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Sinopsis
A computer tool to aid in selecting the best Automatic Target Recognition (ATR) algorithm is developed. The program considers many quantifiers, accepts user-defined parameters, allows for changes in the operational environment and presents results in a meaningful way. It is written for Microsoft Excel. An ATR algorithm assigns a class label to a recognized target. General designations can include "Friend" and "Foe." The error of designating "Friend" as "Foe" as well as "Foe" as "Friend" comes with a high cost. Studying each algorithm's error can minimize this cost. Receiver Operating Characteristic (ROC) curves provide only information on the probabilities given a system state of declaring up to three class labels: "True," "False" or "Unknown." Other quantifiers, including an alternate ROC curve, are developed in this study to provide information on the probability of a system state given any of multiple declarations, which is more useful to the user. Sensitivity to prior probabilities, suggestions for user-defined parameters and areas for future research are identified as the User Interface Tool is described in detail in this thesis.
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A computer tool to aid in selecting the best Automatic Target Recognition (ATR) algorithm is developed. The program considers many quantifiers, accepts user-defined parameters, allows for changes in the operational environment and presents results in a meaningful way. It is written for Microsoft Excel. An ATR algorithm assigns a class label to a recognized target. General designations can include "Friend" and "Foe." The error of designating "Friend" as "Foe" as well as "Foe" as "Friend" comes with a high cost. Studying each algorithm's error can minimize this cost. Receiver Operating Characteristic (ROC) curves provide only information on the probabilities given a system state of declaring up to three class labels: "True," "False" or "Unknown." Other quantifiers, including an alternate ROC curve, are developed in this study to provide information on the probability of a system state given any of multiple declarations, which is more useful to the user. Sensitivity to prior probabilities, suggestions for user-defined parameters and areas for future research are identified as the User Interface Tool is described in detail in this thesis.
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Automatic Target Recognition User Interface Tool
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Automatic Target Recognition User Interface Tool
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Condición: New. KlappentextrnrnA computer tool to aid in selecting the best Automatic Target Recognition (ATR) algorithm is developed. The program considers many quantifiers, accepts user-defined parameters, allows for changes in the operational environment and. Nº de ref. del artículo: 6488059
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Automatic Target Recognition User Interface Tool
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ISBN 10: 1249593956
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Taschenbuch. Condición: Neu. Neuware - A computer tool to aid in selecting the best Automatic Target Recognition (ATR) algorithm is developed. The program considers many quantifiers, accepts user-defined parameters, allows for changes in the operational environment and presents results in a meaningful way. It is written for Microsoft Excel. An ATR algorithm assigns a class label to a recognized target. General designations can include 'Friend' and 'Foe.' The error of designating 'Friend' as 'Foe' as well as 'Foe' as 'Friend' comes with a high cost. Studying each algorithm's error can minimize this cost. Receiver Operating Characteristic (ROC) curves provide only information on the probabilities given a system state of declaring up to three class labels: 'True,' 'False' or 'Unknown.' Other quantifiers, including an alternate ROC curve, are developed in this study to provide information on the probability of a system state given any of multiple declarations, which is more useful to the user. Sensitivity to prior probabilities, suggestions for user-defined parameters and areas for future research are identified as the User Interface Tool is described in detail in this thesis. Nº de ref. del artículo: 9781249593959
Cantidad disponible: 2 disponibles