Bayesian Cognitive Models for 3 D Structure and

Bayesian Cognitive Models for 3 D Structure and Motion Multimodal Perception Systems 01/01/2006 - 31/12/2009 • Goals – To research generic Bayesian models to deal with fusion, multimodality, conflicts, and ambiguities in perception and apply them in artificial cognitive systems. – To answer questions such as: • • Where are the limits on optimal sensory integration behaviour? • What are the temporal aspects of sensory integration? • How do top-down influences such as learning, memory and attention affect sensory integration? • How do we solve the “correspondence problem” for sensory integration? How to answer the combination versus integration debate? • How to answer the switching versus weighing controversy? • What are the limits of crossmodal plasticity? • Motivations – A moving observer is presented with a non-static 3 D scene – how does this observer perceive: • • his own motion (egomotion); the 3 D structure of all objects in the scene; the 3 D trajectory and velocity of moving objects (independent motion)? Challenges – – Perceptual uncertainties: Biological systems: physical constraints on sensors discretisation (analogue-to-spike train) neural noise (firing apparently not due to stimuli) structural constraints on neural representations and computations – Expected Outputs Perceptual ambiguities: Artificial systems: sensor accuracy and precision discretisation (analogue-to-digital) noise not accounted by artificial perception models round-off effects and data representation limitations Development of novel perceptual computational models: 1. based on vision, audition and vestibular sensing; 2. which mimic biological multimodal perceptual fusion processes; 3. which perform perceptual fusion within a Bayesian framework. Fundação para a Ciência e a Tecnologia; Ph. D Scholarship – SRFH/BD/24628/2005 Contacts: João Filipe Ferreira, Jorge Dias {jfilipe, jorge}@isr. uc. pt Mobile Robotics Laboratory Institute of Systems and Robotics ISR – Coimbra