Computer Vision is commonly studied in three categories according to the degree of abstraction from the image: low-level, mid-level, and high-level. Low-level vision focuses on mapping pixel to pixel, which is best for detecting edges and features. Mid-level vision maps pixels to regions, which is used to detect three dimensional structures from motion. High-level vision maps pixels and regions to abstract categories (Huttenlocher, n.d.). In regards to human vision, images travel to the lateral geniculate nucleus(LGN), which separates the image into 2 parallel streams, the parvocellular layers containing color and fine structure, and the magnocellular layers containing contrast and motion. The parvocellular mirrors low-level vision by detecting
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I developed a computer vision system using OpenCV to determine the relationship, if any, between computer vision and human vision. Currently its application areas include image processing, high-level GUI, video analysis, object detection, machine learning, and three dimensional visualizations. In my analysis I noted that a facet of human intelligence could be paired to most application areas. For example, object detection is very similar to the process in which the human brain recognizes the shape of a human body separate from a background. Three dimensional visualization follows the same process that the human brain uses to recognize three dimensional objects. This analysis further supports the theory that artificial intelligence can be defined as …show more content…
For example, most people are aware that computer vision is used in facial recognition, surveillance, and web image searches, yet few people realize that everyday programs like, Google Street View, utilize camera calibration and image stretching techniques (Stoianov, & Zorzi, 2012, pp. 194–196). It is also generally understood that programs such as Google Translate utilize NLP to translate between two natural languages, but many are unaware of the fact that NLP’s applications also include reading handwritten addresses for the USPS. Many people are only aware of the most obvious implementations of artificial intelligence even though its influence is prevalent in an overwhelming amount
I developed a computer vision system using OpenCV to determine the relationship, if any, between computer vision and human vision. Currently its application areas include image processing, high-level GUI, video analysis, object detection, machine learning, and three dimensional visualizations. In my analysis I noted that a facet of human intelligence could be paired to most application areas. For example, object detection is very similar to the process in which the human brain recognizes the shape of a human body separate from a background. Three dimensional visualization follows the same process that the human brain uses to recognize three dimensional objects. This analysis further supports the theory that artificial intelligence can be defined as …show more content…
For example, most people are aware that computer vision is used in facial recognition, surveillance, and web image searches, yet few people realize that everyday programs like, Google Street View, utilize camera calibration and image stretching techniques (Stoianov, & Zorzi, 2012, pp. 194–196). It is also generally understood that programs such as Google Translate utilize NLP to translate between two natural languages, but many are unaware of the fact that NLP’s applications also include reading handwritten addresses for the USPS. Many people are only aware of the most obvious implementations of artificial intelligence even though its influence is prevalent in an overwhelming amount