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首页> 外文会议>Conference on Machine Vision Applications in Industrial Inspection Ⅹ Jan 21-22, 2002, San Jose, USA >Performance-scalable volumetric data classification for on-line industrial inspection
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Performance-scalable volumetric data classification for on-line industrial inspection

机译：性能可扩展的体积数据分类，用于在线工业检测

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摘要

Non-intrusive inspection and non-destructive testing of manufactured objects with complex internal structures typically requires the enhancement, analysis and visualisation of high-resolution volumetric data. Given the increasing availability of fast 3D scanning technology (e.g. cone-beam CT), enabling on-line detection and accurate discrimination of components or sub-structures, the inherent complexity of classification algorithms inevitably leads to throughput bottlenecks. Indeed, whereas typical inspection throughput requirements range from 1 to 1000 volumes per hour, depending on density and resolution, current computational capability is one to two orders -of-magnitude less. Accordingly, speeding up classification algorithms requires both reduction of algorithm complexity and acceleration of computer performance. A shape-based classification algorithm, offering algorithm complexity reduction, by using ellipses as generic descriptors of solids-of-revolution, and supporting performance-scalability, by exploiting the inherent parallelismof volumetric data, is presented. A two-stage variant of the classical Hough transform is used for ellipse detection and correlation of the detected ellipses facilitates position-, scale- and orientation-invariant component classification. Performance-scalability is achieved cost-effectively by accelerating a PC host with one or more COTS (Commercial-Off-The-Shelf) PCI multiprocessor cards. Experimental results are reported to demonstrate the feasibility and cost-effectiveness of the data-parallel classification algorithm for on-line industrial inspection applications.
机译：对具有复杂内部结构的制造对象进行非侵入式检查和非破坏性测试通常需要对高分辨率体积数据进行增强，分析和可视化。鉴于快速3D扫描技术（例如锥形束CT）的可用性不断提高，能够进行在线检测以及对组件或子结构的准确区分，分类算法固有的复杂性不可避免地会导致吞吐量瓶颈。确实，尽管典型的检查吞吐量要求范围为每小时1到1000个体积，具体取决于密度和分辨率，但当前的计算能力要小一到两个数量级。因此，加速分类算法既需要降低算法复杂度，又需要提高计算机性能。提出了一种基于形状的分类算法，该算法通过使用椭圆作为旋转实体的通用描述符来降低算法复杂度，并通过利用体积数据的固有并行性来支持性能可伸缩性。经典霍夫变换的两阶段变体用于椭圆检测，并且所检测到的椭圆的相关性有助于位置，比例和方向不变的组件分类。通过使用一个或多个COTS（现成商用）PCI多处理器卡加速PC主机，可以经济高效地实现性能可扩展性。据报道，实验结果证明了在线工业检测应用中数据并行分类算法的可行性和成本效益。

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