Reflectance Hashing for Material Recognition

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials

The LOPES experiment - recent results, status and perspectives

The LOPES experiment at the Karlsruhe Institute of Technology has been taking radio data in the frequency range from 40 to 80 MHz in coincidence with the KASCADE-Grande air shower detector since 2003. Various experimental configurations have been employed to study aspects such as the energy scaling, geomagnetic dependence, lateral distribution, and polarization of the radio emission from cosmic rays. The high quality per-event air shower information provided by KASCADE-Grande has been the key to many of these studies and has even allowed us to perform detailed per-event comparisons with simulations of the radio emission. In this article, we give an overview of results obtained by LOPES, and present the status and perspectives of the ever-evolving experiment.Comment: Proceedings of the ARENA2010 conference, Nantes, Franc

Comparing 3D descriptors for local search of craniofacial landmarks

This paper presents a comparison of local descriptors for a set of 26 craniofacial landmarks annotated on 144 scans acquired in the context of clinical research. We focus on the accuracy of the different descriptors on a per-landmark basis when constrained to a local search. For most descriptors, we find that the curves of expected error against the search radius have a plateau that can be used to characterize their performance, both in terms of accuracy and maximum usable range for the local search. Six histograms-based descriptors were evaluated: three describing distances and three describing orientations. No descriptor dominated over the rest and the best accuracy per landmark was strongly distributed among 3 of the 6 algorithms evaluated. Ordering the descriptors by average error (over all landmarks) did not coincide with the ordering by most frequently selected, indicating that a comparison of descriptors based on their global behavior might be misleading when targeting facial landmarks

Creating 3D object descriptors using a genetic algorithm

In the technological world that we live in, the need for computer vision became almost as important as human vision. We are surrounded be all kinds of machines that need to have their own virtual eyes. The most developed cars have software that can analyze traffic signs in order to warn the driver about the eventsontheroad. Whenwesendaspacerovertootherplanetitisimportantthatitcananalyzetheground in order to avoid obstacles that would lead to its destruction. Thereisstillmuchworktobedoneinthefieldofcomputervisionwiththeviewtoimprovetheperformance and speed of recognition tasks. There are many available descriptors used for 3D point cloud recognition and some of them are explained in this thesis. The aim of this work is to design descriptors that can match correctly 3D point clouds. The idea is to use artificial intelligence, in the form of a GA to obtain optimized parameters for the descriptors. For this purpose the PCL [RC11] is used, which deals with the manipulation of 3D points data. The created descriptors are explained and experiments are done to illustrate their performance. The main conclusions are that there is still much work to be done in shape recognition. The descriptor developed in this thesis that use only color information is better than the descriptors that use only shape data. Although we have achieved descriptors withgoodperformanceinthisthesis,therecouldbeawaytoimprovethemevenmore. As the descriptor that use only color data is better than the shape-only descriptors, we can expect that there is a better way to represent the shape of an object. Humans can recognize better objects by shape than by color, what makes us wonder if there is a way to improve the techniques used for shape description

Hand gesture recognition with jointly calibrated Leap Motion and depth sensor

Novel 3D acquisition devices like depth cameras and the Leap Motion have recently reached the market. Depth cameras allow to obtain a complete 3D description of the framed scene while the Leap Motion sensor is a device explicitly targeted for hand gesture recognition and provides only a limited set of relevant points. This paper shows how to jointly exploit the two types of sensors for accurate gesture recognition. An ad-hoc solution for the joint calibration of the two devices is firstly presented. Then a set of novel feature descriptors is introduced both for the Leap Motion and for depth data. Various schemes based on the distances of the hand samples from the centroid, on the curvature of the hand contour and on the convex hull of the hand shape are employed and the use of Leap Motion data to aid feature extraction is also considered. The proposed feature sets are fed to two different classifiers, one based on multi-class SVMs and one exploiting Random Forests. Different feature selection algorithms have also been tested in order to reduce the complexity of the approach. Experimental results show that a very high accuracy can be obtained from the proposed method. The current implementation is also able to run in real-time