography behind S-maps, can be tentatively answered using a priori knowledge, including invaluable data from the neuroimaging methods with high spatial resolution.
