We present a novel artistic-verisimilitude driven system for watercolor rendering of images and photos. Our system achieves realistic simulation of a set of important characteristics of watercolor paintings that have not been well implemented before. Specifically, we designed several image filters to achieve: 1) watercolor-specified color transferring; 2) saliency-based level-of-detail drawing; 3) hand tremor effect due to human neural noise; and 4) an artistically controlled wet-in-wet effect in the border regions of different wet pigments. A user study indicates that our method can produce watercolor results of artistic verisimilitude better than previous filter-based or physical-based methods. Furthermore, our algorithm is efficient and can easily be parallelized, making it suitable for interactive image watercolorization.
Oak tree Beech tree (a) biological distribution Unrelaxed Our method (b) photon density estimation (c) point cloud sampling + reconstruction Figure 1: Analogous to bilateral filtering [Tomasi and Manduchi 1998], our bilateral sampling method considers both spatial-domain and nonspatial-domain properties. Our method can generate distributions with sample attributes that are not direct functions of the underlying domains, such as more natural biological distribution with domain-independent tree type and size (a), less noisy photon density estimation with arbitrary flux and incoming direction (b), and more accurate (hidden) surface reconstruction from point cloud sampling (c).
International audienceThe synthesis quality is one of the most important aspects in solid texture synthesis algorithms. In recent years several methods are proposed to generate high quality solid textures. However, these existing methods often suffer from the synthesis artifacts such as blurring, missing texture structures, introducing aberrant voxel colors, and so on. In this paper, we introduce a novel algorithm for synthesizing high quality solid textures from 2D exemplars. We first analyze the relevant factors for further improvements of the synthesis quality, and then adopt an optimization framework with the k-coherence search and the discrete solver for solid texture synthesis. The texture optimization approach is integrated with two new kinds of histogram matching methods, position and index histogram matching, which effectively cause the global statistics of the synthesized solid textures to match those of the exemplars. Experimental results show that our algorithm outperforms or at least is comparable to the previous solid texture synthesis algorithms in terms of the synthesis quality
This paper presents an improvement to the stochastic progressive photon mapping (SPPM), a method for robustly simulating complex global illumination with distributed ray tracing effects. Normally, similar to photon mapping and other particle tracing algorithms, SPPM would become inefficient when the photons are poorly distributed. An inordinate amount of photons are required to reduce the error caused by noise and bias to acceptable levels. In order to optimize the distribution of photons, we propose an extension of SPPM with a Metropolis-Hastings algorithm, effectively exploiting local coherence among the light paths that contribute to the rendered image. A well-designed scalar contribution function is introduced as our Metropolis sampling strategy, targeting at specific parts of image areas with large error to improve the efficiency of the radiance estimator. Experimental results demonstrate that the new Metropolis sampling based approach maintains the robustness of the standard SPPM method, while significantly improving the rendering efficiency for a wide range of scenes with complex lighting.
Depth-of-field is one of the most crucial rendering effects for synthesizing photorealistic images. Unfortunately, this effect is also extremely costly. It can take hundreds to thousands of samples to achieve noise-free results using Monte Carlo integration. This paper introduces an efficient adaptive depth-of-field rendering algorithm that achieves noise-free results using significantly fewer samples. Our algorithm consists of two main phases: adaptive sampling and image reconstruction. In the adaptive sampling phase, the adaptive sample density is determined by a 'blur-size' map and 'pixel-variance' map computed in the initialization. In the image reconstruction phase, based on the blur-size map, we use a novel multiscale reconstruction filter to dramatically reduce the noise in the defocused areas where the sampled radiance has high variance. Because of the efficiency of this new filter, only a few samples are required. With the combination of the adaptive sampler and the multiscale filter, our algorithm renders near-reference quality depth-of-field images with significantly fewer samples than previous techniques.
During data collection, field interviewers often append notes or comments to a case in open text fields to request updates to case-level data. Processing these comments can improve data quality, but many are non-actionable, and processing remains a costly manual task. This article presents a case study using a novel application of machine learning tools to assist in the evaluation of these comments. Using over 5,000 comments from the Medical Expenditure Panel Survey, we built features that were fed to a machine learning model to predict a grouping category for each comment as previously assigned by data technicians to expedite processing. The model achieved high top-3 accuracy and was incorporated into a production tool for editing. A qualitative evaluation of the tool also provided encouraging results. This application of machine learning tools allowed a small but worthwhile increase in processing efficiency, while maintaining exacting standards for data quality.
Members of the YABBY gene family play significant roles in lamina development in cotyledons, floral organs, and other lateral organs. The Orchidaceae family is one of the largest angiosperm groups. Some YABBYs have been reported in Orchidaceae. However, the function of YABBY genes in Cymbidium is currently unknown. In this study, 24 YABBY genes were identified in Cymbidium ensifolium, C. goeringii, and C. sinense. We analyzed the conserved domains and motifs, the phylogenetic relationships, chromosome distribution, collinear correlation, and cis-elements of these three species. We also analyzed expression patterns of C. ensifolium and C. goeringii. Phylogenetic relationships analysis indicated that 24 YABBY genes were clustered in four groups, INO, CRC/DL, YAB2, and YAB3/FIL. For most YABBY genes, the zinc finger domain was located near the N-terminus and the helix-loop-helix domain (YABBY domain) near the C-terminus. Chromosomal location analysis results suggested that only C. goeringii YABBY has tandem repeat genes. Almost all the YABBY genes displayed corresponding one-to-one relationships in the syntenic relationships analysis. Cis-elements analysis indicated that most elements were clustered in light-responsive elements, followed by MeJA-responsive elements. Expression patterns showed that YAB2 genes have high expression in floral organs. RT-qPCR analysis showed high expression of CeYAB3 in lip, petal, and in the gynostemium. CeCRC and CeYAB2.2 were highly expressed in gynostemium. These findings provide valuable information of YABBY genes in Cymbidium species and the function in Orchidaceae.
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