Plinio Moreno

Comparison of target detection algorithms using adaptive background models

This article compares the performance of target detectors based on adaptive background differencing on public benchmark data. Five state of the art methods are described. The performance is evaluated using state of the art measures with respect to ground truth. The original points are the comparison to hand labelled ground truth and the evaluation on a large database. The simpler methods LOTS and SGM are more appropriate to the particular task as MGM using a more complex background model.

Learning relational affordance models for robots in multi-object manipulation tasks

Affordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in statistical relational learning to learn affordance models in such cases. Our models generalize over objects and can deal effectively with uncertainty. Two-object interaction models are learned from robotic interaction with the objects in the world and employed in situations with arbitrary numbers of objects. We illustrate these ideas with experimental results of an action recognition task where a robot manipulates objects on a shelf.

Improving the SIFT descriptor with smooth derivative filters

Several approaches to object recognition make extensive use of local image information extracted in interest points, known as local image descriptors. State-of-the-art methods perform a statistical analysis of the gradient information around the interest point, which often relies on the computation of image derivatives with pixel differencing methods. In this paper, we show the advantages of using smooth derivative filters instead of pixel differences in the performance of a well known local image descriptor. The method is based on the use of odd Gabor functions, whose parameters are selectively tuned to as a function of the local image properties under analysis. We perform an extensive experimental evaluation to show that our method increases the distinctiveness of local image descriptors for image region matching and object recognition.

Gabor parameter selection for local feature detection

Some recent works have addressed the object recognition problem by representing objects as the composition of independent image parts, where each part is modeled with “low-level” features. One of the problems to address is the choice of the low-level features to appropriately describe the individual image parts. Several feature types have been proposed, like edges, corners, ridges, Gaussian derivatives, Gabor features, etc. Often features are selected independently of the object to represent and have fixed parameters. In this work we use Gabor features and describe a method to select feature parameters suited to the particular object considered. We propose a method based on the Information Diagram concept, where “good” parameters are the ones that optimize the filters response in the filter parameter space. We propose and compare some concrete methodologies to choose the Gabor feature parameters, and illustrate the performance of the method in the detection of facial parts like eyes, noses and mouths. We show also the rotation invariance and robustness to small scale changes of the proposed Gabor feature.

Decentralized Sensor Fusion for Ubiquitous Networking Robotics in Urban Areas

In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

A Comparative Study of Local Descriptors for Object Category Recognition: SIFT vs HMAX

In this paper we evaluate the performance of the two most successful state-of-the-art descriptors, applied to the task of visual object detection and localization in images. In the first experiment we use these descriptors, combined with binary classifiers, to test the presence/absence of object in a target image. In the second experiment, we try to locate faces in images, by using a structural model. The results show that HMAX performs slightly better than SIFT in these tasks.

ISROBOTNET: A testbed for sensor and robot network systems

This paper introduces a testbed for sensor and robot network systems, currently composed of 10 cameras and 5 mobile wheeled robots equipped with several sensors for self-localization, obstacle avoidance and vision cameras, and wireless communications. The testbed includes a service-oriented middleware to enable fast prototyping and implementation of algorithms previously tested in simulation, as well as to simplify integration of subsystems developed by different partners. We survey an integrated approach to human-robot interaction that has been developed supported by the testbed under an European research project. The application integrates innovative methods and algorithms for people tracking and waving detection, cooperative perception among static and mobile cameras to improve people tracking accuracy, as well as decision-theoretical approaches to sensor selection and task allocation within the sensor network.

Appearance-Based Object Detection in Space-Variant Images: A Multi-model Approach

Recently, log-polar images have been successfully used in active-vision tasks such as vergence control or target tracking. However, while the role of foveal data has been exploited and is well known, that of periphery seems underestimated and not well understood. Neverthe- less, peripheral information becomes crucial in detecting non-foveated objects or events. In this paper, a multiple-model approach (MMA) for top-down, model-based attention processes is proposed. The advantages oered by this proposal for space-variant image representations are dis- cussed. A simple but representative frontal-face detection task is given as an example of application of the MMA. The combination of appearance- based features and a linear regression-based classifier proved very eec- tive. Results show the ability of the system to detect faces at very low resolutions, which has implications in fields such as visual surveillance.

Vizzy: A Humanoid on Wheels for Assistive Robotics

The development of an assistive robotic platform poses exciting engineering and design challenges due to the diversity of possible applications. This article introduces Vizzy, a wheeled humanoid robot with an anthropomorphic upper torso, that combines easy mobility, grasping ability, human-like visual perception, eye-head movements and arm gestures. The humanoid appearance improves user acceptance and facilitates interaction. The lower body mobile platform is able to navigate both indoors and outdoors. We describe the requirements, design and construction of Vizzy, as well as its current cognitive capabilities and envisaged domains of application.

On the use of probabilistic relational affordance models for sequential manipulation tasks in robotics

In this paper we employ probabilistic relational affordance models in a robotic manipulation task. Such affordance models capture the interdependencies between properties of multiple objects, executed actions, and effects of those actions on objects. Recently it was shown how to learn such models from observed video demonstrations of actions manipulating several objects. This paper extends that work and employs those models for sequential tasks. Our approach consists of two parts. First, we employ affordance models sequentially in order to recognize the individual actions making up a demonstrated sequential skill or high level concept. Second, we utilize the models of concepts to plan a suitable course of action to replicate the observed consequences of a demonstration. For this we adopt the framework of relational Markov decision processes. Empirical results show the viability of the affordance models for sequential manipulation skills for object placement.