On the use of Process Mining and Machine Learning to support decision making in systems design

Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented.FUI GONTRAN

Machine Learning For Object Recognition and Scene Analysis

Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptative target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented and its strong and weak points will be underlined. In conclusion we will suggest what could be the "ideal" learning system for vision