Interactive target recognition in images using machine-learning techniques

Ariel Michaeli; Irit Camon

doi:10.1117/12.884972

5 May 2011 Interactive target recognition in images using machine-learning techniques

Ariel Michaeli, Irit Camon

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80501B (2011) https://doi.org/10.1117/12.884972
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Imagery analysis systems utilize Automatic Target Recognition (ATR) methods in order to improve the accuracy of human-based analysis and save time. Often, ATR methods perform poorly in obtaining these objectives, due to reliance on outdated prior information, while human operators possess updated information that remains unused. This paper presents an interactive target recognition (or ITR) application. The operator marks sample target pixels by an intuitive user-interface. Then machine-learning techniques generate algorithms tailored for their recognition in imagery. The resulting detection map is dynamically controlled by the operator, suiting his needs. The application enables target recognition in zero prior information environments.

Citation Download Citation

Ariel Michaeli and Irit Camon "Interactive target recognition in images using machine-learning techniques", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501B (5 May 2011); https://doi.org/10.1117/12.884972

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