Predicting search time in visually cluttered scenes using the fuzzy logic approach

Meitzler, Thomas; Sohn, Euijung; Singh, Harpreet; Elgarhi, Abdelakrim; Nam, Deok Hee

doi:10.1117/1.1389866

Cited by 10 publications

(2 citation statements)

References 16 publications

(15 reference statements)

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“…The object is to classify the target (type) and assess the time taken to complete this chore. Reproduced from [5], is, Table 3, showing rows of such data. The data is used for illustration purpose only and is used in calculations only to illustrate the comparison between full neurofuzzy analysis, versus what target classification confidence we can achieve using Boolean or fuzzy techniques.…”

Section: Logic Analysismentioning

confidence: 99%

Comparison of Detection and Classification Algorithms Using Boolean and Fuzzy Techniques

Dixit

Singh

2012

Advances in Fuzzy Systems

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Modern military ranging, tracking, and classification systems are capable of generating large quantities of data. Conventional "brute-force" computational techniques, even with Moore's law for processors, present a prohibitive computational challenge, and often, the system either fails to "lock onto" a target of interest within the available duty cycle, or the data stream is simply discarded because the system runs out of processing power or time. In searching for high-fidelity convergence, researchers have experimented with various reduction techniques, often using logic diagrams to make inferences from related signal data. Conventional Boolean and fuzzy logic systems generate a very large number of rules, which often are difficult to handle due to limitations in the processors. Published research has shown that reasonable approximations of the target are preferred over incomplete computations. This paper gives a figure of merit for comparing various logic analysis methods and presents results for a hypothetical target classification scenario. Novel multiquantization Boolean approaches also reduce the complexity of these multivariate analyses, making it possible to better use the available data to approximate target classification. This paper shows how such preprocessing can reasonably preserve result confidence and compares the results between Boolean, multi-quantization Boolean, and fuzzy techniques.

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Section: Logic Analysismentioning

confidence: 99%

Comparison of Detection and Classification Algorithms Using Boolean and Fuzzy Techniques

Dixit

Singh

2012

Advances in Fuzzy Systems

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show abstract

“…The object is to classify the target (type) and assess the time taken to complete this chore. Reproduced from [1][2][3] is a table, Table 1, showing rows of such data. The data is just for illustration purpose only and is used in calculations to illustrate the comparison between full neuro-fuzzy analysis versus what target classification confidence we can achieve using Boolean or fuzzy techniques.…”

Section: Logic Analysismentioning

confidence: 99%

BDD, BNN, and FPGA on Fuzzy Techniques for Rapid System Analysis

Dixit

Singh

2012

Advances in Fuzzy Systems

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This paper looks at techniques to simplify data analysis of large multivariate military sensor systems. The approach is illustrated using representative raw data from a video-scene analyzer. First, develop fuzzy neural net relations using Matlab. This represents the best fidelity fit to the data and will be used as reference for comparison. The data is then converted to Boolean, and using Boolean Decision Diagrams (BDD) techniques, to find similar relations between input vectors and output parameter. It will be shown that such Boolean techniques offer dramatic improvement in system analysis time, and with minor loss of fidelity. To further this study, Boolean Neural Net techniques (BNN) were employed to bridge the Fuzzy Neural Network (FNN) to BDD representations of the data. Neural network approaches give an estimation method for the complexity of Boolean Decision Diagrams, and this can be used to predict the complexity of digital circuits. The neural network model can be used for complexity estimation over a set of BDDs derived from Boolean logic expressions. Experimental results show good correlation with theoretical results and give insights to the complexity. The BNN representations can be useful as a means to FPGA implementation of the system relationships and can be used in embedded processor based multi-variate situations.

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Visual salience and biological motion interact to determine camouflaged target detectability

et al. 2018

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Target visual salience and biological motion independently influence the accuracy and latency of observer detection. However, it is currently unknown how these target parameters might interact in modulating the detectability of camouflaged human targets. In two experiments, observers performed a visual target detection task. In a pilot experiment, observers detected a static human target with parametrically varied visual salience, superimposed on a complex background scene. As expected, results demonstrated varied target detectability as a function of salience, with observers showing higher hit rates and faster response times as a function of increased salience. In the Main Experiment, observers detected simulated human targets walking across a complex scene at five different speeds and three different levels of visual salience (as validated in the pilot experiment). We found strong effects of both movement rate and visual salience, and the two parameters interacted. Specifically, increasing the rate of biological motion increased detectability for even the least salient camouflage patterns. In other words, biological motion can "break" even the least conspicuous camouflage pattern. In contrast, a very salient pattern was highly detectable under static and moving conditions. Results are considered in relation to theories of camouflage detectability, and trade-offs between camouflage development efforts versus advanced training in military maneuvering.

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Predicting search time in visually cluttered scenes using the fuzzy logic approach

Cited by 10 publications

References 16 publications

Comparison of Detection and Classification Algorithms Using Boolean and Fuzzy Techniques

Comparison of Detection and Classification Algorithms Using Boolean and Fuzzy Techniques

BDD, BNN, and FPGA on Fuzzy Techniques for Rapid System Analysis

Visual salience and biological motion interact to determine camouflaged target detectability

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