基于样图的纹理合成技术研究
摘要
纹理合成以人工生成纹理为目的，是计算机图形学与图像处理的重要研究领域。基于样图的纹理合成是近些年来出现的一种新技术，它以小块纹理图像作为输入而合成任意大的同类纹理。该技术大量用于制作虚拟现实中的场景，在电影、游戏娱乐等产业中有着巨大的应用需求。随着近些年来计算机三维绘图能力的飞速提高和对大尺寸高质量纹理的强烈需求，基于样图的纹理合成成为热门研究领域之一。
基于样图的纹理合成以高质量的输出图像、满足实时需要的合成速度与全自动的合成过程为目标。随着近些年来研究者们的大量工作，基于样图的纹理合成技术取得了很大进展。目前该领域的难点主要体现在纹理合成的速度与合成图像的质量上。针对以上两点，本论文以高质量快速纹理合成算法为目标，所作的工作主要内容包括以下4个方面。
(1)结构性纹理全局特性的识别和提取。本文首次提出一种用于识别结构化纹理中特征点分布的结构模式分析方法。该方法首先以邻域相似性为准则，对纹理中的像素进行聚类，然后以像素邻域差别为依据选择一组像素作为特征像素组，并分析该组中像素的分布，最终识别整个特征点网格，以其为基础，可以很方便地对纹理中的全局特性进行捕捉，对纹理合成相关应用起到很大的辅助作用。大量实验证明，该方法对周期及伪周期结构性纹理具有强大的通用性，能准确识别高随机度纹理中的特征点网格，并根据网格的统计数据对纹理的宏观随机度进行量化，量化后的随机度指数准确反映了纹理图像宏观结构的整齐程度。
(2)基于特征点定位的纹理合成算法。本文以传统的基于块的纹理合成框架为基础，提出一种使用特征点定位的纹理合成算法。该算法在预处理阶段对样图进行结构模式分析，根据得到的统计数据对输出图像进行特征点全局模糊定位；在块匹配阶段，将特征点分布与邻域相似性一起作为匹配标准；在块边界修复阶段，基于传统的块边界切割技术，提出和使用一种强化的边界修复算法，对图像中的高频与低频分量进行分离处理，以获得更加平滑的过渡效果，从而提升输出图像的质量。理论及实验证明，使用特征点定位可以保证输出图像不会出现大的结构性错误，同时块匹配时的搜索空间根据纹理随机度的差别有不同程度的缩小，对大多数结构化纹理，搜索空间至少可以减小一到两个数量级，直接加速了纹理合成的过程。
(3)基于结构坐标的纹理合成算法。本文以传统的基于像素的纹理合成框架为基础，给出一种基于结构坐标匹配的纹理合成算法。该算法在结构模式分析的基础上首次提出纹理结构坐标的概念。在像素合成阶段，结构坐标差异与像素邻域色彩差异一起作为匹配准则。通过对结构坐标匹配时的阈值进行设置，该算法还可以对输出纹理的随机度进行控制。理论及实验证明，使用结构坐标匹配明显改善了合成纹理的结构，有效弥补了传统的基于像素的合成难以再现纹理大范围特性的不足。同时，像素全局匹配时的搜索空间根据纹理随机度的差别有不同程度的缩小，有效加速了纹理合成的过程。
(4)使用动态特征点匹配的纹理优化算法。本文以基于优化的纹理合成算法为基础，提出一种加入特征点匹配要素的纹理合成算法。该算法在预处理阶段以特征点周期位置为准则，生成一组经过优化的初始块集合。在块匹配阶段中，算法随时检查和保证不同块中特征点之间应有的相对位置，并对特征点网格进行动态调整。理论及实验证明，使用特征点匹配生成的初始块集合可以大大加快算法的收敛速度，而块匹配时对特征点相对位置的检查则可以有效保证输出图像结构的正确性。
本研究工作的主要创新点为：
(1)首次所提出的一种用于分析纹理中特征点分布的结构模式分析方法，可以有效识别和定位纹理中的全局特性，对传统的纹理合成算法是一个有力的辅助工具。
(2)所提出的基于特征点定位的纹理合成算法，通过特征点定位直接排除了会导致输出图像结构性错误的匹配位置，在确保了输出图像结构正确的同时，也大幅度减少了块匹配时的搜索空间，直接提升了合成速度。
(3)为了定位纹理中的全局特征，首次提出了结构坐标的概念，并将其应用于基于像素的纹理合成框架中。通过结构坐标的匹配，将纹理全局结构信息准确传递给合成算法，有效改善了输出图像的结构，同时大幅度减少了像素匹配时的搜索空间，提升了合成速度。
The goal of texture synthesis is to produce texture. It is an important research area in both computer graphics and image processing. Sample-based texture synthesis was a new technique proposed in recent years. It takes small texture image as input to synthesis same kind of texture but in arbitrary size. This technique is widely used in producing virtual reality scene and satisfies the great need in application areas such as movie industry and computer game. With the rapid development of computer 3D graphics ability and the great need of large size and high quality texture in recent years, sample-based texture synthesis became a hot research area.
The goal of sample-based texture synthesis is high quality output image, synthesis speed that satisfies real time application and full automatic synthesis process. With the large amount work of researchers these years, sample-based texture synthesis technique has achieved much. The main challenge of this area currently lies in synthesis speed and the quality of synthesized image. In order to solve these problems, this thesis aims at fast and high quality texture synthesis algorithm.
Main work of this thesis includes the following:
(1) Global feature recognition and extraction for structured texture. A new structural pattern analysis method which is used to recognize feature point distribution pattern in structured texture is firstly proposed in this thesis. The method first clusters pixels in the texture according to the rule of neighborhood similarity, then selects a pixel group as the feature point group due to pixel neighborhood difference and analyses the distribution of pixels in this group, and finally, the method recognizes the feature point mesh. With the mesh, it is convenient to capture the global characteristics of texture. Thus the method can assist much in texture synthesis related applications. Many experiments shows that the method exhibit high generality to periodic and pseudo-periodic structured textures and can accurately recognize feature point mesh in a highly stochastic texture. Bases on the mesh statistic data, the method also quantizes macro scope randomness of texture. The quantized texture randomness parameter faithfully reflects the regularity of texture macro scope structure.
(2) Feature point locating based texture synthesis algorithm. Bases on the traditional framework of patch-based texture synthesis, this thesis proposes texture synthesis algorithm using feature point locating. In the preprocessing phase, the algorithm uses structural pattern analysis on the sample image and performs global approximate feature point locating for the output image based on. the statistic data get. In the border fix phase, based on the traditional border cut technique, the algorithm proposes and uses a enhanced border fix method. The method splits the high and low frequency image component and deal with them separately to get smoother transition effect. It helps to improve image quality. It can be proved both in theory and in experiments that feature point locating assures the elimination of large scale structural error in the output image and at the same, searching space when performing patch matching is reduced more or less due to texture randomness. For most structured texture, searching space can be reduced not less than 1 to 2 decimal levels. This accelerates the texture synthesis process directly.
(3) Structural coordinate based texture synthesis algorithm. Based on the traditional framework of pixel-based texture synthesis, this thesis proposes a texture synthesis algorithm which bases on structural coordinate matching. The algorithm firstly brings forward the concept of structural coordinate. In the pixel synthesis phase, differences of both structural coordinate and neighborhood color are used as matching principle. By the means of setting threshold value for structural coordinate matching, the algorithm offers the control on output texture randomness. Both theories and experiments shows that the using of structural coordinate matching remarkably improves the structure of synthesis texture and effectively makes up the lack in reproducing large scale texture characteristic. At the same time, searching space when performing global pixel matching decreases more or less due to texture randomness. This effectively accelerates the texture synthesis process.
(4) Texture optimization algorithm using dynamic feature point matching. Bases on the optimization based texture synthesis algorithm, this thesis proposes a texture synthesis algorithm with feature point matching. In the preprocessing phase, the algorithm uses the periodic location of feature point as a rule to generate an optimized initial patch set. In the patch matching phase, the algorithm checks and assures the reasonable distance between feature points from different patches, and adjusts feature point mesh dynamically. Theories and experiments shows that initial patch set generated using feature point matching can accelerate the convergence of algorithm a lot. And feature point relative position check when matching patches can effectively assure the structure correctness of the output image.
Main innovation points of the thesis are:
(1) A firstly proposed structural pattern analysis method. It can effectively recognize and locate global characteristic of texture. It is a powerful assisting tool for conventional texture synthesis algorithms.
(2) The proposed feature point locating based texture synthesis algorithm. The algorithm excludes the matching position which leads to structural errors in output image by means of feature point locating. This assures the correct structure of output image and greatly reduces the searching space when matching patches. It improves the synthesis speed directly.
(3) In order to locate texture global characteristic, this thesis firstly brings forward the concept of structural coordinate, and then uses it in the framework of pixel-based texture synthesis. Through the matching of structural coordinate, global structural information of texture can be accurately passed to synthesis algorithm. This effectively improves output image structure and greatly decreases the searching space when matching pixels. It improves synthesis speed.
基于样图的纹理合成以高质量的输出图像、满足实时需要的合成速度与全自动的合成过程为目标。随着近些年来研究者们的大量工作，基于样图的纹理合成技术取得了很大进展。目前该领域的难点主要体现在纹理合成的速度与合成图像的质量上。针对以上两点，本论文以高质量快速纹理合成算法为目标，所作的工作主要内容包括以下4个方面。
(1)结构性纹理全局特性的识别和提取。本文首次提出一种用于识别结构化纹理中特征点分布的结构模式分析方法。该方法首先以邻域相似性为准则，对纹理中的像素进行聚类，然后以像素邻域差别为依据选择一组像素作为特征像素组，并分析该组中像素的分布，最终识别整个特征点网格，以其为基础，可以很方便地对纹理中的全局特性进行捕捉，对纹理合成相关应用起到很大的辅助作用。大量实验证明，该方法对周期及伪周期结构性纹理具有强大的通用性，能准确识别高随机度纹理中的特征点网格，并根据网格的统计数据对纹理的宏观随机度进行量化，量化后的随机度指数准确反映了纹理图像宏观结构的整齐程度。
(2)基于特征点定位的纹理合成算法。本文以传统的基于块的纹理合成框架为基础，提出一种使用特征点定位的纹理合成算法。该算法在预处理阶段对样图进行结构模式分析，根据得到的统计数据对输出图像进行特征点全局模糊定位；在块匹配阶段，将特征点分布与邻域相似性一起作为匹配标准；在块边界修复阶段，基于传统的块边界切割技术，提出和使用一种强化的边界修复算法，对图像中的高频与低频分量进行分离处理，以获得更加平滑的过渡效果，从而提升输出图像的质量。理论及实验证明，使用特征点定位可以保证输出图像不会出现大的结构性错误，同时块匹配时的搜索空间根据纹理随机度的差别有不同程度的缩小，对大多数结构化纹理，搜索空间至少可以减小一到两个数量级，直接加速了纹理合成的过程。
(3)基于结构坐标的纹理合成算法。本文以传统的基于像素的纹理合成框架为基础，给出一种基于结构坐标匹配的纹理合成算法。该算法在结构模式分析的基础上首次提出纹理结构坐标的概念。在像素合成阶段，结构坐标差异与像素邻域色彩差异一起作为匹配准则。通过对结构坐标匹配时的阈值进行设置，该算法还可以对输出纹理的随机度进行控制。理论及实验证明，使用结构坐标匹配明显改善了合成纹理的结构，有效弥补了传统的基于像素的合成难以再现纹理大范围特性的不足。同时，像素全局匹配时的搜索空间根据纹理随机度的差别有不同程度的缩小，有效加速了纹理合成的过程。
(4)使用动态特征点匹配的纹理优化算法。本文以基于优化的纹理合成算法为基础，提出一种加入特征点匹配要素的纹理合成算法。该算法在预处理阶段以特征点周期位置为准则，生成一组经过优化的初始块集合。在块匹配阶段中，算法随时检查和保证不同块中特征点之间应有的相对位置，并对特征点网格进行动态调整。理论及实验证明，使用特征点匹配生成的初始块集合可以大大加快算法的收敛速度，而块匹配时对特征点相对位置的检查则可以有效保证输出图像结构的正确性。
本研究工作的主要创新点为：
(1)首次所提出的一种用于分析纹理中特征点分布的结构模式分析方法，可以有效识别和定位纹理中的全局特性，对传统的纹理合成算法是一个有力的辅助工具。
(2)所提出的基于特征点定位的纹理合成算法，通过特征点定位直接排除了会导致输出图像结构性错误的匹配位置，在确保了输出图像结构正确的同时，也大幅度减少了块匹配时的搜索空间，直接提升了合成速度。
(3)为了定位纹理中的全局特征，首次提出了结构坐标的概念，并将其应用于基于像素的纹理合成框架中。通过结构坐标的匹配，将纹理全局结构信息准确传递给合成算法，有效改善了输出图像的结构，同时大幅度减少了像素匹配时的搜索空间，提升了合成速度。
The goal of texture synthesis is to produce texture. It is an important research area in both computer graphics and image processing. Sample-based texture synthesis was a new technique proposed in recent years. It takes small texture image as input to synthesis same kind of texture but in arbitrary size. This technique is widely used in producing virtual reality scene and satisfies the great need in application areas such as movie industry and computer game. With the rapid development of computer 3D graphics ability and the great need of large size and high quality texture in recent years, sample-based texture synthesis became a hot research area.
The goal of sample-based texture synthesis is high quality output image, synthesis speed that satisfies real time application and full automatic synthesis process. With the large amount work of researchers these years, sample-based texture synthesis technique has achieved much. The main challenge of this area currently lies in synthesis speed and the quality of synthesized image. In order to solve these problems, this thesis aims at fast and high quality texture synthesis algorithm.
Main work of this thesis includes the following:
(1) Global feature recognition and extraction for structured texture. A new structural pattern analysis method which is used to recognize feature point distribution pattern in structured texture is firstly proposed in this thesis. The method first clusters pixels in the texture according to the rule of neighborhood similarity, then selects a pixel group as the feature point group due to pixel neighborhood difference and analyses the distribution of pixels in this group, and finally, the method recognizes the feature point mesh. With the mesh, it is convenient to capture the global characteristics of texture. Thus the method can assist much in texture synthesis related applications. Many experiments shows that the method exhibit high generality to periodic and pseudo-periodic structured textures and can accurately recognize feature point mesh in a highly stochastic texture. Bases on the mesh statistic data, the method also quantizes macro scope randomness of texture. The quantized texture randomness parameter faithfully reflects the regularity of texture macro scope structure.
(2) Feature point locating based texture synthesis algorithm. Bases on the traditional framework of patch-based texture synthesis, this thesis proposes texture synthesis algorithm using feature point locating. In the preprocessing phase, the algorithm uses structural pattern analysis on the sample image and performs global approximate feature point locating for the output image based on. the statistic data get. In the border fix phase, based on the traditional border cut technique, the algorithm proposes and uses a enhanced border fix method. The method splits the high and low frequency image component and deal with them separately to get smoother transition effect. It helps to improve image quality. It can be proved both in theory and in experiments that feature point locating assures the elimination of large scale structural error in the output image and at the same, searching space when performing patch matching is reduced more or less due to texture randomness. For most structured texture, searching space can be reduced not less than 1 to 2 decimal levels. This accelerates the texture synthesis process directly.
(3) Structural coordinate based texture synthesis algorithm. Based on the traditional framework of pixel-based texture synthesis, this thesis proposes a texture synthesis algorithm which bases on structural coordinate matching. The algorithm firstly brings forward the concept of structural coordinate. In the pixel synthesis phase, differences of both structural coordinate and neighborhood color are used as matching principle. By the means of setting threshold value for structural coordinate matching, the algorithm offers the control on output texture randomness. Both theories and experiments shows that the using of structural coordinate matching remarkably improves the structure of synthesis texture and effectively makes up the lack in reproducing large scale texture characteristic. At the same time, searching space when performing global pixel matching decreases more or less due to texture randomness. This effectively accelerates the texture synthesis process.
(4) Texture optimization algorithm using dynamic feature point matching. Bases on the optimization based texture synthesis algorithm, this thesis proposes a texture synthesis algorithm with feature point matching. In the preprocessing phase, the algorithm uses the periodic location of feature point as a rule to generate an optimized initial patch set. In the patch matching phase, the algorithm checks and assures the reasonable distance between feature points from different patches, and adjusts feature point mesh dynamically. Theories and experiments shows that initial patch set generated using feature point matching can accelerate the convergence of algorithm a lot. And feature point relative position check when matching patches can effectively assure the structure correctness of the output image.
Main innovation points of the thesis are:
(1) A firstly proposed structural pattern analysis method. It can effectively recognize and locate global characteristic of texture. It is a powerful assisting tool for conventional texture synthesis algorithms.
(2) The proposed feature point locating based texture synthesis algorithm. The algorithm excludes the matching position which leads to structural errors in output image by means of feature point locating. This assures the correct structure of output image and greatly reduces the searching space when matching patches. It improves the synthesis speed directly.
(3) In order to locate texture global characteristic, this thesis firstly brings forward the concept of structural coordinate, and then uses it in the framework of pixel-based texture synthesis. Through the matching of structural coordinate, global structural information of texture can be accurately passed to synthesis algorithm. This effectively improves output image structure and greatly decreases the searching space when matching pixels. It improves synthesis speed.
引文
[1] E. Catmull. A Subdivision Algorithm for Computer Display of Curved Surfaces. PhD thesis, Dept. of CS, University of Utah, Dec. 1974.
[2] S-H. Paul. Survey of Texture Mapping. IEEE Computer Graphics and Applications. Nov. 1986. pp. 56-67.
[3] J. Maillot, H. Yahia and A. Verroust. Interactive Texture Mapping. In Proceedings of the ACM SIGGRAPH 1993. pp. 27-35.
[4] J. Dischler, and D. Ghazanfarpour. A Survey of 3D Texturing. Computers and Graphics. 25(10), 2001.
[5] D-R. Peachey. Solid Texturing of Complex Surfaces. Computer Graphics. 19(3), 1985. pp. 279-286.
[6] J. Dorsey, A. Edelman and J. Legakis et al. Modeling and Rendering of Weathered Stone. In Conference Proceedings of the ACM SIGGRAPH 1999. pp. 225-234.
[7] S-P. Worley. A Cellular Texture Basis Function. In Conference Proceedings of the ACM SIGGRAPH 1996. pp. 291-294.
[8] A. Witkin and M. Kass. Reaction-Diffusion Textures. Computer Graphics (SIGGRAPH 1991 Proceedings), v25, pp. 299-308.
[9] J-S. De Bonet. Multi-resolution Sampling Procedure for Analysis and Synthesis of Texture Images. In Conference Preceedings of SIGGRAPH 1997, pp. 361-368.
[10] D-J. Heeger and J-R. Bergen. Pyramid-based Texture Analysis/Synthesis. In Proceedings of SIGGRAPH 1995. pp. 229-238.
[11] J. Portilla and E-F. Simoncelli. Texture Modeling and Synthesis using Joint Statistics of Complex Wavelet Coefficients. In Proceedings of IEEE Workshop on Staticstical and Computational Theories of Vision, Fort Collins, CO, 1999.
[12] E-P. Simoncelli and J. Portilla. Texture Characterization via Joint Statistics of Wavelet Coefficient Magnitudes. The Fifth International Conference on Image Processing, v. 1, 1998. pp. 62-66.
[13] A-A. Efros and T-K. Leung. Texture Synthesis by Non-parametric Sampling. International Conference on Computer Vision 1999. pp. 1033-1038.
[14] L-Y. Wei and M. Levoy. Fast Texture Synthesis using Tree-structured Vector Quantization. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics, 2000. pp.479-488.
[15] M. Ashikhmin. Synthesizing natural textures. In Proceedings of the ACM symposium on interactive 3D graphics, 2001. pp.217-226.
[16] Z. Steve and G. Michael. Jump map-based interactive texture synthesis. ACM Transactions on Graphics, 23(4), 2004. pp.99-104.
[17] Z. Steve and G. Michael. Towards Real-time Texture Synthesis with the Jump Map. In Proceedings of the Eurographics Symposium on Rendering 2002. pp.99-104.
[18] S. Lefebvre and H. Hoppe. Parallel Controllable Texture Synthesis. In Proceedings of the ACM SIGGRAPH 2005.
[19] A. Hertzmann C-E. Jacobs and N. Oliver et al. Image Analogies. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics 2001. pp.327-340.
[20] P. Song, X-X. Meng and C-H. Tu et al. Texton-based Texture Synthesis. In Proceedings of SPIE-The International Society for Optical Engineering, v 5444, Fourth International Conference on Virtual Reality and Its Applications in Industry 2004. pp. 138-144.
[21] 徐晓刚，于金辉，马利庄．多种子快速纹理合成．中国图象图形学报．7(10)， 2002．pp．995-999．
[22] L-Y. Wei and M. Levoy. Order-independent Texture Synthesis. Tech. Rep. TR-2002-01, Stanford University CS Department. 2002.
[23] J. Singh and J. Dana Kristin. Clustering and Blending for Texture Synthesis. Pattern Recognition Letters. 25(6), 2004. pp.619-629.
[24] P. Zhang and S-L. Peng. Color Texture Synthesis Based on Structure. In Proceedings of SPIE-The International Society for Optical Engineering. 5286(2), 2003. pp.577-582.
[25] 张蓬，彭思龙．基于结构的纹理合成．计算机辅助设计与图形学学报．16(3)， 2004．pp．290-296．
[26] Y. Xu, B.Guo, and .H-Y. Shum. Chaos Mosaic: Fast and Memory Efficient Texture Synthesis. Tech. Rep. MSR-TR-2000-32, Microsoft Research, 2000.
[27] T-Y. Lee and C-R. Yan. Feature-based Texture Synthesis. Lecture Notes in Computer Science, 3482(3), Computational Science and Its Applications-ICCSA 2005: International Conference, Proceedings, 2005, pp. 1043-1049.
[28] 董朋朋，叶中付．基于邻域子快相关的快速纹理合成．数据采集与处理． 20(2)，2005．pp．218-222．
[29] A. Efros and W. Freeman. Image Quilting for Texture Synthesis and Transfer. In Proceedings of the ACM SIGGRAPH 2001. pp.341-346.
[30] J-M. Dischler, K. Maritaud and B. Levy et al. Texture Particles. Computer Graphics Forum. 21 (3), 2002. pp.401-410.
[31] V. Kawatra, A. Schodl and I. Essa. Graphcut Textures: Image and Video Synthesis Using Graph Cuts. In Proceedings of the ACM SIGGRAPH 2003. pp.277-286.
[32] F. Dong, H. Lin and C. Gordon. Cutting and Pasting Irregularly Shaped Patches for Texture Synthesis. Computer Graphics Forum, 24(1), 2005. pp. 17-26.
[33] L. Liang, C. Liu and Y-Q. Xu et al. Realtime Texture Synthesis by Patch-based Sampling. ACM Transactions on Graphics. 20(3), 2001. pp.127-150.
[34] Q. Wu and Y. Yu. Feature Matching and Deformation for Texture Synthesis. ACM Transactions on Graphics. 23(4). Proceedings of the ACM SIGGRAPH 2004. pp.364-367.
[35] W-W-L. Lam and B. Zeng. Fast Texture Synthesis by Feature Matching. IEEE International Conference on Image Processing, v2, 2001. pp.614-617.
[36] N. Andrew and A. Marc. Hybrid Texture Synthesis. In Proceedings of Eurographics Symposium on Rendering 2003. pp.97-105.
[37] 叶永凯，顾耀林．一种改进的纹理合成算法．微电子学与计算机．22(5)， 2005．pp．66-69．
[38] Y. Meng, Y. Zhang and W-H. Li et al. Image Analogy Using Patch-based Texture Synthesis. International Conference on Communications, Circuits and Systems, v2, pp.974-978.
[39] D-S. Wickramanayake, E-A. Edirisinghe, and H-E. Bez. Zerotree Wavelet Based Image Quilting for Fast Texture Synthesis. Lecture Notes in Computer Science, 3522(1), Pattern Recognition and Image Analysis: Second Iberian Conference, IbPRIA 2005. Proceedings, 2005, pp.384-391.
[40] D-S. Wickramanayake, E-A. Edirisinghe and H-E. Bez. Fast Wavelet Transform Domain Texture Synthesis. In Proceedings of SPIE-The International Society for Optical Engineering, 5308(2), Visual Communications and Image Processing 2004, pp1979-987.
[41] 刘利娟，叶正麟，古元亭．一种近似周期性纹理的合成方法．计算机工程与应用．2005．11．pp．42-44．
[42] 杨刚，王文成，吴恩华．基于边界图的纹理合成方法．计算机研究与发展．42(12)，2005．pp．2118-2125．
[43] Y. Zhang, Y. Meng,, W-H. Li et al. Texture Synthesis Using Particle Swarm Optimization. International Conference on Communications, Circuits and Systems, v2, 2004. pp.969-973.
[44] 张岩，李文辉，孟宇，庞云阶．应用PSO的快速纹理合成算法．计算机研究与发展，42(3)．pp．424-430．
[45] Y. Zhang, Y. Meng and W-H. Li et al. Particle Swarm Optimization-based Texture Synthesis and Texture Transfer. In Proceedings of International Conference on Machine Learning and Cybernetics 2004, v7. pp.4037-4042.
[46] K. Vivek, E. Irfan and B. Aaron et al. Texture Optimization for Example-based Synthesis. In Proceedings of the ACM SIGGRAPH 2005.
[47] C-H. Wu, Y-Y. Lai and W-K. Tai. A Hybrid-Based Texture Synthesis Approach. Visual Computer. 20(2-3), 2004. pp. 106-129.
[48] A. Nealen and M. Alexa. Fast and High Quality Overlap Repair for Patch-Based Texture Synthesis. In Proceedings of Computer Graphics International Conference, CGI, 2004. pp. 582-585.
[49] T. Greg. Texture Synthesis on Surfaces. In Proceedings of the ACM SIGGRAPH 2001. pp. 347-354.
[50] L. Ying, A. Hertzmann and H. Biermann et al. Texture and Shape Synthesis on Surfaces. In Proceedings of Eurographics Workshop on Rendering 2001. pp. 301-312.
[51] M. Sebastian and K. David. Fast Texture Synthesis on Arbitrary Meshes. In Proceedings of Eurographics Symposium on Rendering 2003. pp.82-89.
[52] L-Y. Wei and M. Levoy. Texture Synthesis over Arbitrary Manifold Surfaces. In Proceedings of the ACM SIGGRAPH 2001. pp.355-360.
[53] C. Soler, M-P, Cani and A. Angelidis. Hierarchical Pattern Mapping. ACM Transactions on Graphics. 21 (3), 2002. pp.673-680.
[54] L-J. Wang, X-F. Gu and K. Mueller et al. Uniform Texture Synthesis and Texture Mapping Using Global Parameterization. Visual Computer. 21(8-10), 2005. pp.801-810.
[55] F-L. Wu, C-H. Mei and J-Y. Shi. Method of Direct Texture Synthesis on Arbitrary Surfaces. Journal of Computer Science and Technology, 19(5) 2004. pp. 643-649.
[56］吴福理，石教英．基于三角块的曲面纹理合成．计算机辅助设计与图形学学报．17(2)，2005． pp．236．242．
[57] R. Jagnow and J. Dorsey. Stereological techniques for solid textures. ACM Transactions on Graphics, 23(3), ACM Transactions on Graphics-Proceedings of ACM SIGGRAPH 2004, pp. 329-335.
[58] Y-S. Chen et al. Texture evolution: 3D Texture Synthesis from Single 2D Growable Texture Pattern. Visual Computer, 20(10), 2004. pp. 650-664.
[59] A. Schodl, R. Szeliski and D-H. Salesin et al. Video textures. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics 2000. pp. 489-498.
[60] D. Gianfranco, C. Alessandro and Y-N. Wu et al. Dynamic Textures. International Journal of Computer Vision, 51 (2), 2003. pp. 91-109.
[61] C. Patrizio, M. Emanuele and N. Alessandro. Video texture synthesis using fractal processes. In Proceedings of SPIE-The International Society for Optical Engineering, v 5672, Proceedings of SPIE-IS and T Electronic Imaging-Image Processing: Algorithms and Systems Ⅳ 2005, pp. 349-357.
[62] L. Lie, M. Yi and W-Y. Liu et al. Audio Restoration by Constrained Audio Texture Synthesis. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, v5, 2003. pp. 636-639.
[63] F. Neyret and M-P. Cani. Pattern-based Texturing Revisited. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics 1999. pp. 235-242.
[64] G. Gorla, V. Interrante and G. Sapiro, Texture Synthesis for 3D Shape Representation. IEEE Transactions on Visualization and Computer Graphics. 9(4), 2003. pp.512-524.
[65] L. Tonietto and M. Walter. Towards Local Control for Image-Based Texture Synthesis. In: Proceedings of SIBGRAPI 2002-ⅩⅤ Brazilian Symposium on Computer Graphics and Image Processing, Fortaleza, 2002. pp.252-258
[66] J-D. Zhang, K. Zhou and L. Velho et al. Synthesis of Progressively Variant Textures on Arbitrary Surfaces. ACM Transactions on Graphics. 22(3), 2003. pp. 295-302.
[67] 汤颖，孙汉秋，张宏鑫等．用户控制的纹理合成．计算机辅助设计与图形学学报．16(10)， 2004．pp．1413-1418．
[68] A. Zalesny, V. Ferrari and G. Caenen et al. Composite Texture Synthesis. International Journal of Computer Vision, 62(1-2), 2005. pp. 161-176.
[69] C. Dimitrios. Texture synthesis: Textons Revisited. IEEE Transactions on Image Processing, 15(3), 2006. pp. 777-787.
[70] X-G. Liu, Y-Z. Yu and H-Y Shum. Synthesizing Bi-directional Texture Functions for Real-World Surfaces. In Proceedings of SIGGRAPH 2001.
[71] G. Muller, J. Meseth and M. Sattler et al. Synthesis, and Rendering of Bidirectional Texture Functions. Computer Graphics Forum, 24(1), 2005. pp.83-109.
[72] X-G. Liu, Y-H. Hu and J-D. Zhang et al. Synthesis and Rendering of Bidirectional Texture Functions on Arbitrary Surfaces. IEEE Transactions on Visualization and Computer Graphics. 10(3), 2004. pp. 278-289.
[73] Y-K. Lai, S-M. Hu and D-X. Gu et al. Geometric texture synthesis and transfer via geometry images. ACM Symposium on Solid Modeling and Applications, SM, Proceedings SPM 2005-ACM Symposium on Solid and Physical Modeling, 2005. pp. 15-26.
[74] K-S. Law, Y. Mong. Texture Synthesis using Bi-directional texture functions for real-world surfaces, IASTED International Conference on Computer Graphics and Imaging, 2003. pp. 171-176.
[75] X. Tong, J-D. Zhang and L-G. Liu et al. Synthesis of Bidirectional Texture Functions on Arbitrary Surfaces. ACM Transactions on Graphics. 21 (3), 2002. pp. 665-672.
[76] Y. Qi and Q-P. Zhao. Texture Synthesis and Transfer from Multi Samples. In Proceedings of SPIE-The International Society for Optical Engineering. 5286(1), 2003. pp.85-90.
[77] 徐晓刚，鲍虎军，马利庄．基于相关性原理的多样图纹理合成方法．自然科学进展．12(6)，2002．pp．665-669．
[78] J-F. Wang, H-J. Hsu and H-M Wang. Constrained Texture Synthesis by Scalable Sub-Patch Algorithm. IEEE International Conference on Multimedia and Expo (ICME), v1, 2004. pp. 635-638.
[79] B. Levy. Constrained Texture Mapping for Polygonal Meshes. In Proceedings of SIGGRAPH 2001.
[80] Y-H. Hu and R-A Sambhare. Constrained Texture Synthesis for Image Post Porcessing. IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, v3, 2003. pp.281-284.
[81] Chaur-Chin Chen and Chien-Chang Chen. Texture synthesis: A Review and Experiments. Journal of Information Science and Engineering. 19(2), 2003. pp. 371-380.
[82] V. Manian and R. Vasquez. Texture Analysis and Synthesis: A Review of Recent Advances. In Proceedings of SPIE-The International Society for Optical Engineering, v5108, 2003. pp.242-250.
[83] 徐晓刚，鲍虎军，马利庄．纹理合成技术研究．计算机研究与发展．39(11)，2002．PP．1405-1411．
[84] R-D. Bonetto, E. Forlerer and J-L. Ladaga. Texture Characterization of Digital Images Which Have a Periodicity or a Quasi-periodicity. Measurement Science and Technology. 13(9), 2002. pp. 1458-1466.
[85] Z-H. Yang, D-X. Qi and L-H. Yang. Signal Period Analysis Based on Hilbert-Huang Transform and Its Application to Texture Analysis. In Proceedings of the Third International Conference on Image and Graphics 2004. pp. 430-433.
[86] V. Starovoitov, S-Y. Jeong and R-H. Park. Texture Periodicity Detection: Features, Properties, and Comparisons. IEEE Transactions on Systems, Man, and Cybernetics Part A:S ystems and Humans. 28(6), 1998. pp.839-849.
[87] V. Starovoitov, S-Y. Jeong and R-H. Park. Investigation of Texture Periodicity Extraction. In Proceedings of SPIE-The International Society for Optical Engineering v2501/2, 1995. pp.870-881.
[88] H-C. Lin, L-L. Wang and S-N. Yang. Extracting Periodicity of a Regular Texture Based on Autocorrelation Functions. Pattern Recognition Letters. 18(5), 1997. pp.433-443.
[89] J. Sobus, B. Pourdeyhimi and J. Gerde et al. Assessing Changes in Texture Periodicity Due to Appearance Loss in Carpets. Gray Level Co-occurrence Analysis. In Textile Research Journal. 61(10), 1991. pp. 557-567.
[90] J-G. Leu. On Indexing the Periodicity of Image Textures. Image and Vision Computing. 19(13), 2001. pp.987-1000.
[91] G. Oh, S. Lee and S-Y. Shin. Fast Determination of Textural Periodicity Using Distance Matching Function: Pattern Recognition Letters. 20(2), 1999. pp.191-197.
[92] J-S. Marques. Periodicity Estimation in Textured Images Using a ML Approach. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, v5, 1994. pp.V29-32.
[93] 古元亭，叶正麟，陈飞．纹理的标准性和强标准性纹理的快速识别与合成．计算机辅助设计与图形学学报．17(4)，2005．pp．712-718．
[94] M-H. Bharati and J-F. MacGregor. Texture Analysis of Images using Principal Component Analysis. In Proceedings of SPIE-The International Society for Optical Engineering, V4188, 2001. pp.27-37.
[95] K-I. Kim, K. Jung and S-H. Park et al. Texture Classification with Kernel Principal Component Analysis. Electronics Letters, 36(12), 2000. 1021-1022.
[96] K-I. Kim, S-H. Park and H-J. Kim et al. Kernel Principal Component Analysis for Texture Classification. IEEE Signal Processing Letters. 8(2). 2001. pp. 39-41.
[97] B. Julesz and J-R. Bergen. Textons, the Fundamental Elements In Preattentive Vision and Perception of Textures. Bell System Technical Journal. 62(6), pt3, 1983. pp.1619-1645.
[98] B. Julesz. Texton Theory of Two-dimensional and Three-dimensional Vision. In Proceedings of the International Society for Optical Engineering, v367, 1983.
[99] T. Leung and J. Malik. Recognizing Surfaces using Three-dimensional Textons. In Proceedings of the IEEE International Conference on Computer Vision, v2, 1999. pp.1010-1017.
[100] T. Leung and J. Malik. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons. International Journal of Computer Vision. 43(1), 2001. pp. 29-44.
[101] T. PAVLIDIS. Algorithms for Graphics and Image Processing. Computer Science Press 1982.
[102] R. Szeliski and H-Y. Shum. Creating FUll View Panoramic Mosaics and Environment Maps. In Proceedings of SIGGRAPH 1997. pp.251-258.
[103] D-M.Mount. ANN Programming Manual. Department of Computer Science, University of Maryland, College Park, Maryland. 1998.
[104] N-E. Huang, Z. Shen and S-R. Long et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of Royal Society of London(Series A). 454 (1), 1998. pp.903～995.
[105] S. Nene and S. Nayar. A Simple Algorithm for Nearest Neighbor Search in High Dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence. vl 9, 1997. pp.989-1003.
[106] A. Gersho and R-M. Gray. Vector Quantization and Signal Compression. Kluwer Academic Publishers. 1992.
[107] A. Boggess and F-J. Narcowich. 小波与傅立叶分析基础，电子工业出版社．2004.
[108] 章毓晋．图象处理和分析．清华大学出版社．1999．
[2] S-H. Paul. Survey of Texture Mapping. IEEE Computer Graphics and Applications. Nov. 1986. pp. 56-67.
[3] J. Maillot, H. Yahia and A. Verroust. Interactive Texture Mapping. In Proceedings of the ACM SIGGRAPH 1993. pp. 27-35.
[4] J. Dischler, and D. Ghazanfarpour. A Survey of 3D Texturing. Computers and Graphics. 25(10), 2001.
[5] D-R. Peachey. Solid Texturing of Complex Surfaces. Computer Graphics. 19(3), 1985. pp. 279-286.
[6] J. Dorsey, A. Edelman and J. Legakis et al. Modeling and Rendering of Weathered Stone. In Conference Proceedings of the ACM SIGGRAPH 1999. pp. 225-234.
[7] S-P. Worley. A Cellular Texture Basis Function. In Conference Proceedings of the ACM SIGGRAPH 1996. pp. 291-294.
[8] A. Witkin and M. Kass. Reaction-Diffusion Textures. Computer Graphics (SIGGRAPH 1991 Proceedings), v25, pp. 299-308.
[9] J-S. De Bonet. Multi-resolution Sampling Procedure for Analysis and Synthesis of Texture Images. In Conference Preceedings of SIGGRAPH 1997, pp. 361-368.
[10] D-J. Heeger and J-R. Bergen. Pyramid-based Texture Analysis/Synthesis. In Proceedings of SIGGRAPH 1995. pp. 229-238.
[11] J. Portilla and E-F. Simoncelli. Texture Modeling and Synthesis using Joint Statistics of Complex Wavelet Coefficients. In Proceedings of IEEE Workshop on Staticstical and Computational Theories of Vision, Fort Collins, CO, 1999.
[12] E-P. Simoncelli and J. Portilla. Texture Characterization via Joint Statistics of Wavelet Coefficient Magnitudes. The Fifth International Conference on Image Processing, v. 1, 1998. pp. 62-66.
[13] A-A. Efros and T-K. Leung. Texture Synthesis by Non-parametric Sampling. International Conference on Computer Vision 1999. pp. 1033-1038.
[14] L-Y. Wei and M. Levoy. Fast Texture Synthesis using Tree-structured Vector Quantization. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics, 2000. pp.479-488.
[15] M. Ashikhmin. Synthesizing natural textures. In Proceedings of the ACM symposium on interactive 3D graphics, 2001. pp.217-226.
[16] Z. Steve and G. Michael. Jump map-based interactive texture synthesis. ACM Transactions on Graphics, 23(4), 2004. pp.99-104.
[17] Z. Steve and G. Michael. Towards Real-time Texture Synthesis with the Jump Map. In Proceedings of the Eurographics Symposium on Rendering 2002. pp.99-104.
[18] S. Lefebvre and H. Hoppe. Parallel Controllable Texture Synthesis. In Proceedings of the ACM SIGGRAPH 2005.
[19] A. Hertzmann C-E. Jacobs and N. Oliver et al. Image Analogies. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics 2001. pp.327-340.
[20] P. Song, X-X. Meng and C-H. Tu et al. Texton-based Texture Synthesis. In Proceedings of SPIE-The International Society for Optical Engineering, v 5444, Fourth International Conference on Virtual Reality and Its Applications in Industry 2004. pp. 138-144.
[21] 徐晓刚，于金辉，马利庄．多种子快速纹理合成．中国图象图形学报．7(10)， 2002．pp．995-999．
[22] L-Y. Wei and M. Levoy. Order-independent Texture Synthesis. Tech. Rep. TR-2002-01, Stanford University CS Department. 2002.
[23] J. Singh and J. Dana Kristin. Clustering and Blending for Texture Synthesis. Pattern Recognition Letters. 25(6), 2004. pp.619-629.
[24] P. Zhang and S-L. Peng. Color Texture Synthesis Based on Structure. In Proceedings of SPIE-The International Society for Optical Engineering. 5286(2), 2003. pp.577-582.
[25] 张蓬，彭思龙．基于结构的纹理合成．计算机辅助设计与图形学学报．16(3)， 2004．pp．290-296．
[26] Y. Xu, B.Guo, and .H-Y. Shum. Chaos Mosaic: Fast and Memory Efficient Texture Synthesis. Tech. Rep. MSR-TR-2000-32, Microsoft Research, 2000.
[27] T-Y. Lee and C-R. Yan. Feature-based Texture Synthesis. Lecture Notes in Computer Science, 3482(3), Computational Science and Its Applications-ICCSA 2005: International Conference, Proceedings, 2005, pp. 1043-1049.
[28] 董朋朋，叶中付．基于邻域子快相关的快速纹理合成．数据采集与处理． 20(2)，2005．pp．218-222．
[29] A. Efros and W. Freeman. Image Quilting for Texture Synthesis and Transfer. In Proceedings of the ACM SIGGRAPH 2001. pp.341-346.
[30] J-M. Dischler, K. Maritaud and B. Levy et al. Texture Particles. Computer Graphics Forum. 21 (3), 2002. pp.401-410.
[31] V. Kawatra, A. Schodl and I. Essa. Graphcut Textures: Image and Video Synthesis Using Graph Cuts. In Proceedings of the ACM SIGGRAPH 2003. pp.277-286.
[32] F. Dong, H. Lin and C. Gordon. Cutting and Pasting Irregularly Shaped Patches for Texture Synthesis. Computer Graphics Forum, 24(1), 2005. pp. 17-26.
[33] L. Liang, C. Liu and Y-Q. Xu et al. Realtime Texture Synthesis by Patch-based Sampling. ACM Transactions on Graphics. 20(3), 2001. pp.127-150.
[34] Q. Wu and Y. Yu. Feature Matching and Deformation for Texture Synthesis. ACM Transactions on Graphics. 23(4). Proceedings of the ACM SIGGRAPH 2004. pp.364-367.
[35] W-W-L. Lam and B. Zeng. Fast Texture Synthesis by Feature Matching. IEEE International Conference on Image Processing, v2, 2001. pp.614-617.
[36] N. Andrew and A. Marc. Hybrid Texture Synthesis. In Proceedings of Eurographics Symposium on Rendering 2003. pp.97-105.
[37] 叶永凯，顾耀林．一种改进的纹理合成算法．微电子学与计算机．22(5)， 2005．pp．66-69．
[38] Y. Meng, Y. Zhang and W-H. Li et al. Image Analogy Using Patch-based Texture Synthesis. International Conference on Communications, Circuits and Systems, v2, pp.974-978.
[39] D-S. Wickramanayake, E-A. Edirisinghe, and H-E. Bez. Zerotree Wavelet Based Image Quilting for Fast Texture Synthesis. Lecture Notes in Computer Science, 3522(1), Pattern Recognition and Image Analysis: Second Iberian Conference, IbPRIA 2005. Proceedings, 2005, pp.384-391.
[40] D-S. Wickramanayake, E-A. Edirisinghe and H-E. Bez. Fast Wavelet Transform Domain Texture Synthesis. In Proceedings of SPIE-The International Society for Optical Engineering, 5308(2), Visual Communications and Image Processing 2004, pp1979-987.
[41] 刘利娟，叶正麟，古元亭．一种近似周期性纹理的合成方法．计算机工程与应用．2005．11．pp．42-44．
[42] 杨刚，王文成，吴恩华．基于边界图的纹理合成方法．计算机研究与发展．42(12)，2005．pp．2118-2125．
[43] Y. Zhang, Y. Meng,, W-H. Li et al. Texture Synthesis Using Particle Swarm Optimization. International Conference on Communications, Circuits and Systems, v2, 2004. pp.969-973.
[44] 张岩，李文辉，孟宇，庞云阶．应用PSO的快速纹理合成算法．计算机研究与发展，42(3)．pp．424-430．
[45] Y. Zhang, Y. Meng and W-H. Li et al. Particle Swarm Optimization-based Texture Synthesis and Texture Transfer. In Proceedings of International Conference on Machine Learning and Cybernetics 2004, v7. pp.4037-4042.
[46] K. Vivek, E. Irfan and B. Aaron et al. Texture Optimization for Example-based Synthesis. In Proceedings of the ACM SIGGRAPH 2005.
[47] C-H. Wu, Y-Y. Lai and W-K. Tai. A Hybrid-Based Texture Synthesis Approach. Visual Computer. 20(2-3), 2004. pp. 106-129.
[48] A. Nealen and M. Alexa. Fast and High Quality Overlap Repair for Patch-Based Texture Synthesis. In Proceedings of Computer Graphics International Conference, CGI, 2004. pp. 582-585.
[49] T. Greg. Texture Synthesis on Surfaces. In Proceedings of the ACM SIGGRAPH 2001. pp. 347-354.
[50] L. Ying, A. Hertzmann and H. Biermann et al. Texture and Shape Synthesis on Surfaces. In Proceedings of Eurographics Workshop on Rendering 2001. pp. 301-312.
[51] M. Sebastian and K. David. Fast Texture Synthesis on Arbitrary Meshes. In Proceedings of Eurographics Symposium on Rendering 2003. pp.82-89.
[52] L-Y. Wei and M. Levoy. Texture Synthesis over Arbitrary Manifold Surfaces. In Proceedings of the ACM SIGGRAPH 2001. pp.355-360.
[53] C. Soler, M-P, Cani and A. Angelidis. Hierarchical Pattern Mapping. ACM Transactions on Graphics. 21 (3), 2002. pp.673-680.
[54] L-J. Wang, X-F. Gu and K. Mueller et al. Uniform Texture Synthesis and Texture Mapping Using Global Parameterization. Visual Computer. 21(8-10), 2005. pp.801-810.
[55] F-L. Wu, C-H. Mei and J-Y. Shi. Method of Direct Texture Synthesis on Arbitrary Surfaces. Journal of Computer Science and Technology, 19(5) 2004. pp. 643-649.
[56］吴福理，石教英．基于三角块的曲面纹理合成．计算机辅助设计与图形学学报．17(2)，2005． pp．236．242．
[57] R. Jagnow and J. Dorsey. Stereological techniques for solid textures. ACM Transactions on Graphics, 23(3), ACM Transactions on Graphics-Proceedings of ACM SIGGRAPH 2004, pp. 329-335.
[58] Y-S. Chen et al. Texture evolution: 3D Texture Synthesis from Single 2D Growable Texture Pattern. Visual Computer, 20(10), 2004. pp. 650-664.
[59] A. Schodl, R. Szeliski and D-H. Salesin et al. Video textures. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics 2000. pp. 489-498.
[60] D. Gianfranco, C. Alessandro and Y-N. Wu et al. Dynamic Textures. International Journal of Computer Vision, 51 (2), 2003. pp. 91-109.
[61] C. Patrizio, M. Emanuele and N. Alessandro. Video texture synthesis using fractal processes. In Proceedings of SPIE-The International Society for Optical Engineering, v 5672, Proceedings of SPIE-IS and T Electronic Imaging-Image Processing: Algorithms and Systems Ⅳ 2005, pp. 349-357.
[62] L. Lie, M. Yi and W-Y. Liu et al. Audio Restoration by Constrained Audio Texture Synthesis. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, v5, 2003. pp. 636-639.
[63] F. Neyret and M-P. Cani. Pattern-based Texturing Revisited. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics 1999. pp. 235-242.
[64] G. Gorla, V. Interrante and G. Sapiro, Texture Synthesis for 3D Shape Representation. IEEE Transactions on Visualization and Computer Graphics. 9(4), 2003. pp.512-524.
[65] L. Tonietto and M. Walter. Towards Local Control for Image-Based Texture Synthesis. In: Proceedings of SIBGRAPI 2002-ⅩⅤ Brazilian Symposium on Computer Graphics and Image Processing, Fortaleza, 2002. pp.252-258
[66] J-D. Zhang, K. Zhou and L. Velho et al. Synthesis of Progressively Variant Textures on Arbitrary Surfaces. ACM Transactions on Graphics. 22(3), 2003. pp. 295-302.
[67] 汤颖，孙汉秋，张宏鑫等．用户控制的纹理合成．计算机辅助设计与图形学学报．16(10)， 2004．pp．1413-1418．
[68] A. Zalesny, V. Ferrari and G. Caenen et al. Composite Texture Synthesis. International Journal of Computer Vision, 62(1-2), 2005. pp. 161-176.
[69] C. Dimitrios. Texture synthesis: Textons Revisited. IEEE Transactions on Image Processing, 15(3), 2006. pp. 777-787.
[70] X-G. Liu, Y-Z. Yu and H-Y Shum. Synthesizing Bi-directional Texture Functions for Real-World Surfaces. In Proceedings of SIGGRAPH 2001.
[71] G. Muller, J. Meseth and M. Sattler et al. Synthesis, and Rendering of Bidirectional Texture Functions. Computer Graphics Forum, 24(1), 2005. pp.83-109.
[72] X-G. Liu, Y-H. Hu and J-D. Zhang et al. Synthesis and Rendering of Bidirectional Texture Functions on Arbitrary Surfaces. IEEE Transactions on Visualization and Computer Graphics. 10(3), 2004. pp. 278-289.
[73] Y-K. Lai, S-M. Hu and D-X. Gu et al. Geometric texture synthesis and transfer via geometry images. ACM Symposium on Solid Modeling and Applications, SM, Proceedings SPM 2005-ACM Symposium on Solid and Physical Modeling, 2005. pp. 15-26.
[74] K-S. Law, Y. Mong. Texture Synthesis using Bi-directional texture functions for real-world surfaces, IASTED International Conference on Computer Graphics and Imaging, 2003. pp. 171-176.
[75] X. Tong, J-D. Zhang and L-G. Liu et al. Synthesis of Bidirectional Texture Functions on Arbitrary Surfaces. ACM Transactions on Graphics. 21 (3), 2002. pp. 665-672.
[76] Y. Qi and Q-P. Zhao. Texture Synthesis and Transfer from Multi Samples. In Proceedings of SPIE-The International Society for Optical Engineering. 5286(1), 2003. pp.85-90.
[77] 徐晓刚，鲍虎军，马利庄．基于相关性原理的多样图纹理合成方法．自然科学进展．12(6)，2002．pp．665-669．
[78] J-F. Wang, H-J. Hsu and H-M Wang. Constrained Texture Synthesis by Scalable Sub-Patch Algorithm. IEEE International Conference on Multimedia and Expo (ICME), v1, 2004. pp. 635-638.
[79] B. Levy. Constrained Texture Mapping for Polygonal Meshes. In Proceedings of SIGGRAPH 2001.
[80] Y-H. Hu and R-A Sambhare. Constrained Texture Synthesis for Image Post Porcessing. IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, v3, 2003. pp.281-284.
[81] Chaur-Chin Chen and Chien-Chang Chen. Texture synthesis: A Review and Experiments. Journal of Information Science and Engineering. 19(2), 2003. pp. 371-380.
[82] V. Manian and R. Vasquez. Texture Analysis and Synthesis: A Review of Recent Advances. In Proceedings of SPIE-The International Society for Optical Engineering, v5108, 2003. pp.242-250.
[83] 徐晓刚，鲍虎军，马利庄．纹理合成技术研究．计算机研究与发展．39(11)，2002．PP．1405-1411．
[84] R-D. Bonetto, E. Forlerer and J-L. Ladaga. Texture Characterization of Digital Images Which Have a Periodicity or a Quasi-periodicity. Measurement Science and Technology. 13(9), 2002. pp. 1458-1466.
[85] Z-H. Yang, D-X. Qi and L-H. Yang. Signal Period Analysis Based on Hilbert-Huang Transform and Its Application to Texture Analysis. In Proceedings of the Third International Conference on Image and Graphics 2004. pp. 430-433.
[86] V. Starovoitov, S-Y. Jeong and R-H. Park. Texture Periodicity Detection: Features, Properties, and Comparisons. IEEE Transactions on Systems, Man, and Cybernetics Part A:S ystems and Humans. 28(6), 1998. pp.839-849.
[87] V. Starovoitov, S-Y. Jeong and R-H. Park. Investigation of Texture Periodicity Extraction. In Proceedings of SPIE-The International Society for Optical Engineering v2501/2, 1995. pp.870-881.
[88] H-C. Lin, L-L. Wang and S-N. Yang. Extracting Periodicity of a Regular Texture Based on Autocorrelation Functions. Pattern Recognition Letters. 18(5), 1997. pp.433-443.
[89] J. Sobus, B. Pourdeyhimi and J. Gerde et al. Assessing Changes in Texture Periodicity Due to Appearance Loss in Carpets. Gray Level Co-occurrence Analysis. In Textile Research Journal. 61(10), 1991. pp. 557-567.
[90] J-G. Leu. On Indexing the Periodicity of Image Textures. Image and Vision Computing. 19(13), 2001. pp.987-1000.
[91] G. Oh, S. Lee and S-Y. Shin. Fast Determination of Textural Periodicity Using Distance Matching Function: Pattern Recognition Letters. 20(2), 1999. pp.191-197.
[92] J-S. Marques. Periodicity Estimation in Textured Images Using a ML Approach. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, v5, 1994. pp.V29-32.
[93] 古元亭，叶正麟，陈飞．纹理的标准性和强标准性纹理的快速识别与合成．计算机辅助设计与图形学学报．17(4)，2005．pp．712-718．
[94] M-H. Bharati and J-F. MacGregor. Texture Analysis of Images using Principal Component Analysis. In Proceedings of SPIE-The International Society for Optical Engineering, V4188, 2001. pp.27-37.
[95] K-I. Kim, K. Jung and S-H. Park et al. Texture Classification with Kernel Principal Component Analysis. Electronics Letters, 36(12), 2000. 1021-1022.
[96] K-I. Kim, S-H. Park and H-J. Kim et al. Kernel Principal Component Analysis for Texture Classification. IEEE Signal Processing Letters. 8(2). 2001. pp. 39-41.
[97] B. Julesz and J-R. Bergen. Textons, the Fundamental Elements In Preattentive Vision and Perception of Textures. Bell System Technical Journal. 62(6), pt3, 1983. pp.1619-1645.
[98] B. Julesz. Texton Theory of Two-dimensional and Three-dimensional Vision. In Proceedings of the International Society for Optical Engineering, v367, 1983.
[99] T. Leung and J. Malik. Recognizing Surfaces using Three-dimensional Textons. In Proceedings of the IEEE International Conference on Computer Vision, v2, 1999. pp.1010-1017.
[100] T. Leung and J. Malik. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons. International Journal of Computer Vision. 43(1), 2001. pp. 29-44.
[101] T. PAVLIDIS. Algorithms for Graphics and Image Processing. Computer Science Press 1982.
[102] R. Szeliski and H-Y. Shum. Creating FUll View Panoramic Mosaics and Environment Maps. In Proceedings of SIGGRAPH 1997. pp.251-258.
[103] D-M.Mount. ANN Programming Manual. Department of Computer Science, University of Maryland, College Park, Maryland. 1998.
[104] N-E. Huang, Z. Shen and S-R. Long et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of Royal Society of London(Series A). 454 (1), 1998. pp.903～995.
[105] S. Nene and S. Nayar. A Simple Algorithm for Nearest Neighbor Search in High Dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence. vl 9, 1997. pp.989-1003.
[106] A. Gersho and R-M. Gray. Vector Quantization and Signal Compression. Kluwer Academic Publishers. 1992.
[107] A. Boggess and F-J. Narcowich. 小波与傅立叶分析基础，电子工业出版社．2004.
[108] 章毓晋．图象处理和分析．清华大学出版社．1999．