论文标题
2D图像功能检测器和描述符选择专家系统
2D Image Features Detector And Descriptor Selection Expert System
论文作者
论文摘要
从图像中对关键点的检测和描述是计算机视觉中有充分研究的问题。 SIFT，SURF或ORB等某些方法在计算上确实有效。本文提出了针对基于层次分类的工业零件对象识别的特定案例研究的解决方案。减少实例的数量会导致更好的性能，实际上，这就是层次分类的使用。我们证明，这种方法的性能要比仅使用Orb，Sift或Freak之类的一种方法更好，尽管较慢。
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower.
