Evaluation of different chrominance models in the detection and reconstruction of faces and hands using the growing neural gas network

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling’s, Alicante, Essex, and Stegmann’s) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction

Gesture-Based Robot Path Shaping

For many individuals, aging is frequently associated with diminished mobility and dexterity. Such decreases may be accompanied by a loss of independence, increased burden to caregivers, or institutionalization. It is foreseen that the ability to retain independence and quality of life as one ages will increasingly depend on environmental sensing and robotics which facilitate aging in place. The development of ubiquitous sensing strategies in the home underpins the promise of adaptive services, assistive robotics, and architectural design which would support a person\u27s ability to live independently as they age. Instrumentation (sensors and processing) which is capable of recognizing the actions and behavioral patterns of an individual is key to the effective component design in these areas. Recognition of user activity and the inference of user intention may be used to inform the action plans of support systems and service robotics within the environment. Automated activity recognition involves detection of events in a sensor data stream, conversion to a compact format, and classification as one of a known set of actions. Once classified, an action may be used to elicit a specific response from those systems designed to provide support to the user. It is this response that is the ultimate use of recognized activity. Hence, the activity may be considered as a command to the system. Extending this concept, a set of distinct activities in the form of hand and arm gestures may form the basis of a command interface for human-robot interaction. A gesture-based interface of this type promises an intuitive method for accessing computing and other assistive resources so as to promote rapid adoption by elderly, impaired, or otherwise unskilled users. This thesis includes a thorough survey of relevant work in the area of machine learning for activity and gesture recognition. Previous approaches are compared for their relative benefits and limitations. A novel approach is presented which utilizes user-generated feedback to rate the desirability of a robotic response to gesture. Poorly rated responses are altered so as to elicit improved ratings on subsequent observations. In this way, responses are honed toward increasing effectiveness. A clustering method based on the Growing Neural Gas (GNG) algorithm is used to create a topological map of reference nodes representing input gesture types. It is shown that learning of desired responses to gesture may be accelerated by exploiting well-rewarded actions associated with reference nodes in a local neighborhood of the growing neural gas topology. Significant variation in the user\u27s performance of gestures is interpreted as a new gesture for which the system must learn a desired response. A method for allowing the system to learn new gestures while retaining past training is also proposed and shown to be effective

Growing Neural Gas with Different Topologies for 3D Space Perception

Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial