The life and death of ATR/sensor fusion and the hope for resurrection

Rogers, Steven K.; Sadowski, C.; Bauer, Kenneth W.; Oxley, Mark E.; Kabrisky, Matthew; Rogers, Adam; Mott, Stephen D.

doi:10.1117/12.783012

Cited by 13 publications

(12 citation statements)

References 14 publications

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“…This engineering advantage is gained by exploiting the ideas described by the QUEST tenets [4]. Our kernel is motivated by our current belief of how people process information to make decisions and our proposed framework captures many of the applicable QUEST tenets to address many of the shortcomings and challenges of 3D ladar object classification.…”

Section: Implementation Of Quest Tenetsmentioning

confidence: 99%

“…Our kernel is motivated by our current belief of how people process information to make decisions and our proposed framework captures many of the applicable QUEST tenets to address many of the shortcomings and challenges of 3D ladar object classification. Throughout this section, the QUEST tenets [4] appear in bold. Our framework captures more tenets than listed, but for brevity, we only list the tenets we feel are most relevant for our purpose of object classification.…”

Section: Implementation Of Quest Tenetsmentioning

confidence: 99%

“…This approach, so-named QUEST (Qualia Exploitation of Sensor Technology) [4], presents a list of tenets to guide engineering solutions.…”

Section: Quest-based Frameworkmentioning

confidence: 99%

“…We consider the biologically inspired process for learning systems based on the ideas presented in [4] [5] which proposes a general purpose recognition and fusion approach to bridge the representational gap in computational intelligence. This approach, so-named QUEST (Qualia Exploitation of Sensor Technology) [4], presents a list of tenets to guide engineering solutions.…”

Section: Quest-based Frameworkmentioning

confidence: 99%

“…We believe that the proposed framework will allow for formation of new classes and meet many of the other challenges of object classification using the Qualia Exploitation of Sensor Technology (QUEST) paradigm described in [4]. We refer interested readers to [5] for further information regarding qualia as it relates to intelligent systems.…”

Section: Introductionmentioning

confidence: 99%

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A Qualia Framework for Ladar 3D Object Classification

Eyster

Mendenhall

Rogers

2008

2008 Seventh International Conference on Machine Learning and Applications

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Ladar provides 3D shape information that has yet to be fully exploited for object recognition or classification. This is partly due to the operating conditions, but mostly due to a representational gap in computational intelligence. This paper briefly explores some of the hurdles of object classification using ladar data and proposes a theoretical framework, based on the biological inspiration of qualia, we believe will allow us to address these operating conditions and, most importantly, this representational gap. Our framework works on concepts instead of parts, and iterates a top-down and bottom-up solution that updates the hypothesis with the accrual of evidence. This creates a system that we believe will generalize concepts, learn from experience, and even recognize the need for the addition of new classes based on its current world view.

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Section: Implementation Of Quest Tenetsmentioning

confidence: 99%

Section: Implementation Of Quest Tenetsmentioning

confidence: 99%

“…This approach, so-named QUEST (Qualia Exploitation of Sensor Technology) [4], presents a list of tenets to guide engineering solutions.…”

Section: Quest-based Frameworkmentioning

confidence: 99%

Section: Quest-based Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%