Protoobject based saliency for eventdriven cameras Autonomous robots can rely on attention mechanisms to explore complex scenes and select salient stimuli relevant for behaviour. Stimulus selection should be fast to efficiently allocate available (and limited) computational resources to process in detail a subset of the otherwise overwhelmingly large sensory input. The amount of processing required is a product of the amount of data sampled by a robotxe2x80x99s sensors; while a standard RGB camera produces a fixed amount of data for every pixel of the sensor, an eventcamera produces data only for where there is a contrast change in the field of view, and does so with a lower latency. In this paper, we describe the implementation of a stateoftheart bottomup attention model, based on structuring the visual scene in terms of protoobjects. As an eventcamera encodes different visual information compared to framebased cameras, the original algorithm must be adapted and modified. We find that the eventcameraxe2x80x99s inherent detection of edges removes the need for some early stages of processing in the model. We describe the modifications, compare the eventdriven algorithm to the original, and validate the potential for use on the iCub humanoid robot.

Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception? The recent emergence of object proposal algorithms in the computer vision community shows great promise to addressing difficult problems in robotic such as object discovery and salient object detection. However, it is difficult to determine how these algorithms actually perform for realworld robot vision applications, because the standard evaluation protocol uses datasets which do not adequately account for realworld noise (motion blur, occlusion, etc.). We evaluated several stateoftheart object proposal algorithms using naturalistic datasets from the robotics community, and found a substantial performance drop across all algorithms. This suggests that many object proposal algorithms are not as generalizable as the computer vision literature purports, which can have a significant impact on how they are applied to robotics. We also conducted a study on how each algorithm is influenced by specific kinds of realworld robot vision challenges, including variable brightness, gamma correction, Gaussian blur, and Gaussian noise. Our results provide insight into certain weaknesses of object proposal algorithms, which can be used to gauge how they might be suitable for different robotics applications. It is our intent that this work will motivate future research about how to design more flexible and robust object proposal algorithms for the robotics community.