This paper describes a novel hand-eye calibration technique based on reprojection error minimization. In contrast to traditional hand-eye calibration methods, the proposed method directly takes images of the calibration pattern and does not require to explicitly estimate the camera pose for each input image. The proposed method is implemented as a pose graph optimization problem, so that it can solve the estimation problem efficiently and robustly, and it can be easily extended for different projection models. It can deal with different camera models (e.g, X-ray cameras with a source-detector projection model) by changing the projection model. Through simulations, we validated that the proposed method shows a good estimation accuracy, and it can be applied to hand-eye calibration with a source-detector camera model. The experimental results with real robots show that the proposed method is applicable to real environments, and it improves the quality of a task which requires accurate hand-eye estimation, like 3D reconstruction.

In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test. The source code used for the approaches tested in this paper will be available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=.

This paper proposes a very fast and robust multi-people tracking algorithm suitable for mobile platforms equipped with a RGB-D sensor. Our approach features a novel depth-based sub-clustering method explicitly designed for detecting people within groups or near the background and a three-term joint likelihood for limiting drifts and ID switches. Moreover, an online learned appearance classifier is proposed, that robustly specializes on a track while using the other detections as negative examples. Tests have been performed with data acquired from a mobile robot in indoor environments and on a publicly available dataset acquired with three RGB-D sensors and results have been evaluated with the CLEAR MOT metrics. Our method reaches near state of the art performance and very high frame rates in our distributed ROS-based CPU implementation.