אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Motion capture is the process of recording the movement of objects or people by sensors and transforming it into computer readable format. To achieve best results, the environment needs to be optimally covered by the sensors. I will propose my latest advances in optimally placing and orienting multiple cameras for motion capture systems in risky industrial settings. Sample objectives for the optimization are to reconstruct an object most accurately, to maximally cover important regions of a cluttered 3D scene, for instance. Formally, our algorithm is a block-coordinate ascent combined with a surrogate of the objective and an exclusion area method. The efficiency of the optimization method is increased by the following properties: (i) the objective is invariant under the permutation of cameras and
(ii) placing the cameras consecutively reduces the computational costs of the objective.
Previous consecutive methods for optimal camera placement tolerate non-optimal stationary solutions. Conversely, our method is globally convergent on a continuous domain for various objectives that are quantized, non-differentiable, symmetric, costly, or black-box functions. Moreover, it can be computed in parallel.

Bio:
She is a computer scientist, mathematician, and developer who is fascinated about the interplay of complex mathematical theory and practical applications. Her research falls under the scope of Optimization, Computer Vision, Computational Geometry, and Robotics. In Her doctoral work she developed a technique for the optimal placement of multiple cameras in three-dimensional space in the context of safe human-robot cooperation.