"In 1966, Seymour Papert proposed the Summer Vision Project, bringing together artificial intelligence researchers for “the construction of a significant part of a visual system” over the course of just a few months. But the goal of solving many problems of computer vision proved overambitious and instead the researchers rediscovered a fact long familiar in vision science: seeing is harder than it looks.
Since the optical studies of Ibn al-Haytham in the 11th century, scientists recognized a gap between the confused mess of visual information hitting the eye and visual experience, where segmented objects appear arrayed in space with clear differences between foreground and background. The assumption for the last millennium has been that there must be unconscious judgments engaged in processing the visual information: segmentation, spatial configuration, object recognition, and so on.
Papert recognized this in part. His optimism stemmed from the idea that the different unconscious judgments necessary for understanding an image could be instantiated in different computer programs. Thus the labor could be divided among different teams, with one team writing a program to detect edges, corners, and other pixel-level information in an image, another forming continous shapes out of these low-level features, a different group arranging the shapes in three-dimensional space, and so on. While the summer project failed, the general approach remained: treat vision not as a single problem, but as a number of discrete subproblems which can be stacked one on top of another...
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Funding for computer vision was often generous because the military was being overwhelmed with spy plane and later satellite images (it was the Cold War, after all). The funding for this approach typically came from either the Department of Defense or the Advanced Research Projects Agency (ARPA, later DARPA), the US military research slush fund."
https://philippschmitt.com/archive/computer-vision-history/#:~:text=leaves%20your%20computer.-,Introduction%20Seeing%20is%20harder%20than%20it%20looks,the%20bottom%20of%20the%20page
Excited to share our new paper: 
Why it matters: Dynamic textures (e.g., fire, water, foliage) are everywhere, but modeling them in real-time has been a challenge. DynTex bridges this gap with a biologically inspired, efficient approach. 
TODAY at 
Bio-inspired selective attention mechanism
