Foveation can be defined as the organic action of directing the gaze towards a visual region of interest to acquire relevant information selectively. With the recent advent of event cameras, we believe that taking advantage of this visual neuroscience mechanism would greatly improve the efficiency of event data processing. Indeed, applying foveation to event data would allow to comprehend the visual scene while significantly reducing the amount of raw data to handle. In this respect, we demonstrate the stakes of neuromorphic foveation theoretically and empirically across several computer vision tasks, namely semantic segmentation and classification. We show that foveated event data have a significantly better trade-off between quantity and quality of the information conveyed than high- or low-resolution event data. Furthermore, this compromise extends even over fragmented datasets. Our code is publicly available online at: https://github.com/amygruel/FoveationStakes_DVS.

注视点可以定义为将视线引导至感​​兴趣的视觉区域以选择性地获取相关信息的有机行为。随着最近事件相机的出现，我们相信利用这种视觉神经科学机制将大大提高事件数据处理的效率。事实上，将注视点应用于事件数据将有助于理解视觉场景，同时显着减少要处理的原始数据量。在这方面，我们从理论上和经验上证明了神经形态注视点在多个计算机视觉任务（即语义分割和分类）中的风险。我们表明，与高分辨率或低分辨率事件数据相比，中心凹事件数据在所传达的信息的数量和质量之间具有更好的权衡。此外，这种妥协甚至延伸到碎片数据集。我们的代码可在线公开获取：https://github.com/amygruel/FoveationStakes_DVS。