An efficient algorithm to clip a 2D-polygon against a rectangular clip window

Dimri, Sushil Chandra; Tiwari, Umesh; Ram, Mangey

doi:10.1007/s11766-022-4556-0

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…Determining whether a target is inside or outside a specific area can be framed as a PIP problem. Many works in the literature have discussed related issues in the past [16][17][18][19][20][21]. In [16], the study presented a detailed discussion of the PIP problem for arbitrary polygons.…”

Section: Related Workmentioning

confidence: 99%

“…In [18], the authors outlined a fast and efficient method for determining whether a coordinate point lies within a closed region or polygon defined by any number of coordinate points. In [19], the study proposed an efficient polygon clipping algorithm using a parametric representation of polygon edges and point clipping to find required intersection points with clip window boundaries. In [20], the study presented an extension of a winding number and PIP algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Study [21] provided a provably correct implementation of a PIP method based on the computation of the winding number. We can follow the methods in [16][17][18][19][20][21] to design different PIP_EPs. These PIP_EPs share a common characteristic in that they can only determine whether a point is inside a specified polygon one at a time.…”

Section: Related Workmentioning

confidence: 99%

“…We simply call these procedures the PIP_EPs (PIP evaluation procedures). Because the PIP_EPs continue to have promising applications in many fields, related technologies continue to be developed [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient GNSS Coordinate Classification Strategy with an Adaptive KNN Algorithm for Epidemic Management

Chen,

Kuo

2024

Mathematics

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In times of widespread epidemics, numerous individuals are at risk of contracting viruses, such as COVID-19, monkeypox, and pneumonia, leading to a ripple effect of impacts on others. Consequently, the Centers for Disease Control (CDC) typically devises strategies to manage the situation by monitoring and tracing the infected individuals and their areas. For convenience, “targets” and “areas” represent the following individuals and areas. A global navigation satellite system (GNSS) can assist in evaluating the located areas of the targets with pointing-in-polygon (PIP) related technology. When there are many targets and areas, relying solely on PIP technology for classification from targets to areas could be more efficient. The classification technique of k-nearest neighbors (KNN) classification is widely utilized across various domains, offering reliable classification accuracy. However, KNN classification requires a certain quantity of targets with areas (training dataset) for execution, and the size of the training dataset and classification time often exhibit an exponential relationship. This study presents a strategy for applying KNN technology to classify targets into areas. Additionally, within the strategy, we propose an adaptive KNN algorithm to enhance the efficiency of the classification procedure.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient GNSS Coordinate Classification Strategy with an Adaptive KNN Algorithm for Epidemic Management

Chen,

Kuo

2024

Mathematics

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“…Some additional modifications of algorithms were published in Brackenbury (1984), Chao et al (2009), Cheng and Yen (1989), Dimri (2015), Dimri et al (2022), Elliriki et al (2019), Hattab and Yusof (2014), Iraji et al (2011), Jiang and Han (2013), Jianrong (2006), Kumar and Awasthi (2011), Kuzmin (1995), Li et al (2014), Li and Lei (2012), Meriaux (1984), Molla et al (2003), Nisha (2017bNisha ( , 2017a, Sobkow et al (1987), Sharma and Manohar (1993), Wang et al (1998aWang et al ( , 1998bWang et al ( , 2012Wang et al ( , 2001, Yang (1988), Pandey and Jain (2013), Bhuiyan (2009). The hardware FPGA implementation was proposed in Dawod (2011).…”

Section: S-l-clip Algorithmmentioning

confidence: 99%

A Brief Survey of Clipping and Intersection Algorithms with a List of References (including Triangle-Triangle Intersections)

Skala¹

2023

Informatica

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This contribution presents a brief survey of clipping and intersection algorithms in ${E^{2}}$ and ${E^{3}}$ with a nearly complete list of relevant references. Some algorithms use the projective extension of the Euclidean space and vector-vector operations, which support GPU and SSE use. This survey is intended to help researchers, students, and practitioners dealing with intersection and clipping algorithms.

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An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management

2023

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Many highly contagious infectious diseases, such as COVID-19, monkeypox, chickenpox, influenza, etc., have seriously affected or currently are seriously affecting human health, economic activities, education, sports, and leisure. Many people will be infected or quarantined when an epidemic spreads in specific areas. These people whose activities must be restricted due to the epidemic are represented by targets in the article. Managing targets by using targeted areas is an effective option for slowing the spread. The Centers for Disease Control (CDC) usually determine management strategies by tracking targets in specific areas. A global navigation satellite system (GNSS) that can provide autonomous geospatial positioning of targets by using tiny electronic receivers can assist in recognition. The recognition of targets within a targeted area is a point-in-polygon (PtInPy) problem in computational geometry. Most previous methods used the method of identifying one target at a time, which made them unable to effectively deal with many targets. An earlier method was able to simultaneously recognize several targets but had the problem of the repeated recognition of the same targets. Therefore, we propose a GNSS coordinate recognition algorithm. This algorithm can efficiently recognize a large number of targets within a targeted area, which can provide assistance in epidemic management.

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An efficient algorithm to clip a 2D-polygon against a rectangular clip window

Cited by 3 publications

References 19 publications

An Efficient GNSS Coordinate Classification Strategy with an Adaptive KNN Algorithm for Epidemic Management

An Efficient GNSS Coordinate Classification Strategy with an Adaptive KNN Algorithm for Epidemic Management

A Brief Survey of Clipping and Intersection Algorithms with a List of References (including Triangle-Triangle Intersections)

An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management

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