Iterative Region Merging and Object Retrieval Method Using Mean Shift Segmentation and Flood Fill Algorithm

Bhargava, Neeraj; Trivedi, Piyush; Toshniwal, Akanksha; Swarnkar, Himanshu

doi:10.1109/icacc.2013.100

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…After image binarization, many black areas that contain noise must be deleted. As mentioned, the OCT algorithm, which used the region growing method, was applied [27]. The spanning tree method was used to record the cover of adjacent connections.…”

Section: Establishment Of the Optimal Cover Tree (Oct Algorithm)mentioning

confidence: 99%

An unsupervised image segmentation algorithm for coronary angiography

Yin

2022

BioData Mining

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Computer visual systems can rapidly obtain a large amount of data and automatically process them with ease. These characteristics constitute advantages for the application of such systems in the automatic analysis of medical images, as well as in processing technology. The precision of image segmentation, which plays a critical role in computer visual systems, directly affects the quality of processing results. Coronary angiographs feature various background colors, complex patterns, and blurry edges. The image areas containing blood vessels cannot be precisely segmented through regular methods. Therefore, this study proposed an unsupervised learning algorithm that uses regional parameter expansion (RPE). This method was derived from the flood fill algorithm, which can effectively segment image areas containing blood vessels despite a complex background or uneven light and shadow. An optimal cover tree (OCT) algorithm was proposed for the establishment of coronary arteries and the estimation of vessel diameter. Through the region growing method, spanning trees were used to record the cover length of adjacent connections, thereby establishing vessel paths, and the length can be used to track changes in vessel diameter.

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Section: Establishment Of the Optimal Cover Tree (Oct Algorithm)mentioning

confidence: 99%

An unsupervised image segmentation algorithm for coronary angiography

Yin

2022

BioData Mining

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“…In the context of image processing, FFAs have seen ongoing widespread use in commercial products as a time-efficient method for filling a bounded region with a given colour [13]. The principle of the bucket-fill programme has been inverted to aid segmentation algorithms in 2D and 3D contexts from a user-inputted mask [14][15][16]. In recent years FFAs have aided machine-learning programmes in object recognition through automatic mask generation [17].…”

Section: First Iterationmentioning

confidence: 99%

A Novel Complete-Surface-Finding Algorithm for Online Surface Scanning with Limited View Sensors

Poole

Sutcliffe

Pierce

et al. 2021

Sensors

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Robotised Non-Destructive Testing (NDT) has revolutionised the field, increasing the speed of repetitive scanning procedures and ability to reach hazardous environments. Application of robot-assisted NDT within specific industries such as remanufacturing and Aersopace, in which parts are regularly moulded and susceptible to non-critical deformation has however presented drawbacks. In these cases, digital models for robotic path planning are not always available or accurate. Cutting edge methods to counter the limited flexibility of robots require an initial pre-scan using camera-based systems in order to build a CAD model for path planning. This paper has sought to create a novel algorithm that enables robot-assisted ultrasonic testing of unknown surfaces within a single pass. Key to the impact of this article is the enabled autonomous profiling with sensors whose aperture is several orders of magnitude smaller than the target surface, for surfaces of any scale. Potential applications of the algorithm presented include autonomous drone and crawler inspections of large, complex, unknown environments in addition to situations where traditional metrological profiling equipment is not practical, such as in confined spaces. In simulation, the proposed algorithm has completely mapped significantly curved and complex shapes by utilising only local information, outputting a traditional raster pattern when curvature is present only in a single direction. In practical demonstrations, both curved and non-simple surfaces were fully mapped with no required operator intervention. The core limitations of the algorithm in practical cases is the effective range of the applied sensor, and as a stand-alone method it lacks the required knowledge of the environment to prevent collisions. However, since the approach has met success in fully scanning non-obstructive but still significantly complex surfaces, the objectives of this paper have been met. Future work will focus on low-accuracy environmental sensing capabilities to tackle the challenges faced. The method has been designed to allow single-pass scans for Conformable Wedge Probe UT scanning, but may be applied to any surface scans in the case the sensor aperture is significantly smaller than the part.

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“…The morphological flood-fill operation was further applied to fill holes within each tree crown. The flood-fill process was based on dilation, complementation, and intersection [35,36].…”

Section: Hole Fillingmentioning

confidence: 99%

Individual Tree Crown Delineation from UAS Imagery Based on Region Growing and Growth Space Considerations

Grybas

Congalton

2020

Remote Sensing

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The development of unmanned aerial systems (UAS) equipped with various sensors (e.g., Lidar, multispectral sensors, and/or cameras) has provided the capability to “see” the individual trees in a forest. Individual tree crowns (ITCs) are the building blocks of precision forestry, because this knowledge allows users to analyze, model and manage the forest at the individual tree level by combing multiple data sources (e.g., remote sensing data and field surveys). Trees in the forest compete with other vegetation, especially neighboring trees, for limited resources to grow into the available horizontal and vertical space. Based on this assumption, this research developed a new region growing method that began with treetops as the initial seeds, and then segmented the ITCs, considering its growth space between the tree and its neighbors. The growth space was allocated by Euclidian distance and adjusted based on the crown size. Results showed that the over-segmentation accuracy (Oa), under-segmentation (Ua), and quality rate (QR) reached 0.784, 0.766, and 0.382, respectively, if the treetops were detected from a variable window filter based on an allometric equation for crown width. The Oa, Ua, and QR increased to 0.811, 0.853, and 0.296, respectively, when the treetops were manually adjusted. Treetop detection accuracy has a great impact on ITCs delineation accuracy. The uncertainties and limitations within this research including the interpretation error and accuracy measures were also analyzed and discussed, and a unified framework assessing the segmentation accuracy was highly suggested.

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Iterative Region Merging and Object Retrieval Method Using Mean Shift Segmentation and Flood Fill Algorithm

Cited by 10 publications

References 14 publications

An unsupervised image segmentation algorithm for coronary angiography

An unsupervised image segmentation algorithm for coronary angiography

A Novel Complete-Surface-Finding Algorithm for Online Surface Scanning with Limited View Sensors

Individual Tree Crown Delineation from UAS Imagery Based on Region Growing and Growth Space Considerations

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