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This study presents an age and gender estimation system that considers ethnic difference in face images using a Convolutional Neural Network(CNN) and Support Vector Machine(SVM). Most age and gender estimation systems using face images are trained on ethnicity-biased databases. Therefore, these systems show limited performance on face images of ethnic groups occupying a small proportion of the training...
Recently, polyhedral conic classifiers have become popular since they perform better compared to the Support Vector Machines (SVMs). Cone vertex of polyhedral conic classifiers is an important parameter and it is generally taken as the mean of positive data in literature. In this paper, we studied optimally estimating the cone vertex to improve the accuracy of the polyhedral conic classifiers. The...
We have been developing and researching chat robot for elderly people. This paper proposed category estimation for voice based chat robot system. Proposed method estimates a category of the user's utterance sentence. Category or topic estimation have been studied by many researchers, however voice base chat system needs a quick response. Therefore our system is simple method just using keyword match...
Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical...
We extend a recent low cost real-time method of hand tracking and pose estimation in order to control an anthropomorphic robot hand. The approach is data-driven and based on matching the current image of a color-gloved hand with the best fitting image in a database to retrieve the posture. Then, using depth information from a Kinect camera and a color-sensitive iterative closest point-to-triangle...
The robust perception of robots is strongly needed to handle various objects skillfully. In this paper, we propose a novel approach to recognize objects and estimate their 6-DOF pose using 3D feature descriptors, called Geometric and Photometric Local Feature (GPLF). The proposed descriptors use both the geometric and photometric information of 3D point clouds from RGB-D camera and integrate those...
This paper presents a system that gives a robot the ability to diminish its own disturbing noise (i.e., ego noise) by utilizing template-based ego noise estimation, an algorithm previously developed by the authors. In pursuit of an autonomous, online and adaptive template learning system in this work, we specifically focus on eliminating the requirement of an offline training session performed in...
In this paper a novel solution to the problem of guiding a robotic gripper in order to perform manipulation tasks, is presented. The proposed approach consists of two main modules corresponding to the training and testing sessions, respectively. During training, we employ an ontology-based framework with a view to the establishment of a database holding information regarding several geometrical attributes...
This paper describes an extension of the sequential scene analysis system presented by Hager and Wegbreit [12]. In contrast to the original system, which was limited to scenes consisting of geometric primitives, such as spheres, cuboids, and cylinders computed from range data, the extended system is capable of dealing with arbitrarily shaped objects computed from range and intensity images. An object...
A fundamental task for a robotic audition system is sound source localization. This paper addresses the localization problem in a robotic humanoid context, providing a novel learning algorithm that uses binaural cues to determine the sound source's position. Sound signals are extracted from a humanoid robot's ears. Binaural cues are then computed to provide inputs for a neural network. The neural...
This paper deals with sound source localization in a humanoid robotics context. Classical binaural localization algorithms often rely on the following process: first, binaural cues are extracted from the left and right microphone/ear signals; next, a model is exploited to infer the possible localization of the sound source. Such a method thus requires an accurate modeling of the head acoustic shadowing,...
Using pre-recorded templates to estimate and suppress the ego noise of a robot is advantageous because this method is able to cope with the non-stationarity of this particular type of noise. However, standard template-based estimation requires human intervention in the offline training sessions, storage of large amounts of data and does not adapt to the dynamical changes in the environmental conditions...
While a robot is moving, ego noise is generated due to the fans and motors of the robot. Furthermore, a robot is not only subject to the ego noise, but also to the ambient noise of the environment, both having different short-term signal characteristics. Because ego-motion noise generated by the motors is non-stationary, and the BackGround Noise (BGN) is stationary, one single noise estimation method...
The demand for accurate indoor localization techniques is increasing. The existing infrastructure of 802.11 WiFi networks can be exploited to position a device in a building. In this paper a positioning system is presented to locate a robot device in the Faculty of Computer Science building. Several techniques are experimented and the algorithms K Nearest-Neighbours (KNN), weighted KNN, weighted Centroid,...
Markerless, vision based estimation of human hand pose over time is a prerequisite for a number of robotics applications, such as learning by demonstration (LbD), health monitoring, teleoperation, human-robot interaction. It has special interest in humanoid platforms, where the number of degrees of freedom makes conventional programming challenging. Our primary application is LbD in natural environments...
This paper proposes a novel approach to sensor planning for simultaneous object identification and 3D pose estimation. We consider the problem of determining the next-best-view for a movable sensor (or an autonomous agent) to identify an unknown object from among a database of known object models. We use an information theoretic approach to define a metric (based on the difference between the current...
Contact-free estimation of the human somatosensory information is an essential skill for robots working in daily environments. The main objective of this paper is to develop a method for estimating muscle tensions without any sensors attached to the body. Muscle tension is an important information for evaluating physical load during motions. Existing approaches utilizing optimization techniques and/or...
The authors propose a hand posture estimation system in real time and with high accuracy, for robot hand control and human interface with hand motions without no sensors attached to the users. This method searches the similar image quickly from a large volume of previously-sorted image database which contains complicated shapes and self-occlusions of the human hand. Because the system doesn??t need...
In this paper, we study how to build a vision-based system for global localization with accuracies within 10 cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers. In particular, we study the parameters of building a landmark database rapidly and utilizing that database online for real-time accurate global localization. Although the accuracy...
For target tracking in Interference Environments of cognitive radar problem, Extended Karman, Particle filter algorithms etc. are generally used to be regarded as usual solutions to state estimation. Many techniques have been developed to improve performance of target tracking. In this paper, we set the structure and key features of target's tracking design for cognitive radar, and newly propose cognitive...
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