A simple technique for improving multi-class classification with neural networks

We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.Comment: European Symposium on artificial neural networks (ESANN), Jun 2015, Bruges, Belgiu

A pragmatic approach to multi-class classification

We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.Comment: European Symposium on artificial neural networks (ESANN), Apr 2015, Bruges, Belgium. 201

Bewegungssteuerung autonomer Fahrzeuge mit neuronalen Feldern

Fahrerassistenzsysteme werden eingesetzt, um dem Fahrer eines Kraftfahrzeugs Handlungsabläufe abzunehmen. Diese Handlungsabläufe werden definiert durch eine Aufgabenstellung, die vom Fahrer an das Fahrerassi- stenzsystem übergeben oder systembedingt gelöst wird. Bei komplexen Fahreras- sistenzsystemen ist an eine autonome Navigation im Straßenverkehr gedacht. Es wird ein neues Verfahren vorgestellt, welches eine Bewegungssteuerung eines autonomen Fahrzeugs durchführen kann. Es werden der Lenkwinkel und die Ge- schwindigkeit beeinflußt. Für diese Aufgabe wird ein dynamischer Ansatz aus dem Bereich der neuronalen Felder gewählt. Relevante Attribute für den Fahrt- verlauf auf unterschiedlichem Abstraktionsniveau können dabei einfach (additiv) verarbeitet werden

A simple technique for improving multi-class classification with neural networks

International audienceWe present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results , overall as well as for particular difficult cases, on the hard 10-class gesture classification task

A Deep Learning Approach for Hand Posture Recognition from Depth Data

International audienceGiven the success of convolutional neural networks (CNNs) during recent years in numerous object recognition tasks, it seems logical to further extend their applicability to the treatment of three-dimensional data such as point clouds provided by depth sensors. To this end, we present an approach exploiting the CNN's ability of automated feature generation and combine it with a novel 3D feature computation technique , preserving local information contained in the data. Experiments are conducted on a large data set of 600.000 samples of hand postures obtained via ToF (time-of-flight) sensors from 20 different persons, after an extensive parameter search in order to optimize network structure. Generalization performance, measured by a leave-one-person-out scheme, exceeds that of any other method presented for this specific task, bringing the error for some persons down to 1.5%

Microsoft Word - _2014_robio4.docx

Abstract-Currently in home environments, robot assisting systems with emotion understanding ability are generally achieved in two several manners. The first is the implementing of such systems in such a way that they offer general services for all considered persons without considering privacy, special needs of their interaction partners. The second way is the targetting of such systems for merely one person. In this work we present a robot assisting system, which has both the abilities of assisting several persons at the same time and sustaining their privacy and security issues. The robot can interact with its interaction partner emotionally by analyzing the emotions of her expressed either visually, facial expression, or auditive, speech prosody. The role of this system is the providing of person-specific support in home environment. In order to identify its interaction partner the system uses diverse biometric traits. According to the recognized ID the system, first, adopts towards the needs of recognized person. Second the system loads the corresponding emotional profile of the detected interaction partner in order to practice a personspecific emotional human-robot interaction, which has an advantage over the person independent interaction

Gesichtserkennung auf mobilen Roboterplattformen zu dem Verbundvorhaben "DESIRE - Deutsche Servicerobotik-Initiative"

Schlussbericht ; Laufzeit des Vorhabens/Berichtszeitraum: 01.10.2005-30.11.2009 Auch als elektronische Ressource vorh. Förderkennzeichen BMBF 01IME01K [richtig] - 01IMEO1K [falsch]. - Verbund-Nr. 01042153. - Engl. Berichtsbl. u.d.T.: Face recognition on mobile robot plattforms. - Literaturverz. Bl. 32 Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werde

Fusion of texture and contour based methods for object recognition

We propose a new approach to object detection based on data fusion of texture and edge information. A self organizing Kohonen map is used as the coupling element of the different representations. Therefore, an extension of the proposed architecture incorporating other features, even features not derived from vision modules, is straight forward. It simplifies to a redefinition of the local feature vectors and a retraining of the network structure. The resulting hypotheses of object locations generated by the detection process are finally inspected by a neural network classifier based on co-occurence matrices