3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation

This paper proposes a simple and efficient method for the reconstruction and extraction of geometric parameters from 3D tubular objects. Our method constructs an image that accumulates surface normal information, then peaks within this image are located by tracking. Finally, the positions of these are optimized to lie precisely on the tubular shape centerline. This method is very versatile, and is able to process various input data types like full or partial mesh acquired from 3D laser scans, 3D height map or discrete volumetric images. The proposed algorithm is simple to implement, contains few parameters and can be computed in linear time with respect to the number of surface faces. Since the extracted tube centerline is accurate, we are able to decompose the tube into rectilinear parts and torus-like parts. This is done with a new linear time 3D torus detection algorithm, which follows the same principle of a previous work on 2D arc circle recognition. Detailed experiments show the versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing, Sep 2015, Genova, Italy. 201

Datasets for the Evaluation of Substitution-Tolerant Subgraph Isomorphism

International audienceDue to their representative power, structural de-scriptions have gained a great interest in the community working on graphics recognition. Indeed, graph based representations have successful been used for isolated symbol recognition. New challenges in this research field have focused on symbol recog-nition, symbol spotting or symbol based indexing of technical drawing. When they are based on structural descriptions, these tasks can be expressed by means of a subgraph isomorphism search. Indeed, in consists in locating the instance of a pattern graph representing a symbol in a target graph representing the whole document image. However, there is a lack of publicly available datasets allowing to evaluate the performance of subgraph iso-morphism approaches in presence of noisy data. In this paper, we present three datasets that can be used to evaluate the performance of algorithms on several tasks involving subgraph isomorphism. Two of these datasets have been synthetically generated and allow to evaluate the search of a single instance of the pattern with or without perturbed labels. The third dataset corresponds to the structural description of architectural plans and allows to evaluate the search of multiple occurrences of the pattern. These datasets are made available for download. We also propose several measures to qualify each of the tasks