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Text Benno Premsela Lecture by Benjamin Bratton November 2015
Text Benno Premsela Lecture by Benjamin Bratton November 2015

... It is better to examine how identification works from our side of the conversation. It is clearly much easier to make a robot that a human believes to have emotions (and for which, in turn, a human has emotions, positive or negative) than it is to make a robot that actually has those emotions. The h ...
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... circumstances presented. Thus, an approach that differs from the guidelines, standing alone, does not necessarily imply that the approach was below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidel ...
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An Application of Ant Colony Optimization to Image Clustering
An Application of Ant Colony Optimization to Image Clustering

... knowledge to be available for all the thematic classes that are present in the considered dataset. On the contrary, unsupervised classification does not require predefined knowledge about the data. The main task of classification with unsupervised learning, commonly known as clustering, is to partit ...
chapter14
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... creation, storage and manipulation of models and images. • Such models come from a diverse and expanding set of fields including physical, mathematical, artistic, biological, and even conceptual (abstract) structures. • The term “computer graphics” was coined in 1960 by William Fetter to describe ne ...
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Artificial Morality: Bounded Rationality, Bounded

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Computer vision



Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. A theme in the development of this field has been to duplicate the abilities of human vision by electronically perceiving and understanding an image. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. Computer vision has also been described as the enterprise of automating and integrating a wide range of processes and representations for vision perception.As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer vision systems.Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, object pose estimation, learning, indexing, motion estimation, and image restoration.
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