Comparative study of mammary gland development and differentiation between beef and dairy heifers

International audienc

Discrete Morphology with Line Structuring Elements

Discrete morphological operations with line segments are notoriously hard to implement. In this paper we study different possible implementations of the line structuring element, compare them, and examine their rotation and translation invariance in the continuous-domain sense. That is, we are interested in obtaining a morphological operator that is invariant to rotations and translations of the image before sampling

Pareto-based multi-output metamodeling with active learning

When dealing with computationally expensive simulation codes or process measurement data, global surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, and splines. In addition, the cost of each simulation mandates the use of active learning strategies where data points (simulations) are selected intelligently and incrementally. When applying surrogate models to multi-output systems, the hyperparameter optimization problem is typically formulated in a single objective way. The different response outputs are modeled separately by independent models. Instead, a multi-objective approach would benefit the domain expert by giving information about output correlation, facilitate the generation of diverse ensembles, and enable automatic model type selection for each output on the fly. This paper outlines a multi-objective approach to surrogate model generation including its application to two problems