Abstract
This chapter provides a brief introduction to early visual processing within the primary visual cortex (V1) and how we can use computational techniques to model these processes. After explaining the fundamentals of visual processing within the brain, we briefly introduce computational aspects of image comprehension and the similarities to the brain. Our brains have evolved to process visual information in a specific manner. This evolutionary trait is known as the efficient coding hypothesis and states that the use of a sparse neural response to stimuli allows for energy conservation within the brain. These sparse neural responses can be viewed as linear filters that resemble 2D Gabor wavelet codes. While there are multiple methods to represent these neural codes, we will discuss in more depth how an efficient coding technique, such as independent coding analysis (ICA), can represent these codes. We further explore the potential AI applications of human neuroanatomy related to early visual processing. Thus, this chapter depicts the bridging of human neuroanatomy and artificial intelligence by showing the similarities between early visual processing in mammals and computational parallels.
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Moye, R., Liang, C., Albert, M.V. (2022). Early Visual Processing: A Computational Approach to Understanding Primary Visual Cortex. In: Albert, M.V., Lin, L., Spector, M.J., Dunn, L.S. (eds) Bridging Human Intelligence and Artificial Intelligence. Educational Communications and Technology: Issues and Innovations. Springer, Cham. https://doi.org/10.1007/978-3-030-84729-6_12
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DOI: https://doi.org/10.1007/978-3-030-84729-6_12
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