From uncertainty to visual exploration

Whaite, P.; Ferrie, Frank P.

doi:10.1109/34.99237

Cited by 102 publications

(13 citation statements)

References 6 publications

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“…It can be shown that the LMS estimator has a 50% breakdown point (i.e., it can tolerate up to 50% noise and outlier points), but it also has a low efficiency [43]. The weighted least squares (WLS) method is also a robust estimator [4] that has been used in computer vision [44], [45]. Its objective function is given by…”

Section: A Robust Estimatormentioning

confidence: 99%

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A robust approach to image enhancement based on fuzzy logic

Choi

Krishnapuram

1997

IEEE Trans. on Image Process.

177

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In this paper, we propose a robust approach to image enhancement based on fuzzy logic that addresses the seemingly conflicting goals of image enhancement: (i) removing impulse noise, (ii) smoothing out nonimpulse noise, and (iii) enhancing (or preserving) edges and certain other salient structures. We derive three different filters for each of the above three tasks using the weighted (or fuzzy) least squares (LS) method, and define the criteria for selecting each of the three filters. The criteria are based on the local context, and they constitute the antecedent clauses of the fuzzy rules. The overall result of the fuzzy rule-based system is the combination of the results of the individual filters, where each result contributes to the degree that the corresponding antecedent clause is satisfied. This approach gives us a powerful and flexible image enhancement paradigm. Results of the proposed method on several types of images are compared with those of other standard techniques.

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Section: A Robust Estimatormentioning

confidence: 99%

“…In [44] and [45], the inverse of the squared residual was used as the weight . It can be shown that the WLS is a particular formulation of the -estimator [48].…”

Section: A Robust Estimatormentioning

confidence: 99%

A robust approach to image enhancement based on fuzzy logic

Choi

Krishnapuram

1997

IEEE Trans. on Image Process.

177

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“…The determination of optimal sensor explorations is not new in the robotics literature. While some approaches (e.g., [10], [11]) optimize a criterion based on "visibility," other approaches (including the present one) are more concerned with "uncertainty" [12]- [14].…”

Section: Introductionmentioning

confidence: 98%

“…In [12], the optimal exploration is determined for the characterization of an unknown object. In [15], the optimal viewpoint is determined that allows a best recognition of the observed object among a given set of possible ones.…”

Section: Introductionmentioning

confidence: 99%

Minimum uncertainty explorations in the self-localization of mobile robots

Borghi

Caglioti

1998

IEEE Trans. Robot. Automat.

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The self-localization of a mobile robot within a known environment, by means of an orientable range finder, is considered. The problem of the determination of the sensor orientation which minimizes the position uncertainty of the mobile robot is addressed. An efficient technique is proposed to determine the optimal sensor exploration, given the current robot position estimate and its uncertainty. Once a tentative exploration is given, the technique avoids to take any worst exploration into account, allowing to efficiently determine the optimal one. Both location accuracy and efficiency have been analyzed in the paper. The time needed to plan the exploration is found to be well below the time needed for the sensor activation. The technique is demonstrated by experimental results on environments containing curvilinear parts.Index Terms-Curvilinear environments, nonlinear environments, odometric navigation, pose estimate updating, range measurements, range scanning, robot navigation, robot selflocalization, sensor exploration, sensor planning, uncertaintydriven exploration, uncertainty minimization.

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“…They used superellipsoids which are nonunique and cause uncertainties in the estimated model parameters, especially when representing noisy and partially-viewed data [17]. Instead, we propose the parametric geons as volumetric primitives.…”

Section: Introductionmentioning

confidence: 99%

Recovering parametric geons from multiview range data

Levine

1994

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94

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This paper focuses on approximating object part shapes b y distinctive types of volumetric primitives. Shape approximation is accomplished b y fitting volumetric models called parametric geons to multiview range data of single-part objects and classifying the fitting residuals. Parametric geons are seven qualitative shape types defined b y parameterized equations which control the size and degree of tapering and bending. Model fitting is performed by minimizing an objective function which measures the similarity in both size and shape between models and objects. Multiple view daia, global shape constraints and global optimization are employed to obtain unique models and to compensate for noise and minor variations in object shape. This approach has been studied in experiments with both synthetic 3 0 data and actual rangefinder data of perfect and imperfect geon-like objects.

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From uncertainty to visual exploration

Cited by 102 publications

References 6 publications

A robust approach to image enhancement based on fuzzy logic

A robust approach to image enhancement based on fuzzy logic

Minimum uncertainty explorations in the self-localization of mobile robots

Recovering parametric geons from multiview range data

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