Information fusion in computer vision using the fuzzy integral

Tahani, Hossein; Keller, James M.

doi:10.1109/21.57289

Cited by 363 publications

(161 citation statements)

References 11 publications

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“…In contrast, the fuzzy integrals take into account the importance of the coalition of any subset of the criteria [48]. In general, the fuzzy integral is a non-linear function that is defined with respect to the fuzzy measure such as a belief or a plausibility measure [49], and is employed in the aggregation step. As the fuzzy measure in the fuzzy integral is defined on a set of criteria, it provides precious information about the importance and relevance of the criteria to the discriminative classes.…”

Section: Fuzzy Integralmentioning

confidence: 99%

Fuzzy human motion analysis: A review

Lim

Vats

Chan

2015

Pattern Recognition

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Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

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Section: Fuzzy Integralmentioning

confidence: 99%

Fuzzy human motion analysis: A review

Lim

Vats

Chan

2015

Pattern Recognition

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“…One of the properties of these integrals is that Choquet integral is adapted for cardinal aggregation while Sugeno integral is more suitable for ordinal aggregation (more details can be found in ( [14], [11], [13])).…”

Section: Definition 1 the Sugeno Integral Of H With Respect To µ Is Dmentioning

confidence: 99%

“…The combination of several measuring features can strengthen the pixels classification as background or foreground. In a general way, the Choquet and Sugeno integrals have been successfully applied widely in classification problems [14], in decision making [11] and also in data modelling [13] to aggregate different measures. In the context of foreground detection, these integrals seem to be good candidates for fusing different measures from different features.…”

Section: Introductionmentioning

confidence: 99%

Foreground Detection Using the Choquet Integral

Baf

Bouwmans

Vachon

2008

2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services

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Foreground Detection is a key step in background subtraction problem. This approach consists in the detection of moving objects from static cameras through a classification process of pixels as foreground or background. The presence of some critical situations i.e noise, illumination changes and structural background changes produces an uncertainty in the classification of image pixels which can generate false detections. In this context, we propose a fuzzy approach using the Choquet integral to avoid the uncertainty in the classification. The experiments on different video datasets have been realized by testing different color space and by fusing color and texture features. The proposed method is characterized through robustness against illumination changes, shadows and little background changes, and it is validated with the experimental results.

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“…This is how the NDFI operates, although not the gFI (which is based on the extension principle (EP)). Second, Yager's two classes of operators, and even his related ordered weighted averages (OWAs) [11], are special cases of the CI (with respect to particular FMs) [12]. Third, and related to the second point, the NDFI and gFI are not restricted to being additive (they only need to satisfy the more general rule of monotonicity) and they can model and exploit rich interactions between rules when/if available.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Integral for Rule Aggregation in Fuzzy Inference Systems

Tomlin

Anderson

Wagner

et al. 2016

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Abstract. The fuzzy inference system (FIS) has been tuned and revamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires nonadditivity and exploitation of interactions between rules. Herein, we show how the fuzzy integral, a parametric non-linear aggregation operator, can be used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to "unrestricted" fuzzy sets, i.e., subnormal and nonconvex, makes this now possible. We explore the role of two extensions, the gFI and the NDFI, discuss when and where to apply these aggregations, and present efficient algorithms to approximate their solutions.

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Information fusion in computer vision using the fuzzy integral

Cited by 363 publications

References 11 publications

Fuzzy human motion analysis: A review

Fuzzy human motion analysis: A review

Foreground Detection Using the Choquet Integral

Fuzzy Integral for Rule Aggregation in Fuzzy Inference Systems

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