Nolte Aleke, Hennes Daniel, Izzo Dario, Blum Christian, Hafner Verena V, Gheysens Tom
Bernstein Center for Computational Neuroscience Berlin, Unter den Linden 6, 10099 Berlin, Germany.
Robotics Innovation Center, German Research Center for Artificial Intelligence, 28359 Bremen, Germany.
Biomimetics (Basel). 2017 Mar 14;2(1):3. doi: 10.3390/biomimetics2010003.
Jumping spiders are capable of estimating the distance to their prey relying only on the information from one of their main eyes. Recently, it has been shown that jumping spiders perform this estimation based on image defocus cues. In order to gain insight into the mechanisms involved in this blur-to-distance mapping as performed by the spider and to judge whether inspirations can be drawn from spider vision for depth-from-defocus computer vision algorithms, we constructed a three-dimensional (3D) model of the anterior median eye of the , a well studied species of jumping spider. We were able to study images of the environment as the spider would see them and to measure the performances of a well known depth-from-defocus algorithm on this dataset. We found that the algorithm performs best when using images that are averaged over the considerable thickness of the spider's receptor layers, thus pointing towards a possible functional role of the receptor thickness for the spider's depth estimation capabilities.
跳蛛仅依靠其一只主眼所提供的信息就能估算出与猎物之间的距离。最近的研究表明,跳蛛是基于图像散焦线索来进行这种估算的。为了深入了解蜘蛛所执行的这种模糊到距离映射的机制,并判断能否从蜘蛛视觉中获取灵感以用于基于散焦的深度计算机视觉算法,我们构建了一种研究充分的跳蛛物种——的前中眼的三维(3D)模型。我们能够像蜘蛛看到的那样研究环境图像,并在此数据集上测量一种知名的基于散焦的深度算法的性能。我们发现,当使用在蜘蛛受体层相当厚度范围内进行平均的图像时,该算法表现最佳,这表明受体厚度对于蜘蛛的深度估计能力可能具有功能性作用。