Park Minwoo, Brocklehurst Kyle, Collins Robert T, Liu Yanxi
Computer Science and Engineering Department, Pennsylvania State University, University Park, PA 16802, USA.
IEEE Trans Pattern Anal Mach Intell. 2009 Oct;31(10):1804-16. doi: 10.1109/TPAMI.2009.73.
We propose a novel and robust computational framework for automatic detection of deformed 2D wallpaper patterns in real-world images. The theory of 2D crystallographic groups provides a sound and natural correspondence between the underlying lattice of a deformed wallpaper pattern and a degree-4 graphical model. We start the discovery process with unsupervised clustering of interest points and voting for consistent lattice unit proposals. The proposed lattice basis vectors and pattern element contribute to the pairwise compatibility and joint compatibility (observation model) functions in a Markov Random Field (MRF). Thus, we formulate the 2D lattice detection as a spatial, multitarget tracking problem, solved within an MRF framework using a novel and efficient Mean-Shift Belief Propagation (MSBP) method. Iterative detection and growth of the deformed lattice are interleaved with regularized thin-plate spline (TPS) warping, which rectifies the current deformed lattice into a regular one to ensure stability of the MRF model in the next round of lattice recovery. We provide quantitative comparisons of our proposed method with existing algorithms on a diverse set of 261 real-world photos to demonstrate significant advances in accuracy and speed over the state of the art in automatic discovery of regularity in real images.
我们提出了一种新颖且强大的计算框架，用于自动检测真实世界图像中变形的二维壁纸图案。二维晶体学群理论为变形壁纸图案的底层晶格与四度图形模型之间提供了合理且自然的对应关系。我们从无监督的兴趣点聚类开始发现过程，并对一致的晶格单元提议进行投票。所提出的晶格基向量和图案元素有助于马尔可夫随机场（MRF）中的成对兼容性和联合兼容性（观测模型）函数。因此，我们将二维晶格检测公式化为一个空间多目标跟踪问题，在MRF框架内使用一种新颖且高效的均值漂移信念传播（MSBP）方法来解决。变形晶格的迭代检测和生长与正则化薄板样条（TPS）变形交织进行，后者将当前变形晶格校正为规则晶格，以确保MRF模型在下一轮晶格恢复中的稳定性。我们在261张真实世界照片的多样集合上，将我们提出的方法与现有算法进行了定量比较，以证明在自动发现真实图像中的规律性方面，相对于现有技术在准确性和速度上有显著进步。
