Detection of line junctions and line terminations using curvilinear features

“…To detect these edges, a thinning algorithm is applied to the ROI (Lam, Lee, & Suen, 1992 • line end points • line junction points • high curvature points Line end points and line junction points are extracted by convolving the trace image with specific kernels taking into account 8-neighbor-connectivity. The curvature of the trace line along each point p is obtained by computing C p within a 2n + 1 band along the line (Deschênes & Ziou, 2000), with…”

Section: Geometrical Features Extractionmentioning

confidence: 99%

Efficient three‐dimensional scene modeling and mosaicing

Nicosevici

Gracias

Negahdaripour

et al. 2009

Journal of Field Robotics

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Scene modeling has a key role in applications ranging from visual mapping to augmented reality. This paper presents an end-to-end solution for creating accurate three-dimensional (3D) textured models using monocular video sequences. The methods are developed within the framework of sequential structure from motion, in which a 3D model of the environment is maintained and updated as new visual information becomes available. The proposed approach contains contributions at different levels. The camera pose is recovered by directly associating the 3D scene model with local image observations, using a dual-registration approach. Compared to the standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures, while allowing 3D reconstructions for any type of scene. Motivated by the need to map large areas, a novel 3D vertex selection mechanism is proposed, which takes into account the geometry of the scene. Vertices are selected not only to have high reconstruction accuracy but also to be representative of the local shape of the scene. This results in a reduction in the complexity of the final 3D model, with minimal loss of precision. is generated. We present a method for blending image textures using 3D geometric information and photometric differences between registered textures. The method allows high-quality mosaicing over 3D surfaces by reducing the effects of the distortions induced by camera viewpoint and illumination changes. The results are presented for four scene modeling scenarios, including a comparison with ground truth under a realistic scenario and a challenging underwater data set. Although developed primarily for underwater mapping applications, the methods are general and applicable to other domains, such as aerial and land-based mapping. C 2009 Wiley Periodicals, Inc.

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“…Line end points and line junction are obtained by convolving the binary image with specific kernels and extracting points with local maxima. The curvature of the trace line along each point p is obtained by computing C p within a 2n + 1 1D window along the line [15], with:…”

Section: Geometrical Features Extractionmentioning

confidence: 99%

Identification of Suitable Interest Points Using Geometric and Photometric Cues in Motion Video for Efficient 3-D Environmental Modeling

Nicosevici¹,

García²,

Negahdaripour³

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-Many applications in mobile and underwater robotics employ 3D vision techniques for navigation and mapping. These techniques usually involve the extraction and 3D reconstruction of scene interest points. Nevertheless, in large environments the huge volume of acquired information could pose serious problems to real-time data processing. Moreover, In order to minimize the drift, these techniques use data association to close trajectory loops, decreasing the uncertainties in estimating the position of the robot and increasing the precision of the resulting 3D models. When faced to large amounts of features, the efficiency of data association decreases drastically, affecting the global performance. This paper proposes a framework that highly reduces the number of extracted features with minimum impact on the precision of the 3D scene model. This is achieved by minimizing the representation redundancy by analyzing the geometry of the environment and extracting only those features that are both photometrically and geometrically significant.

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Detection of line junctions and line terminations using curvilinear features

Cited by 34 publications

References 12 publications

Efficient 3D Scene Modeling and Mosaicing

Efficient 3D Scene Modeling and Mosaicing

Efficient three‐dimensional scene modeling and mosaicing

Identification of Suitable Interest Points Using Geometric and Photometric Cues in Motion Video for Efficient 3-D Environmental Modeling

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