An adaptive reduction procedure for the piecewise linear approximation of digitized curves

Fahn, Chin-Shyurng; Wang, J.-F.; Lee, J.-Y.

doi:10.1109/34.35499

Cited by 27 publications

(5 citation statements)

References 11 publications

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“…Other approaches use critical points [5] and landmarks [10], such as those points with the highest curvature to represent curves. The use of the key points for curve matching considerably saves computation time.…”

Section: Introductionmentioning

confidence: 99%

<title>Robust contour model for matching synthetic aperture radar (SAR) images with maps</title>

Zhao

Chen

2003

SPIE Proceedings

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This paper deals with the registration of images which are subject to translation, rotation, scaling and other geometric transformations. The problem is tackled with a contour-based registration technique. We present a novel approach for finding a representation of planar curves and matching such two representations in this paper. In our framework, the matching of curves is proceeded by 1) using connected equi-length line segments (CELLS) to represent curves, (2) attaching an identification vector to each line segment, which reflects the distribution of the rest of line segments with respect to the current one using orientation difference between cells. A new matrix called Orientation Difference Matrix (ODM) has been constructed from the identification vectors. This approach uniquely specifies a curve and the representation for matching is invariant under rotation, scaling and translation of the curve. A practical use of the proposed approach is demonstrated by registering a SAR image of a certain area to a map.

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Section: Introductionmentioning

confidence: 99%

<title>Robust contour model for matching synthetic aperture radar (SAR) images with maps</title>

Zhao

Chen

2003

SPIE Proceedings

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“…In edge-based image vectorization, after sequential tracing of an object boundary, a number of algorithms have been developed to select sparse anchor points in the traced boundary pixels to generate the final polygon, including top-down [Douglas and Peucker 2011;Ballard 1981], bottom-up [Fahn et al 1989;Latecki and Lakmper 1999], and global optimization methods [Glover and Laguna 1997;Sun and Huang 2000].…”

Section: Related Workmentioning

confidence: 99%

Online Structure Analysis for Real-Time Indoor Scene Reconstruction

Zhang

Tong

et al. 2015

ACM Trans. Graph.

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Figure 1: A lab scene (100m 2 ) reconstructed on-the-fly. The complete scan finished within 70 minutes. Top left: the final reconstructed model by our system, which contains planar regions (polygons) and extracted meshes for separate objects, created and refined progressively in real-time by our online analysis procedure during the scan. Top right: the result of the plane/object labeling procedure, which provides the segmentation of planes and objects for the online analysis. The colors distinguish planar regions and objects by their labels in the volumetric data structure. Bottom: close-up views of the reconstructed scene. AbstractWe propose a real-time approach for indoor scene reconstruction. It is capable of producing a ready-to-use 3D geometric model even while the user is still scanning the environment with a consumer depth camera. Our approach features explicit representations of planar regions and non-planar objects extracted from the noisy feed of the depth camera, via an online structure analysis on the dynamic, incomplete data. The structural information is incorporated into the volumetric representation of the scene, resulting in a seamless integration with KinectFusion's global data structure and an efficient implementation of the whole reconstruction process. Moreover, heuristics based on rectilinear shapes in typical indoor scenes effectively eliminate camera tracking drift and further improve reconstruction accuracy. The instantaneous feedback enabled by our on-the-fly structure analysis, including repeated object recognition, allows the user to selectively scan the scene and produce high fidelity large-scale models efficiently. We demonstrate the capability of our system with real-life examples. *

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“…The information conveyed by every ordered contour pixel set y m can be compactly represented after performing a piecewise linear approximation of its associated plane curve [11], [12], [13], [14]. The goal of the approximation is to find a polygon whose vertices form a subset…”

Section: Object Segmentation and Modelingmentioning

confidence: 99%

Unsupervised clustering in Hough space for identification of partially occluded objects

Yáñez-Suárez

Azimi-Sadjadi

1999

IEEE Trans. Pattern Anal. Machine Intell.

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AbstractÐAn automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided.

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An adaptive reduction procedure for the piecewise linear approximation of digitized curves

Cited by 27 publications

References 11 publications

<title>Robust contour model for matching synthetic aperture radar (SAR) images with maps</title>

<title>Robust contour model for matching synthetic aperture radar (SAR) images with maps</title>

Online Structure Analysis for Real-Time Indoor Scene Reconstruction

Unsupervised clustering in Hough space for identification of partially occluded objects

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