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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(More)
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(More)
We present a method for detecting repeated structures, which is applied on facade images for describing the regularity of their windows. Our approach finds and explicitly represents repetitive structures and thus gives initial representation of facades. No explicit notion of a window is used; thus, the method also appears to be able to identify other(More)
The paper addresses the problem of feature selection during classification of image regions within the context of interpreting images showing highly structured objects such as buildings. We present a feature selection scheme that is connected with the classification framework Adaboost, cf. (Schapire and Singer, 1999). We constricted our weak learners on(More)
The conservation and sustainable utilization of global biodiversity necessitates the mapping and assessment of the current status and the risk of loss of biodiversity as well as the continual monitoring of biodiversity. These demands in turn require the reliable identification and comparison of animal and plant species or even subspecies. We have developed(More)
This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting methods, Adaboost and ADTboost. In our evaluation, we have focused on three different criteria: the classification error and the efficiency of the process depending on the number of most(More)
Multi-class image classification has made significant advances in recent years through the combination of local and global features. This paper proposes a novel approach called hierarchical conditional random field (HCRF) that explicitly models region adjacency graph and region hierarchy graph structure of an image. This allows to set up a joint and(More)