Mohamed Chahhou

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This paper describes an approach for 3D triangular meshes segmentation. The method that we adopt for the 3D mesh segmentation is based on the unbiased hierarchical queue which has been used for 2D images. Our method uses the hierarchical transformation on a connected faces structure. The approach that we propose is based on the principal curvature to(More)
In this paper, we propose a new approach to get the optimal segmentation of a 3D mesh as a human can perceive using the minima rule and spectral clustering. This method is fully unsupervised and provides a hierarchical segmentation via recursive cuts. We introduce a new concept of the adjacency matrix based on cognitive studies. We also introduce the use of(More)
3D retrieval has become an important field for applications that require 3D databases. Several descriptors have been defined in the past, most of them are based on the global geometric signature of the 3D objects and only a few of them allow a partial matching using segments of a 3D object as queries. In this paper, we propose to improve the results of(More)
The watershed transformation is a useful tool for the 3D segmentation. However, over segmentation have become the key problems for the conventional algorithm. This paper presents two new methods for solving these problems. The first method is to establish a generic-adjacencies graph of regions resulting from the application of watershed segmentation and to(More)
Three-dimensional models are more and more used in applications in which the necessity to visualize realistic objects is felt (CAD/CAO, medical simulations, games, virtual reality etc.). Consequently, the management of large sizes of 3D data collections becomes an important field. The indexation of such data allows a designer for instance to easily find(More)
There are many algorithms and systems for mining data that are being constantly developed and improved by research communities and industrial organizations worldwide, but choosing the most adequate and with the most optimal results for the problem at hand remains a main concern and a critical decision to make. This paper inspects the most used algorithms(More)
Decision Tree is one of the most popular supervised Machine Learning algorithms; it is also the easiest to understand. But finding an optimal decision tree for a given data is a harder task and the use of multiple performance metrics adds some complexity to the problem of selecting the most appropriate DT.
Predicting stock prices is an important task of financial time series forecasting, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in recent times to predict the stock price, including regression algorithms which can be useful tools to provide good accuracy of financial(More)
3D objects learning is a challenging problem in computer vision and digital multimedia due to the wide development of 3D objects scanning technology. Nevertheless, using machine learning for solving such problems is a potential and effective tool. In this paper, we propose a novel approach for 3D objects labeling, it relies on a multi-class boosting(More)
Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using machine learning techniques in this area. A large number of successful applications have shown that regression algorithms can be very useful tools for time-series modelling and forecasting. In this paper we run a comparative(More)