Scale detection via keypoint density maps in regular or near-regular textures

• We introduce the Keypoint Density Map (KDM) as a new tool for Image Analysis.
• We compare three different algorithms to extract the interest points of the image.
• We suppose a linear distribution of the modes of the KDMs at different scales.
• Output scale minimizes the error mode distribution vs linear model.
• We compare our method with a state of the art technique.

In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture “scale” as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A “mode” vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as the one which minimizes an error function between the related subsampled vector and the linear model. Results, compared with a state of the art method, are very encouraging.