Speeded up robust features (SURF) can detect scale- and rotation-invariant features at high speed by relying on integral images for image convolutions. However, since the number of image convolutions greatly increases in proportion to the image size, another method for reducing the time for detecting features is required. In this letter, we propose a method, called ordinal convolution, of reducing the number of image convolutions for fast feature detection in SURF and compare it with a previous method based on sparse sampling.

This letter presents a novel edge-based blur metric that averages the ratios between the slopes and heights of edges. The metric computes the edge slopes more carefully, i.e., by averaging the edge gradients. The effectiveness of the proposed metric is confirmed by experiments with motion or Gaussian blurred real images and comparison with existing edge-based blur metrics.

In nearest neighbor distance ratio (NNDR) matching the fixed distance ratio threshold sometimes results in an insufficient number of inliers or a huge number of outliers, which is not good for robust tracking. In this letter, we propose adjusting the distance ratio threshold based on maximizing the number of inliers while maintaining the ratio of the number of outliers to that of inliers. By applying the proposed method to a model-based camera tracking system, its effectiveness is verified.