Ghosh Anarta, Petkov Nicolai
Institute of Mathematics and Computing Science, University of Groningen, The Netherlands.
IEEE Trans Pattern Anal Mach Intell. 2005 Nov;27(11):1793-804. doi: 10.1109/TPAMI.2005.225.
With inspiration from psychophysical researches of the human visual system, we propose a novel aspect and a method for performance evaluation of contour-based shape recognition algorithms regarding their robustness to incompleteness of contours. We use complete contour representations of objects as a reference (training) set. Incomplete contour representations of the same objects are used as a test set. The performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained. We call this evaluation procedure the ICR test. We consider three types of contour incompleteness, viz. segment-wise contour deletion, occlusion, and random pixel depletion. As an illustration, the robustness of two shape recognition algorithms to contour incompleteness is evaluated. These algorithms use a shape context and a distance multiset as local shape descriptors. Qualitatively, both algorithms mimic human visual perception in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours. The distance multiset method performs better than the shape context method in this test framework.
受人类视觉系统心理物理学研究的启发，我们提出了一个新的视角和一种方法，用于评估基于轮廓的形状识别算法在面对轮廓不完整时的鲁棒性。我们将物体的完整轮廓表示用作参考（训练）集。相同物体的不完整轮廓表示用作测试集。算法的性能通过识别率作为保留轮廓百分比的函数来报告。我们将此评估过程称为ICR测试。我们考虑三种类型的轮廓不完整，即逐段轮廓删除、遮挡和随机像素损耗。作为示例，评估了两种形状识别算法对轮廓不完整的鲁棒性。这些算法使用形状上下文和距离多重集作为局部形状描述符。定性地说，这两种算法在某种意义上都模仿了人类视觉感知，即识别性能随着完整程度单调增加，并且它们在随机损耗情况下表现最佳，在遮挡轮廓情况下表现最差。在这个测试框架中，距离多重集方法比形状上下文方法表现更好。
