Image classification is a process that depends on the descriptor used to represent an object. To create such descriptors we use object models with rich information of the distribution of points. The object model stage is improved with an optimization process by spreading the point that conforms the mesh. In this paper, particle swarm optimization (PSO) is used to improve the model generation, while for the classification problem a support vector machine (SVM) is used. In order to measure the performance of the proposed method a group of objects from a public RGB-D object data set has been used. Experimental results show that our approach improves the distribution on the feature space of the model, which allows to reduce the number of support vectors obtained in the training process.

Abstract. In this paper we introduce a representation for object verification and a system for object recognition based on local features, invariant moments, silhouette creation and a 'net' reduction for depth information. The results are then compared with some of the most recent approaches for object detection such as local features and orientation histograms. Additionally, we used depth information to create descriptors that can be used for 3D verification of detected objects. Moments are computed from a 3D set of points which are arranged to create a descriptive object model. This information showed to be of matter in the decision whether the object is present within the analyzed image segment, or not.