Abstract not found

Queensland. Lists of recently-published titles of this series and of other publications are provided at the end of this report. Requests for copies of any of these documents should be addressed to the Civil Engineering Secretary. The interpretation and opinions expressed herein are solely those of the author(s). Considerable care has been taken to ensure accuracy of the material presented. Nevertheless, responsibility for the use of this material rests with the user.

transformation of the descriptors into a representation better adapted to the task, and (2) a pooling step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pooling schemes have been proposed in the literature. The goal of this paper is threefold. We seek

Aeration, flow instabilities and residual energy on pooled stepped spillways of embankment dams.

Step-pool units, common features in steep, narrow streams, are highly dynamic systems that adjust during high flows and active sediment transport conditions. Consecutive step pools in a reach form a step-pool sequence, and though these features are prevalent in nature, quantifying the stability

Abstract not found

ABSTRACT. Rhythmic sequences of steps and pools in steep mountain streams have captured the attention of numerous workers, but whether periodicities exist in the occurrence of step-pools has not been investigated in a comprehensive way, and the implications for such periodicities have not been

[1] Curran and Wilcock [2005] report an experimental study on the formation of step-pool patterns under active transport of all sediment sizes. They focus on the geomet-rical properties (step height, H, and step spacing, L) of the

Many modern visual recognition algorithms incorporate a step of spatial ‘pooling’, where the outputs of several nearby feature detectors are combined into a local or global ‘bag of features’, in a way that preserves task-related information while removing irrelevant details. Pooling is used

information. Despite of its predominance in current recognition systems, we have seen little progress to fully adapt the pooling strategy to the task at hand. In this paper, we propose a flexible parameterization of the spatial pooling step and learn the pooling regions together with the classifier. We