We develop a sampling extension of M-theory focused on invariance to scale
and translation. Quite surprisingly, the theory predicts an architecture of
early vision with increasing receptive field sizes and a high resolution fovea
-- in agreement with data about the cortical magnification factor, V1 and the
retina. From the slope of the inverse of the magnification factor, M-theory
predicts a cortical "fovea" in V1 in the order of $40$ by $40$ basic units at
each receptive field size -- corresponding to a foveola of size around $26$
minutes of arc at the highest resolution, $\approx 6$ degrees at the lowest
resolution. It also predicts uniform scale invariance over a fixed range of
scales independently of eccentricity, while translation invariance should
depend linearly on spatial frequency. Bouma's law of crowding follows in the
theory as an effect of cortical area-by-cortical area pooling; the Bouma
constant is the value expected if the signature responsible for recognition in
the crowding experiments originates in V2. From a broader perspective, the
emerging picture suggests that visual recognition under natural conditions
takes place by composing information from a set of fixations, with each
fixation providing recognition from a space-scale image fragment -- that is an
image patch represented at a set of increasing sizes and decreasing
resolutions.

本文提出了基于尺度和平移不变性的 M 理论采样扩展，并预测了越来越大的感受野大小和高分辨率视点的早期视觉架构。该论文认为视觉识别发生在一组凝视点上，每个凝视点提供一个空间尺度的图像片段的识别。