The final output is a synthetic image in which the subjects have been digitally refocused.
Our analysis is based on extensive experiments on Amazon Mechanical Turk using real and synthetic image datasets.
We experimentally show on synthetic, image, and genomic data that our approach is scalable and lead to more connected subgraphs than other regularization functions for graphs.
Good registration in which a synthetic image appears over a real scene requires representation of the two images in the same frame of reference.
A synthetic image analysis strategy is proposed for in-situ crystal size measurement and shape identification for monitoring crystallization processes, based on using a real-time imaging system.
More specifically, we focus our attention in the evaluation of the number of useful individual units, and its contribution to the synthetic image characteristics.
Hence, our results help to make essential design decisions while developing interactive multimedia applications in general, involving e.g., distributed synthetic image data, or augmented and virtual reality.
In order to determine the benefits of this fusion technique, first, we tested it on a synthetic image containing different forms with different colors.
It is demonstrated that for each group of three arrays (in a 15 view system) a synthetic image may be employed to make the middle array redundant.
An evaluation of our framework based on synthetic image data yields an average elevation accuracy of the constructed DEMs of better than 20 m as long as the correct reflectance model is assumed.
First, an optimal distance-based weighted interpolation of the image sequence is performed using a new neural architecture, hybrid of a multi-layer perceptron and a probabilistic neural network, trained on synthetic image data.
