A new characterization of digital lines by least square fits☆
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Cited by (27)
Measuring linearity of curves in 2D and 3D
2016, Pattern RecognitionCitation Excerpt :Shape descriptors have been employed in many computer vision and image processing tasks (e.g. image retrieval, object classification, object recognition, object identification, etc). Different mathematical tools have been used to define the shape descriptors: algebraic invariants [14], Fourier analysis [6], morphological operations [26], integral transformations [23], statistical methods [17], fractal techniques [15], logic [27], combinatorial methods [1], multiscale approaches [9], integral invariants [16], multi-scale integral geometry [3,4,18], etc. Generally speaking, shape descriptors can be classified into two groups: area based descriptors and boundary based ones.
ADR shape descriptor - Distance between shape centroids versus shape diameter
2012, Computer Vision and Image UnderstandingCitation Excerpt :For this reason, different methods are developed to measure shape properties (e.g., convexity, circularity, rectangularity, and so forth). In order to match a spectrum of specific demands for efficient object recognition, identification or classification systems, different techniques have been used to describe the shape: algebraic invariants [6], integral invariants [14], Fourier analysis [26], statistics [16], wavelets [13], fractals [7], curvature [8,12], integral transformations [18], computational geometry [29], and so forth. Also note that requests vary when shape descriptors were created.
Measuring linearity of open planar curve segments
2011, Image and Vision ComputingThe distance between shape centroids is less than a quarter of the shape perimeter
2011, Pattern RecognitionCitation Excerpt :Shape descriptors [19] are a useful tool in many computer vision and image processing tasks (e.g., image retrieval, object classification, object recognition, etc.). Different mathematical tools have been used to define the shape descriptors: algebraic invariants [7], Fourier analysis [9], morphological operations [4], geometric properties [23,24], integral transformations [18], computational geometry techniques [28], statistics [11,16] fractals [8], tensor scale [1], etc. Generally speaking, shape descriptors can be classified into two large groups: area-based descriptors and boundary-based ones.
On discrete triangles characterization
2003, Computer Vision and Image UnderstandingA representation of digital hyperbolas y = 1/x α + β
1996, Pattern Recognition Letters
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This research is supported by NATO Collaborative Research Grant CRG 900840 and NSERC operating grant OGPIN007.