Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

Mariethoz, Grégoire; Lefèbvre, Sylvain

doi:10.1016/j.cageo.2014.01.001

Cited by 81 publications

(50 citation statements)

References 139 publications

(185 reference statements)

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“…Because the fine spatial resolution patch is a high dimensional vector, the effectiveness of similarity calculation should be further improved by extracting features from fine spatial resolution patches. Given the recent development of multiple point geo-statistics for spatial patterns reproduction [62], some approaches used to simulate spatial patterns in the multiple point geo-statistics community [63,64] should be applied for the improvement of the proposed learning based SRM algorithm in the further research.…”

Section: Discussionmentioning

confidence: 99%

Learning-Based Superresolution Land Cover Mapping

Ling

Zhang

Foody

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Super-resolution mapping (SRM) is a technique for generating a fine spatial resolution land cover map from coarse spatial resolution fraction images estimated by soft classification. The prior model used to describe the fine spatial resolution land cover pattern is a key issue in SRM. Here, a novel learning based SRM algorithm, whose prior model is learned from other available fine spatial resolution land cover maps, is proposed.The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine and coarse spatial resolution representation for the same area. From the learning database, patch pairs that have similar coarse spatial resolution patches as those in input fraction images are selected. Fine spatial resolution patches in these selected patch pairs are then used to estimate the latent fine spatial resolution land cover map, by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and Kappa values in all these SRM algorithms, by using the entire maps in the accuracy assessment.

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Section: Discussionmentioning

confidence: 99%

Learning-Based Superresolution Land Cover Mapping

Ling

Zhang

Foody

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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“…While for the 3D fruit surface, the existing mesh-based graphics hardware is hard to develop its high-speed advantages, because a high precision fruit model will consume large numbers of storage and rendering time, due to the following deficiencies [9]. In these methods, triangles number is twice of vertices, and texture was generally mixed by simply assigning the average value of three vertex colors to triangles.…”

Section: Related Workmentioning

confidence: 99%

“…However, these traditional methods still have difficult to reproduce the real pear texture morphology accurately and acquire a good balance between texturing quality and space in areas of Computer Graphics, 3D reconstruction and image processing, due to the following status quo [2][3][4][5][6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

“…It is much easier to edit the sample point on the surface of the model and reconstruct the final texture directly by the point information, because the sampling points can be used to express not only model position but also the texture properties. Nevertheless, the direct points based texture synthesis seems to be difficult due to no adjacency relations between points [9], hence most of the texture synthesis on point model is only an auxiliary application of model editing [19]. Such as PointShop3D system edited 3D model [20], The point based three-dimensional surface shading system [21], pixel based texture synthesis and bump mapping result on point model [22].…”

Section: Related Workmentioning

confidence: 99%

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High-efficiency texture coding and synthesis on point-based pear surface

Yang

Wang

et al. 2017

JCM

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Abstract. The fruit images on points cloud acquired by the current 3D scanner from field will appear a visible seams, inconvenient data acquisition or taking large space due to unorganized background. We give a SAOW method to cope with the space efficiency and realistic effects of texture synthesis on pear point model. At first, a point-quadtree is proposed to simplify the pear image division. Then, an adaptive multi-granularity morton coding scheme are presented to optimizing the memory space of pear image. At last, weighted oversampling mixing method is mainly focused on texture quality of pear surface. As shown in the experiment results, our adaptive division makes the memory space decline dramatically about 90.7% than non-division and 92.9% than general division respectively; adaptive code scheme helps to reduce the memory to 72.1% of ordinary morton code; weighted oversampling keeps the mixed texture more real and smoothly than current methods.

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“…Although newer implementations exist, SNESIM is still the most widely used MPS method, largely because of its userfriendly interface (Remy et al, 2009). Mariethoz and Lefebvre (2014) suggested combining MPS with texture synthesis. MPS has been extensively and successfully used to model geographical phenomena that have complex spatial structures.…”

Section: Introductionmentioning

confidence: 99%

Utilizing spatial association analysis to determine the number of multiple grids for multiple-point statistics

Bai

Mariethoz

2016

Spatial Statistics

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a b s t r a c tScale selection is a fundamental issue of spatial analysis. Based on spatial association analysis, this paper proposes a quantitative method for estimating the scale, which is represented using the minimal number of multiple grids in a single normal equation simulation. First, the largest scale structural information that a data template can consider for the finest grid is computed using the distance between the central cell and the border of the data template. The maximum distance at which cells are associated with each other in the training image is then analyzed using join count statistics. Finally, the minimal number of multiple grids is estimated based on the criterion that the data template used on the largest grid should account for the maximum distance between associated cells. The proposed method is validated using two-and three-dimensional experiments. The results show that increasing the number of multiple grids does not significantly improve the simulation quality when the number of multiple grids used is larger than that estimated. A sensitivity analysis demonstrates that the proposed method adapts to the configuration of the data template, 84H. Bai et al. / Spatial Statistics 17 (2016) the geometric structure of the target surface objects, and a rescaled training image if it adequately represents the large-scale structural information of the target surface objects.

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Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

Cited by 81 publications

References 139 publications

Learning-Based Superresolution Land Cover Mapping

Learning-Based Superresolution Land Cover Mapping

High-efficiency texture coding and synthesis on point-based pear surface

Utilizing spatial association analysis to determine the number of multiple grids for multiple-point statistics

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