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This paper addresses the inference of probabilistic classification models using weakly supervised learning. In contrast to previous work, the use of proportion-based training data is investigated in combination to non-linear classification models. An application to fisheries acoustics and fish school classification is considered and experiments are reported for synthetic and real datasets.
This paper deals with the automation of the analysis of images depicting shape sequences. Here, a robust method for matching shape sequence images is developed. First, the successive shapes are represented by a level-set representation, then the algorithm of registration is carried out on this level-set representation. It considers the levels as elements in a shape space and the corresponding matching...
We propose a new method for the estimation of fish abundance from both acoustic data and some trawl hauls catches. In this work, we operate at a global level and we aim at estimating fish abundance from these images and not to identify the species of each school. We associate each trawl catch to the nearest acoustic image and we describe each image by a set of global statistical distributions estimated...
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