By John G. Webster (Editor)
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Extra resources for 43.Pattern Analysis and Machine Intelligence
14 Artiﬁcial Intelligence differ from the functions of the human memory. For example, the operational differences between the long-term memory (LTM) and short-term (working) memory (STM) is not addressed (26). Even if this strict dichotomy has been questioned, it still offers a ﬁrm basis for concrete analysis. The fuzziness of categories (57) has not been embedded as an integral entity in the knowledge representations. The “crispness” of the concepts is one reason for the brittleness of the inference systems, and the gap between the symbolic and the subsymbolic levels makes machine learning a difﬁcult task.
Englewood Cliffs, New Jersey: Prentice-Hall International, 1995. 59. J. Russell, Rationality and intelligence. Artiﬁcial Intelligence, 94(1–2): 57–77, 1997. 60. R. Searle, Minds, brains and programs. Behavioral and Brain Sciences, 3: 417–424, 1980. 61. G. Schyns, A modular neural network model of concept acquisition. Cognitive Science, 15: 461–508, 1991. 20 Artiﬁcial Intelligence 62. J. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World. New York: Irwin/McGraw-Hill, 2000.
Scanning is performed by the detector and the motion of the text object with respect to the detector. After an image is captured, the document transport removes the document from the scanning field. Recent advances in scanner technology have made available higher resolution, often in the range of 300 pixels per inch (ppi) to 400 ppi. Recognition methods that use features (as opposed to template matching) use resolutions of at least 200 ppi and careful consideration of gray-scale. Lower resolutions and simple thresholding tend to break thin lines or fill gaps, thus invalidating features.
43.Pattern Analysis and Machine Intelligence by John G. Webster (Editor)