Risk evaluations for agricultural chemicals are necessary to preserve healthy populations of honey bee colonies. Field studies on whole colonies are limited in behavioural research, while results from lab studies allow only restricted conclusions on whole colony impacts. Methods for automated long-term investigations of behaviours within comb cells, such as brood care, were hitherto missing. In the present study, we demonstrate an innovative video method that enables within-cell analysis in honey bee (Apis mellifera) observation hives to detect chronic sublethal neonicotinoid effects of clothianidin (1 and 10 ppb) and thiacloprid (200 ppb) on worker behaviour and development. In May and June, colonies which were fed 10 ppb clothianidin and 200 ppb thiacloprid in syrup over three weeks showed reduced feeding visits and duration throughout various larval development days (LDDs). On LDD 6 (capping day) total feeding duration did not differ between treatments. Behavioural adaptation was exhibited by nurses in the treatment groups in response to retarded larval development by increasing the overall feeding timespan. Using our machine learning algorithm, we demonstrate a novel method for detecting behaviours in an intact hive that can be applied in a versatile manner to conduct impact analyses of chemicals, pests and other stressors.

Object classification based on shape features for video surveillance has been a research problem for number of years. The object classification accuracy depends on the type of classifier and the extracted object features used for classification. Excellent classification accuracy can be obtained with an appropriate combination of the extracted features with a particular classifier. In this paper, we propose to use an online feature selection method which gives a good subset of features while the machine learns the classification task and use these selected features for object classification. This paper also explores the impact of different kinds of shape features on the object classification accuracy and the performance of different classifiers in a typical automated video surveillance application. 10th International Conference on Information Technology 0-7695-3068-0/07 $25.00 © 2007 IEEE DOI 97 10th International Conference on Information Technology 0-7695-3068-0/07 $25.00

We present an efficient algorithm for on-road vehicle (e.g. side and rear view of cars) detection problem using cascade of boosted classifiers. Adaptive boosting based classifier in cascaded structure is one of the few good approaches for object detection. This approach filters different non-target (negative) samples in different stages of cascaded structure according to their level of similarity with target object class. The boosted weak learners are quick and efficient for initial stages only, but in later stage of cascaded structure they are not efficient enough to remove the critical false alarms. In this paper, we propose a method of cascading complex features at the later stage of cascaded classifier to enhance the detection performance. We compared the performance of local and global texture features in combination with boosted haar like features. The best performance for on-road obstacle detection is achieved by Adaboost with Haar-like feature along with SVM and Histograms of Oriented Gradients (HOG) features.

As the computer vision matures into a systems science and engineering discipline, there is a trend in leveraging latest advances in computer graphics simulations for performance evaluation, learning, and inference. However, there is an open question on the utility of graphics simulations for vision with apparently contradicting views in the literature. In this paper, we place the results from the recent literature in the context of performance characterization methodology outlined in the 90's and note that insights derived from simulations can be qualitative or quantitative depending on the degree of fidelity of models used in simulation and the nature of the question posed by the experimenter. We describe a simulation platform that incorporates latest graphics advances and use it for systematic performance characterization and tradeoff analysis for vision system design. We verify the utility of the platform in a case study of validating a generative model inspired vision hypothesis, Rank-Order consistency model, in the contexts of global and local illumination changes, and bad weather, and high-frequency noise. Our approach establishes the link between alternative viewpoints, involving models with physics based semantics and signal and perturbation semantics and confirms insights in literature on robust change detection.

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