Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering

Abstract

3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts. Our approach was evaluated using different evaluation metrics. The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature.

A novel approach for partial shape matching and similarity based on data envelopment analysis

“…1 shows the obtained results. As can be seen from the obtained results, the curve obtained using learning-based approach (LB) [30] gives once again a very good classification performance by being classified at the top of the automatic segmentation algorithms, followed by the Randomized Cut algorithm (RC) [31] which slightly superforms the approach based on spectral clustering (SC) [34]. In addition, the learning-based ap-proach(LB) [30] offers a gain in operating time compared to the Randomized Cuts algorithm (RC) [31], which is costly in terms of response time of the different random segmentations it based.…”

“…Table 1 present for each segmentation method its average of the dissimilarity scores obtained for the entire database by using the cited evaluation methods. As can be deduced from this quantitative comparison, the dissimilarity scores of the learning-based algorithm (LB) [30] obtained by almost all the used evaluation methods are better than all the other methods, which highlight its performance. After evaluating a set of segmentation methods by applying some of the most recent and efficient evaluation methods, we will continue by studying the behavior and the impact of the segmentation algorithms in the context of content-based research.…”

“…From all these results, we can easily deduce that the learning based method (LB) [30] is the better choice to be used in the partial indexation partial from all the tested segmentation methods. The Fig.…”

“…using learning based method (LB) [30] The Fig. 3 represents the global process of the proposed approach.…”

Surface recognition of machine parts based on the results of optical scanning

“…To partially automate engine assembly processes, it is necessary to recognize both the individual parts and the surfaces of the parts along which the assembly will take place. Face recognition is possible using computer vision approaches [1,2,3]. The aim of this work is to create a model based on the use of neural networks, designed to recognize the surfaces of engineering parts after their measurement using an optical or laser scanner.…”