Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Yu, Changzhi; Ji, Fang; Jing, Xingjiu; Liu, Mi

doi:10.1155/2019/1959671

Cited by 7 publications

(15 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) By introducing the dynamic granularity matrix space model, the optimal initial population of each ROI can be obtained as the input of the quantum-inspired group leader optimization(QGLO) algorithm [37,41] to calculate the segmentation thresholds of each ROI. The sub -pixel edge points of each ellipse ROI are calculated by the Canny-Zernike operator.…”

Section: A Flowchart Of the Proposed Hrmce Methodsmentioning

confidence: 99%

“…However, not all of the granularity layers are meaningfu l fo r image segmentation. Information entropy [37] is used to measure the effective granularity layer fro m the hierarch ical structure, which can be defined as,…”

Section: Adaptive Sub-pixel Edge Segmentation Methods For Multi-ellmentioning

confidence: 99%

“…In order to solve the adaptive segmentation thresholds for each ROI, the above optimal clustering results are input into the quantum-inspired group leader optimization(QGLO) algorithm [37,41]…”

Section: Adaptive Sub-pixel Edge Segmentation Methods For Multi-ellmentioning

confidence: 99%

“…In this section, we introduce the theory of fuzzy quotient space [35][36][37][38][39], and propose the dynamic granularity matrix space model. Considering the consistence of granularity division and image segmentation theory, a dynamic granularity matrix space model is proposed, and the problem of image segmentation can be reconstructed based on the DGMS model.…”

Section: B Dynamic Granularity Matrix Space Modelmentioning

confidence: 99%

“…According to Theorem 8, through the inclusion relation of quotient sets, the cluster of quotient sets will generate an ordered chain, that is, a hierarchical structure [35][36][37][38]. According to Theorem 8-9, a fu zzy equivalence relation corresponds to a hierarchical structure, also corresponds to a distance function.…”

Section: Ab  Must Have Solution On (X F T)mentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Xue

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Ellipse center extract ion is the important basis of camera calib ration with circle arrays calibrat ion board, wh ich direct ly affects the measurement accuracy of mach ine vision system. A iming at the problem of high noise sensitivity of the most ellipse center extract ion methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of mult i-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jump ing and transformat ion of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center ext raction method are proposed based on the dynamic granularity matrix space model. Experimental results sh ow that the proposed method has higher accuracy, robustness and lower camera calibration error. INDEX TERMS multi-ellipse center, dynamic granularity matrix space, sub-pixel edge segmentation, hierarchical structure, camera calibration.

show abstract

Section: A Flowchart Of the Proposed Hrmce Methodsmentioning

confidence: 99%

Section: Adaptive Sub-pixel Edge Segmentation Methods For Multi-ellmentioning

confidence: 99%

Section: Adaptive Sub-pixel Edge Segmentation Methods For Multi-ellmentioning

confidence: 99%

Section: B Dynamic Granularity Matrix Space Modelmentioning

confidence: 99%

Section: Ab  Must Have Solution On (X F T)mentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Xue

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

Three-dimensional shape measurement for the steep surface using DMD camera

Zhao

Yang

2022

Sci Rep

View full text Add to dashboard Cite

Three-Dimensional shape measurement has been confronted with the ambiguity on the steep surface. To address the problem, compressed sensing theory is employed to reconstruct phase shifting images in DMD camera. Specially, every CCD pixel in the region of interest is aligned to N × N DMD mirrors to construct DMD camera. The one-dimensional measurement matrices are chosen to collect the measured values on CCD pixel according to the directional judgement of original gradient image. Due to the enhancement of the spatial sampling frequency and the noise robustness, the reconstructed sinusoidal stripe images are utilized to obtain the three-dimensional model of the steep surface accurately. We measure the planes with various slopes to discuss the measurement capability. The comparative experiments show that our proposed method can correct the deformed phase and repair the defect on the steep surface.

show abstract

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Tariq

Hamzah

et al. 2021

IEEE Access

View full text Add to dashboard Cite

A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.INDEX TERMS Digital image processing, edge detection algorithm, image segmentation, assessment, validation, quality measures, reviews.

show abstract

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Cited by 7 publications

References 26 publications

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Three-dimensional shape measurement for the steep surface using DMD camera

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Contact Info

Product

Resources

About