-Currently available morphometric and genetic techniques that can accurately identify Africanized honey bees are both costly and time consuming. We tested two new morphometric techniques (ABIS -Automatic Bee Identification System and geometric morphometrics analysis) on samples consisting of digital images of five worker forewings per colony. These were collected from 394 colonies of Africanized bees from all over Brazil and from colonies of African bees, Apis mellifera scutellata (n = 14), and European bees, A. m. ligustica (n = 10), A. m. mellifera (n = 15), and A. m. carnica (n=15) from the Ruttner collection in Oberursel, Germany (preserved specimens). Both methods required less than five minutes per sample, giving more than 99% correct identifications. There was just one misidentification (based on geometric morphometrics analysis) of Africanized bees compared with European subspecies, which would be the principal concern in newly-colonized areas, such as the southern USA. These new techniques are inexpensive, fast and precise.Africanized honey bee / morphometrics / geometric morphometrics analysis / ABIS / Apis mellifera / automatic identification
AbSTRACT. Though the replacement of European bees by Africanized honey bees in tropical America has attracted considerable attention, little is known about the temporal changes in morphological and genetic characteristics in these bee populations. We examined the changes in the morphometric and genetic profiles of an Africanized honey bee population collected near where the original African swarms escaped, after 34 years of Africanization. Workers from colonies sampled in 1968 and in 2002 were morphometrically analyzed using relative warps analysis and an Automatic Bee Identification System (ABIS). All the colonies had their mitochondrial DNA identified. The subspecies that mixed to form the Africanized honey bees were used as a comparison for the morphometric analysis. The two morphometric approaches showed great similarity of Africanized bees with the African subspecies, Apis mellifera scutellata, corroborating with other markers. We also found the population of 1968 to have the pattern of wing venation to be more similar to A. m. scutellata than the current population. The mitochondrial DNA of European origin, which was very common in the 1968 population, was not found in the current population, indicating selective pressure replacing the European with the African genome in this tropical region. Both morphometric methodologies were very effective in discriminating the A. mellifera groups; the non-linear analysis of ABIS was the most successful in identifying the bees, with more than 94% correct classifications.
ABSTRACT:In this paper, we present a fully automatic approach to localize the outlines of facade objects (windows and doors) in 3D point clouds of facades. We introduce an approach to search for the main facade wall and locate the facade objects within a probabilistic framework. Our search routine is based on Monte Carlo Simulation (MC-Simulation). Templates containing control points of curves are used to approximate the possible shapes of windows and doors. These are interpolated using parametric B-spline curves. These templates are scored in a sliding window style over the entire facade using a likelihood function in a probabilistic matching procedure. This produces many competing results for which a two layered model selection based on Bayes factor is applied. A major thrust in our work is the introduction of a 2D shape-space of similar shapes under affine transform in this architectural scene. This transforms the initial parametric B-splines curves representing the outlines of objects to curves of affine similarity in a strongly reduced dimensionality thus facilitating the generation of competing hypotheses within the search space. A further computational speedup is achieved through the clustering of the search space to disjoint regions, thus enabling a parallel implementation. We obtain state-of-the results on self-acquired data sets. The robustness of our algorithm is evaluated on 3D point clouds from image matching and LiDAR data of diverse quality.
ABSTRACT:We propose a novel fully automatic technique for roof fitting in 3D point clouds based on sequential importance sampling (SIS). Our approach makes no assumption of the nature (sparse, dense) or origin (LIDAR, image matching) of the point clouds and further distinguishes, automatically, between different basic roof types based on model selection. The algorithm comprises an inherent data parallelism, the lack of which has been a major drawback of most Monte Carlo schemes. A further speedup is achieved by applying a coarse to fine search within all probable roof configurations in the sample space of roofs. The robustness and effectiveness of our roof reconstruction algorithm is illustrated for point clouds of varying nature.
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