Distance transform: a tool for the study of animal colour patterns

Summary The information in animal colour patterns plays a key role in many ecological interactions; quantification would help us to study them, but this is problematic. Comparing patterns using human judgement is subjective and inconsistent. Traditional shape analysis is unsuitable as patterns do not usually contain conserved landmarks. Alternative statistical approaches also have weaknesses, particularly as they are generally based on summary measures that discard most or all of the spatial information in a pattern. We present a method for quantifying the similarity of a pair of patterns based on the distance transform of a binary image. The method compares the whole pattern, pixel by pixel, while being robust to small spatial variations among images. We demonstrate the utility of the distance transform method using three ecological examples. We generate a measure of mimetic accuracy between hoverflies (Diptera: Syrphidae) and wasps (Hymenoptera) based on abdominal pattern and show that this correlates strongly with the perception of a model predator (humans). We calculate similarity values within a group of mimetic butterflies and compare this with proposed pairings of Müllerian comimics. Finally, we characterise variation in clypeal badges of a paper wasp (Polistes dominula) and compare this with previous measures of variation. While our results generally support the findings of existing studies that have used simpler ad hoc methods for measuring differences between patterns, our method is able to detect more subtle variation and hence reveal previously overlooked trends

Effective outlier matches pruning algorithm for rigid pairwise point cloud registration using distance disparity matrix

This study focuses on fast and robust outlier matches removal strategy to improve the efficiency and precision of initial alignment and further the quality of pairwise registration. Starts from the point matches obtained via feature detecting and matching, the distance disparity matrix derived from Euclidean invariants of rigid transformation is introduced, based on which a fast and effective pruning method is proposed to eliminate the outlier correspondences, especially the sharp ones. Then, the remaining matches are sent into the enhanced least‐square backward method to estimate an initial transformation in lesser attempts. Since most of the outliers are rejected, presented backward method could provide a finer alignment to input point clouds in higher efficiency than existing methods, and the following refining procedure converges to a more precise registration consuming fewer iterations, which have been proved in designed experiments. The thresholds employed in the pipeline are all automatically determined according to the actual resolution of input point clouds. Users are just required to control the error precision through a scale factor, in which way the inaccuracy and inconvenience of manually threshold defining are avoided