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21 September 2004 Wurfelspiel-based training data methods for ATR
James K. Peterson
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Proceedings Volume 5426, Automatic Target Recognition XIV; (2004) https://doi.org/10.1117/12.540426
Event: Defense and Security, 2004, Orlando, Florida, United States
Abstract
A data object is constructed from a P by M Wurfelspiel matrix W by choosing an entry from each column to construct a sequence A0A1•AM-1. Each of the PM possibilities are designed to correspond to the same category according to some chosen measure. This matrix could encode many types of data. (1) Musical fragments, all of which evoke sadness; each column entry is a 4 beat sequence with a chosen A0A1A2 thus 16 beats long (W is P by 3). (2) Paintings, all of which evoke happiness; each column entry is a layer and a given A0A1A2 is a painting constructed using these layers (W is P by 3). (3) abstract feature vectors corresponding to action potentials evoked from a biological cell's exposure to a toxin. The action potential is divided into four relevant regions and each column entry represents the feature vector of a region. A given A0A1A2 is then an abstraction of the excitable cell's output (W is P by 4). (4) abstract feature vectors corresponding to an object such as a face or vehicle. The object is divided into four categories each assigned an abstract feature vector with the resulting concatenation an abstract representation of the object (W is P by 4). All of the examples above correspond to one particular measure (sad music, happy paintings, an introduced toxin, an object to recognize)and hence, when a Wurfelspiel matrix is constructed, relevant training information for recognition is encoded that can be used in many algorithms. The focus of this paper is on the application of these ideas to automatic target recognition (ATR). In addition, we discuss a larger biologically based model of temporal cortex polymodal sensor fusion which can use the feature vectors extracted from the ATR Wurfelspiel data.
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James K. Peterson "Wurfelspiel-based training data methods for ATR", Proc. SPIE 5426, Automatic Target Recognition XIV, (21 September 2004); https://doi.org/10.1117/12.540426
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KEYWORDS
Neurons
Sensors
Automatic target recognition
Action potentials
Visual process modeling
Data modeling
Sensor fusion
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