Realistic Simulation of Seasonal Variant Maples

International audienceThis paper presents a biologically-motivated system of seasonal variant scenes generation for maples, which has a obvious leaf color transformation during the time. Given climate data and knowledge on environmental influence to maples, our system is able to simulate this seasonal leaf color transformation process. Our system consists of three steps: environment configuration, climate influence simulation and leaf texture acquisition. The first step decides the general color change timing of the maple tree based on its local environment. Then we make further adjustments to the timing determined in the last step taking into account the influence of climate in the specific case. In the last step, the texture maps of leaves are generated based on the pigment information. Our system is also able to simulate the seasonal color variance of other trees by adjusting related parameters

Robust tile-based texture synthesis using artificial immune system

The original publication is avalaible at www.springerlink.comInternational audienceOne significant problem in tile-based texture synthesis is the presence of conspicuous seams in the tiles. The reason is that sample patches employed as primary patterns of the tile set may not be well stitched if carelessly picked. In this paper, we introduce a robust approach that can stably generate an ω-tile set of high quality and pattern diversity. First, an extendable rule is introduced to increase the number of sample patches to vary the patterns in an ω-tile set. Second, in contrast to other concurrent techniques that randomly choose sample patches for tile construction, ours uses artificial immune system (AIS) to select the feasible patches from the input example. This operation ensures the quality of the whole tile set. Experimental results verify the high quality and efficiency of the proposed algorithm

Perspective-aware texture analysis and synthesis

The original publication is available at www.springerlink.comInternational audienceThis paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo