Editing Computer vision (section)

===Neurobiology===
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   | image1    = Simplified neural network training example.svg
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   | caption1  = Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict [[starfish]] and [[sea urchin]]s, which are correlated with "nodes" that represent visual [[Feature (computer vision)|features]]. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring-textured sea urchin creates a weakly weighted association between them.
   | image2    = Simplified neural network example.svg
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   | caption2  = Subsequent run of the network on an input image (left):<ref>{{cite book|author=Ferrie, C. |author2=Kaiser, S.|year=2019|title=Neural Networks for Babies|publisher=Sourcebooks|isbn=978-1492671206}}</ref> The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a [[false positive]] result for sea urchin.<br>In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.}}
[[Neurobiology]] has greatly influenced the development of computer vision algorithms. Over the last century, there has been an extensive study of eyes, neurons, and brain structures devoted to the processing of visual stimuli in both humans and various animals. This has led to a coarse yet convoluted description of how natural vision systems operate in order to solve certain vision-related tasks. These results have led to a sub-field within computer vision where artificial systems are designed to mimic the processing and behavior of biological systems at different levels of complexity. Also, some of the learning-based methods developed within computer vision (''e.g.'' [[Artificial neural network|neural net]] and [[deep learning]] based image and feature analysis and classification) have their background in neurobiology.  The [[Neocognitron]], a neural network developed in the 1970s by [[Kunihiko Fukushima]], is an early example of computer vision taking direct inspiration from neurobiology, specifically the [[Visual cortex#Primary visual cortex (V1)|primary visual cortex]].

Some strands of computer vision research are closely related to the study of [[biological vision]]—indeed, just as many strands of [[Artificial intelligence|AI]] research are closely tied with research into human intelligence and the use of stored knowledge to interpret, integrate, and utilize visual information. The field of biological vision studies and models the physiological processes behind visual perception in humans and other animals. Computer vision, on the other hand, develops and describes the algorithms implemented in software and hardware behind artificial vision systems. An interdisciplinary exchange between biological and computer vision has proven fruitful for both fields.<ref name="TextbookP1" />