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 specific colors and acute structures while the magnocellular layers mirror mid-level vision by detecting regions and structures from contrast in motion (How Vision Works, 2015). The way in which the brain interprets this information is homologous with high-level vision. Both interpretations separate the image into subcategories that allow for the distinguishing of abstract information. These similarities all suggest that computer vision is derived from human vision alone. The process of separating an image into levels is a synthetic form of the parvocellular and magnocellular layers in human vision. OpenCV is a computer vision application program interface (API) originally made open source as an Intel Research initiative to advance CPU-intensive applications (see Appendix B for the OpenCV documentation).
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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 synthetic. Conclusion In recent years, artificial intelligence has become more prevalent in fields outside of computer science, such as economics and art. Analysis of multiple artificial intelligence systems provides a well rounded set of data as each system focuses on one of the main requirements of an artificial intelligence agent. These requirements are prevalent in all artificial intelligence agents, regardless of the application. Any analysis of artificial intelligence, however, is limited by the fact that artificial intelligence is not bound by any laws or limitations as it is tangible in the real world like chemistry or physics; this makes it is impossible to analyze future innovations in the study …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 synthetic. Conclusion In recent years, artificial intelligence has become more prevalent in fields outside of computer science, such as economics and art. Analysis of multiple artificial intelligence systems provides a well rounded set of data as each system focuses on one of the main requirements of an artificial intelligence agent. These requirements are prevalent in all artificial intelligence agents, regardless of the application. Any analysis of artificial intelligence, however, is limited by the fact that artificial intelligence is not bound by any laws or limitations as it is tangible in the real world like chemistry or physics; this makes it is impossible to analyze future innovations in the study …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