Sentence examples for surf descriptors from inspiring English sources

These SURF descriptors are then matched against SURF descriptors from a template QR code as demonstrated in the OpenCV library.

Fig. 3 Filters used for computing SURF descriptors.

We extract SURF descriptors on six facial fiducial points which are calculated based on the detected eye markings.

We also imported diagonal and anti-diagonal filters because this can improve the description capability of the SURF descriptors.

We also imported diagonal and anti-diagonal filters to improve the description capability of the SURF descriptors.

We separately transformed the resulting SURF descriptors into a lower dimensional subspace for every facial fiducial point.

In this section, we perform thorough comparative study on feature-based image comparison with various combinations of the four feature detectors (i.e., SIFT detector, SURF detector, Harris detector, and the improved DWT-based detector) and the three feature descriptors (i.e., SIFT descriptor, SURF descriptor, and the moment invariants).

The positions of the applied SURF descriptor for local feature extraction.

Harris detector performs well when used together with SIFT or SURF descriptor, generating better balance between recall and precision.

In addition, by observing the fourth to seventh rows, we find out that the SIFT descriptor is superior to the SURF descriptor in terms of the precision.

In this study, we adopt an 8-bin T2 SURF descriptor to describe the local features, inspired by the approach of Li et al. [20].