A simple method for fitting of bounding rectangle to closed regions

Chaudhuri, Debasis; Samal, Ashok

doi:10.1016/j.patcog.2006.08.003

Cited by 134 publications

(69 citation statements)

References 16 publications

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“…Among many other 2-D parametric polygons/curves fitting of rectangle/ square, circle, and ellipse plays important roles in real life applications. In general, approaches for locating parametric polygons/curves can be divided into two steps 7,8,77 . The first step is to detect the boundary of the object.…”

Section: • Best Fitting Methods Of 2-d Parametric Polygons/curvesmentioning

confidence: 99%

“…The first step is to detect the boundary of the object. The second step is to estimate its parameters based on the boundary points 2,7,8,77 . The parameters estimation procedures are discussed briefly.…”

Section: • Best Fitting Methods Of 2-d Parametric Polygons/curvesmentioning

confidence: 99%

“…Although the term similarity is often used, dissimilarity corresponds to the notion of distance: small distance means small dissimilarity, and large similarity. Shape similarity measures are used in building extraction and oil tank detection from remote sensing images 2,6,7,8 . Various shape features are often evaluated by how accurately they allow one to retrieve similar shapes from a designated database.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global Contour and Region Based Shape Analysis and Similarity Measures

Chaudhuri

2013

DSJ

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Section: • Best Fitting Methods Of 2-d Parametric Polygons/curvesmentioning

confidence: 99%

Section: • Best Fitting Methods Of 2-d Parametric Polygons/curvesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Contour and Region Based Shape Analysis and Similarity Measures

Chaudhuri

2013

DSJ

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“…It can be divided into two dimensions and three dimensions of MBB as shown in (RMBR) applied to the building boundaries generated from LiDAR data which was able to make the LiDAR boundaries partition into a set of rectangles for future processing [99]. Example for four steps of bounding fitting rectangle [98] (1) To begin with, compute boundary (edge points) of the object and the centroid of the object through these edge points.…”

Section: C) a Minimum Bounding Boxmentioning

confidence: 99%

Automated process planning and cost estimation under material quality uncertainty

Huang

Chaiprapat

Waiyagan

2015

Int J Adv Manuf Technol

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“…Since rectangular models are chosen as the basic model, the MBR algorithm is applied to regularise the initial LiDAR-derived boundaries and decompose them into rectangles. MBR is the rectangle with minimum area among the rectangles of arbitrary orientation which contain all the vertices of a LiDAR boundary (Freeman and Shapira, 1975;Chaudhuri and Samal, 2007). MBR generation of a simple rectangular building is described in Habib et al (2011).…”

Section: Initial Model Parameter Generation: Recursive Mbrmentioning

confidence: 99%

Automatic 3d Building Model Generation From Lidar and Image Data Using Sequential Minimum Bounding Rectangle

Kwak

Al-Durgham

Habib

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Digital Building Model is an important component in many applications such as city modelling, natural disaster planning, and aftermath evaluation. The importance of accurate and up-to-date building models has been discussed by many researchers, and many different approaches for efficient building model generation have been proposed. They can be categorised according to the data source used, the data processing strategy, and the amount of human interaction. In terms of data source, due to the limitations of using single source data, integration of multi-senor data is desired since it preserves the advantages of the involved datasets. Aerial imagery and LiDAR data are among the commonly combined sources to obtain 3D building models with good vertical accuracy from laser scanning and good planimetric accuracy from aerial images. The most used data processing strategies are data-driven and model-driven ones. Theoretically one can model any shape of buildings using data-driven approaches but practically it leaves the question of how to impose constraints and set the rules during the generation process. Due to the complexity of the implementation of the data-driven approaches, model-based approaches draw the attention of the researchers. However, the major drawback of modelbased approaches is that the establishment of representative models involves a manual process that requires human intervention. Therefore, the objective of this research work is to automatically generate building models using the Minimum Bounding Rectangle algorithm and sequentially adjusting them to combine the advantages of image and LiDAR datasets.

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A simple method for fitting of bounding rectangle to closed regions

Cited by 134 publications

References 16 publications

Global Contour and Region Based Shape Analysis and Similarity Measures

Global Contour and Region Based Shape Analysis and Similarity Measures

Automated process planning and cost estimation under material quality uncertainty

Automatic 3d Building Model Generation From Lidar and Image Data Using Sequential Minimum Bounding Rectangle

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