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Robust approach to object recognition through fuzzy clustering and Hough transform based methods.

机译：通过模糊聚类和基于Hough变换的方法进行对象识别的鲁棒方法。

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Object detection from two dimensional intensity images as well as three dimensional range images is considered. The emphasis is on the robust detection of shapes such as cylinders, spheres, cones, and planar surfaces, typically found in mechanical and manufacturing engineering applications. Based on the analyses of different HT methods, a novel method, called the Fast Randomized Hough Transform (FRHT) is proposed. The key idea of FRHT is to divide the original image into multiple regions and apply random sampling method to map data points in the image space into the parameter space or feature space, then obtain the parameters of true clusters. This results in the following characteristics, which are highly desirable in any method: high computation speed, low memory requirement, high result resolution and infinite parameter space. This project also considers use of fuzzy clustering techniques, such as Fuzzy C Quadric Shells (FCQS) clustering algorithm but combines the concept of "noise prototype" to form the Noise FCQS clustering algorithm that is robust against noise. Then a novel integrated clustering algorithm combining the advantages of FRHT and NFCQS methods is proposed. It is shown to be a robust clustering algorithm having the distinct advantages such as: the number of clusters need not be known in advance, the results are initialization independent, the detection accuracy is greatly improved, and the computation speed is very fast. Recent concepts from robust statistics, such as least trimmed squares estimation (LTS), minimum volume ellipsoid estimator (MVE) and the generalized MVE are also utilized to form a new robust algorithm called the generalized LTS for Quadric Surfaces (GLTS-QS) algorithm is developed. The experimental results indicate that the clustering method combining the FRHT and the GLTS-QS can improve clustering performance. Moreover, a new cluster validity method for circular clusters is proposed by considering the distribution of the points on the circular edge. Different methods for the computation of distance of a point from a cluster boundary, a common issue in all the range image clustering algorithms, are also discussed. The performance of all these algorithms is tested using various real and synthetic range and intensity images. The application of the robust clustering methods to the experimental granular flow research is also included.
机译：考虑从二维强度图像以及三维范围图像进行对象检测。重点是对机械，制造工程应用中常见的形状（例如圆柱体，球体，圆锥体和平面）进行可靠的检测。在分析不同的HT方法的基础上，提出了一种新的方法，称为快速随机霍夫变换（FRHT）。 FRHT的关键思想是将原始图像划分为多个区域，并应用随机采样方法将图像空间中的数据点映射到参数空间或特征空间中，然后获得真实聚类的参数。这导致以下特征，在任何方法中都非常需要：高计算速度，低内存需求，高结果分辨率和无限的参数空间。该项目还考虑了使用模糊聚类技术，例如模糊C二次壳（FCQS）聚类算法，但结合了“噪声原型”的概念来形成对噪声具有鲁棒性的噪声FCQS聚类算法。然后结合FRHT和NFCQS方法的优点，提出了一种新的集成聚类算法。它被证明是一种鲁棒的聚类算法，具有明显的优势，例如：无需预先知道聚类的数量，结果独立于初始化，极大地提高了检测精度，并且计算速度非常快。鲁棒统计的最新概念，例如最小修剪平方估计（LTS），最小体积椭球估计器（MVE）和广义MVE，也被用于形成一种新的鲁棒算法，称为针对二次曲面的广义LTS（GLTS-QS）。发达。实验结果表明，结合FRHT和GLTS-QS的聚类方法可以提高聚类性能。此外，提出了一种新的考虑圆形边缘点分布的圆形聚类有效性方法。还讨论了计算点到聚类边界的距离的不同方法，这是所有距离图像聚类算法中的常见问题。所有这些算法的性能都使用各种真实的和合成的范围和强度图像进行了测试。还包括鲁棒聚类方法在实验颗粒流研究中的应用。

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