Examples of the fluorescence microscopy images.

<p>The two columns to the left show images from the <i>cell</i> subset, whereas the two columns to the right present images from the <i>tissue</i> subset. The scale bars correspond to 20 <i>μ</i>m. The second row shows magnified parts of the images in the first row.</p

The VL benchmark performance comparison of the three descriptors.

<p>SIFT, LPM in the two sampling variants: 16 x 16 and 32 x 32, resulting in 48- and 56-long feature vectors respectively, and SURF with the two feature vector lengths: 64 and 128. The same feature detector (SIFT) was used with all descriptors. The plots present results for the two image subsets with increasing viewpoint angles in the Oxford dataset: <i>graf</i> (upper row) and <i>wall</i> (lower row).</p

Feature matching results obtained with SIFT and the two Log-Polar Magnitude descriptor (LPM) sampling versions on the features found with the SIFT detector on the transmission electron microscopy image dataset.

<p>The first 6 image pairs contained only translation differences whereas the last two image pairs differed in magnification levels.</p

Sample images used for training the general purpose frequency mask and feature scaling coefficients of the Log-Polar Magnitude feature transform (LPM)(A) and the corresponding results (B).

<p>The first row presents two natural scene images captured in Firenze, Italy by Anders Hast. The top-right image was additionally artificially rotated (by 45 degrees) and scaled (factor 0.65 with bicubic interpolation). The second row shows two transmission electron microscopy (TEM) images of a cell section with transversely cut cilia (hair-like cell protrusions) acquired at two different magnifications.</p

The proposed feature descriptor evaluation approach.

<p>The proposed feature descriptor evaluation approach.</p

The VL benchmark performance comparison of the three descriptors.

<p>SIFT, LPM (in the two sampling variants: 16 x 16 and 32 x 32 resulting in 48- and 56-long feature vectors respectively), and SURF (with the two feature vector lengths: 64 and 128). The same feature detector (SIFT) was used with all descriptors. The plots present the results for the two image subsets with different levels of blur in the Oxford dataset: <i>bikes</i> (upper row) and <i>trees</i> (lower row).</p

Performance comparison between SIFT and LPM with the 32 x 32 sampling and 56-long feature vectors.

<p>The same feature detector (SIFT) was used with both descriptors. The plots present the results of the alternative (threshold- and RANSAC-based) evaluation framework for the two image subsets with an increasing blur in the Oxford dataset: <i>bikes</i> (upper row) and <i>trees</i> (lower row).</p

Comparison of the feature matching results obtained with SIFT and the two sampling versions of the Log Polar Magnitude descriptor (LPM) for features found with the SIFT detector.

<p>The plots present results for the fluorescence microscopy images: <i>cells</i> subset (upper row) and <i>tissue</i> subset (lower row).</p

Flowchart of the Log-Polar Magnitude feature descriptor.

<p>Each feature, defined by a center (green cross) and a radius (black circle) in the original image (A, image provided by Anders Hast), is Log-Polar Transformed (LPT) to its square representation (B). Next, Fast Fourier Transform (FFT) is used to compute the magnitude spectrum (C, log-scaled image) present in the LPT image. Finally, a frequency mask (D) is used to select the frequencies that compose the feature vector (E). The green and yellow circles in A were added as a reference and correspond to the green and yellow lines in B. The smallest sampling ring, i.e. the one closest to the feature center (marked with the green cross in A), constitutes the bottom row in B whereas the largest ring (marked with the black circle in A) corresponds to the top row in B.</p

Performance comparison between SIFT and LPM with the 32 x 32 sampling and 56-long feature vectors.

<p>The same feature detector (SIFT) was used with both descriptors. The plots present results of the alternative (threshold- and RANSAC-based) evaluation framework for the two image subsets with increasing viewpoint angles in the Oxford dataset: <i>graf</i> (upper row) and <i>wall</i> (lower row).</p