Evaluation of the Uncertainty of Edge Detector Algorithms

Anchini, R.; Paolillo, Alfredo

doi:10.1109/amyem.2006.1650753

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…On the pixel level, this uncertainty can be used to assess the information quality of a single pixel due to sensor imperfections or temperature dependence [31,40]. Furthermore, the localization uncertainty of geometric features such as corners, centroids, edges and lines in images has been assessed in [3,15,16,36]. An approach to evaluate the quality of image registration algorithms was presented in [27].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Stegmaier

Mikut

2017

PLoS ONE

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Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout.

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Section: Introductionmentioning

confidence: 99%

“…Feature histograms (top) and fuzzy set membership functions (bottom) for volume (θ θ θ vol = (449, 617, 1405, 2016) ), width (θ θ θ w =(13,15,24,31) ), height (θ θ θ h =(13,15,24,34) ) and depth (θ θ θ w =(3,5,8,11) ).…”

mentioning

confidence: 99%

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Stegmaier

Mikut

2017

PLoS ONE

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“…However, a single feedback insertion strategy may not yield the precise location of x and y and orientation of θ of the pin–hole required for assembly due to the edge uncertainty 5 caused by uneven illumination variation. 21 Hence, the uncertainty on the edge localization directly influences the centroid position of the pin and hole. Examples of sets from three actual experimental results are shown in Figure 13.…”

Section: Visual Servoing Feedback With Uncertainty Compensationmentioning

confidence: 99%

Machine vision based in-process light-emitting diode chip mounting system

Chen

2018

Measurement and Control

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Background: In this study, a machine vision–based method was developed for automated in-process light-emitting diode chip mounting lines with position uncertainty. In order to place the tiny light-emitting diode chips on the pattern of a printed circuit board, a highly accurate mounting process is achieved with online feedback of the visual assistance. Methods: The system consists of a charge-coupled device camera, a six-axis robot arm, and a delta robot. The lighting system is a critical point for the in-process machine vision problem. Hence, designing the optimal lighting solution is one of the most difficult parts of a machine vision system, and several lighting techniques and experiments are examined in this study. In order to commence the mounting process, the light-emitting diode chip targets inside the camera field were identified and used to guide the delta robot to the grabbing zone based on the calibrated homography transformation. Efforts have been focused on the field of machine vision–based feature extraction of the chip pins and the holes on the printed circuit board. The correspondence of each other is determined by the position of the chip pins and the printed circuit board circuit pattern. The image acquisition is achieved directly online in real time. The image analysis algorithm must be sufficiently fast to follow the production rate. In order to compensate for the uncertainty of the light-emitting diode chip mounting process, a visual feedback strategy in conjunction with an uncertainty compensation strategy is employed. Results: Finally, the light-emitting diode chip was automatically grabbed and accurately placed at the desired positions. Conclusion: On-line and off-line experiments were conducted to investigate the performance of the vision system with respect to detecting and mounting light-emitting diode chips.

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“…The measurement uncertainty in the vision-based measurement has been discussed in the literature (19) - (21) . Major contributors to the vision-based position measurement presented in this paper are the following factors.…”

Section: Remarks On Major Contributors For Measurement Uncertaintiesmentioning

confidence: 99%

Vision-Based Measurement of Two-Dimensional Positioning Errors of Machine Tools

Ibaraki

Tanizawa

2011

JAMDSM

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This paper presents vision-based measurement of two-dimensional positioning errors of machine tools. A pre-calibrated grid plate is used as an artefact. The position of a grid point is measured in an image captured by a charge coupled device (CCD) camera attached to a spindle. Unlike conventional two-dimensional digital scales, the vision-based measurement can target the artefact in any orientation, and thus can be also applied to error calibration of a rotary axis. A longer working distance between the lens and the target is also its potential advantage for safer measurement. The paper demonstrates experimental applications of the vision-based measurement to the measurement of 1) static and 2) dynamic error motions of 1) a linear axis and 2) a rotary axis of a machine tool.

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Evaluation of the Uncertainty of Edge Detector Algorithms

Cited by 8 publications

References 14 publications

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Machine vision based in-process light-emitting diode chip mounting system

Vision-Based Measurement of Two-Dimensional Positioning Errors of Machine Tools

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