Constraints on the spatiotemporal accuracy of interceptive action: effects of target size on hitting a moving object

Results of two experiments are reported that examined how people respond to rectangular targets of different sizes in simple hitting tasks. If a target moves in a straight line and a person is constrained to move along a linear track oriented perpendicular to the targetrsquos motion, then the length of the target along its direction of motion constrains the temporal accuracy and precision required to make the interception. The dimensions of the target perpendicular to its direction of motion place no constraints on performance in such a task. In contrast, if the person is not constrained to move along a straight track, the targetrsquos dimensions may constrain the spatial as well as the temporal accuracy and precision. The experiments reported here examined how people responded to targets of different vertical extent (height): the task was to strike targets that moved along a straight, horizontal path. In experiment 1 participants were constrained to move along a horizontal linear track to strike targets and so target height did not constrain performance. Target height, length and speed were co-varied. Movement time (MT) was unaffected by target height but was systematically affected by length (briefer movements to smaller targets) and speed (briefer movements to faster targets). Peak movement speed (Vmax) was influenced by all three independent variables: participants struck shorter, narrower and faster targets harder. In experiment 2, participants were constrained to move in a vertical plane normal to the targetrsquos direction of motion. In this task target height constrains the spatial accuracy required to contact the target. Three groups of eight participants struck targets of different height but of constant length and speed, hence constant temporal accuracy demand (different for each group, one group struck stationary targets = no temporal accuracy demand). On average, participants showed little or no systematic response to changes in spatial accuracy demand on any dependent measure (MT, Vmax, spatial variable error). The results are interpreted in relation to previous results on movements aimed at stationary targets in the absence of visual feedback.

Structural refinement of systems specified in object-z and CSP

This paper is concerned with methods for refinement of specifications written using a combination of Object-Z and CSP. Such a combination has proved to be a suitable vehicle for specifying complex systems which involve state and behaviour, and several proposals exist for integrating these two languages. The basis of the integration in this paper is a semantics of Object-Z classes identical to CSP processes. This allows classes specified in Object-Z to be combined using CSP operators. It has been shown that this semantic model allows state-based refinement relations to be used on the Object-Z components in an integrated Object-Z/CSP specification. However, the current refinement methodology does not allow the structure of a specification to be changed in a refinement, whereas a full methodology would, for example, allow concurrency to be introduced during the development life-cycle. In this paper, we tackle these concerns and discuss refinements of specifications written using Object-Z and CSP where we change the structure of the specification when performing the refinement. In particular, we develop a set of structural simulation rules which allow single components to be refined to more complex specifications involving CSP operators. The soundness of these rules is verified against the common semantic model and they are illustrated via a number of examples.

Analysis of support for modularity in object teams based on design patterns

Dissertação de Mestrado em Engenharia Informática

Description of a robotics-oriented relational positioning methodology

This paper presents a relational positioning methodology for flexibly and intuitively specifying offline programmed robot tasks, as well as for assisting the execution of teleoperated tasks demanding precise movements.In relational positioning, the movements of an object can be restricted totally or partially by specifying its allowed positions in terms of a set of geometric constraints. These allowed positions are found by means of a 3D sequential geometric constraint solver called PMF – Positioning Mobile with respect to Fixed. PMF exploits the fact that in a set of geometric constraints, the rotational component can often be separated from the translational one and solved independently.

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

Evaluation of sets of oriented and non-oriented receptive fields as local descriptors

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. We propose a performance criterion for a local descriptor based on the tradeoff between selectivity and invariance. In this paper, we evaluate several local descriptors with respect to selectivity and invariance. The descriptors that we evaluated are Gaussian derivatives up to the third order, gray image patches, and Laplacian-based descriptors with either three scales or one scale filters. We compare selectivity and invariance to several affine changes such as rotation, scale, brightness, and viewpoint. Comparisons have been made keeping the dimensionality of the descriptors roughly constant. The overall results indicate a good performance by the descriptor based on a set of oriented Gaussian filters. It is interesting that oriented receptive fields similar to the Gaussian derivatives as well as receptive fields similar to the Laplacian are found in primate visual cortex.

Rotation Invariant Object Recognition from One Training Example

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of oriented Gaussian derivative filters-- is used in our recognition system. We report here an evaluation of several techniques for orientation estimation to achieve rotation invariance of the descriptor. We also describe feature selection based on a single training image. Virtual images are generated by rotating and rescaling the image and robust features are selected. The results confirm robust performance in cluttered scenes, in the presence of partial occlusions, and when the object is embedded in different backgrounds.

Service oriented approach to facility management using extended building information modelling

Facility management (FM), from a service oriented approach, addresses the functions and requirements of different services such as energy management, space planning and security service. Different service requires different information to meet the needs arising from the service. Object-based Building Information Modelling (BIM) is limited to support FM services; though this technology is able to generate 3D models that semantically represent facility’s information dynamically over the lifecycle of a building. This paper presents a semiotics-inspired framework to extend BIM from a service-oriented perspective. The extended BIM, which specifies FM services and required information, will be able to express building service information in the right format for the right purposes. The service oriented approach concerns pragmatic aspect of building’s information beyond semantic level. The pragmatics defines and provides context for utilisation of building’s information. Semiotics theory adopted in this paper is to address pragmatic issues of utilisation of BIM for FM services.

Analysis of the lightning attractive radius of power transmission lines through the use of a physically-oriented model

Models of different degrees of complexity are found in the literature for the estimation of lightning striking distances and attractive radius of objects and structures. However, besides the oversimplifications of the physical nature of the lightning discharge on which most of them are based, till recently the tridimensional structure configuration could not be considered. This is an important limitation, as edges and other details of the object affect the electric field and, consequently, the upward leader initiation. Within this context, the Self-consistent leader initiation and propagation model (SLIM) proposed by Becerra and Cooray is state-of-the-art leader inception and propagation leader model based on the physics of leader discharges which enables the tridimensional geometry of the structure to be taken into account. In this paper, the model is used for estimating the striking distance and attractive radius of power transmission lines. The results are compared with those obtained from the electrogeometric and Eriksson's models. © 2003-2012 IEEE.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Context-Oriented Programming

Context-dependent behavior is becoming increasingly important for a wide range of application domains, from pervasive computing to common business applications. Unfortunately, mainstream programming languages do not provide mechanisms that enable software entities to adapt their behavior dynamically to the current execution context. This leads developers to adopt convoluted designs to achieve the necessary runtime flexibility. We propose a new programming technique called Context-oriented Programming (COP) which addresses this problem. COP treats context explicitly, and provides mechanisms to dynamically adapt behavior in reaction to changes in context, even after system deployment at runtime. In this paper we lay the foundations of COP, show how dynamic layer activation enables multi-dimensional dispatch, illustrate the application of COP by examples in several language extensions, and demonstrate that COP is largely independent of other commitments to programming style.

Histograms of oriented gradients for fast on-board vehicle verification

Histograms of Oriented Gradients (HoGs) provide excellent results in object detection and verification. However, their demanding processing requirements bound their applicability in some critical real-time scenarios, such as for video-based on-board vehicle detection systems. In this work, an efficient HOG configuration for pose-based on-board vehicle verification is proposed, which alleviates both the processing requirements and required feature vector length without reducing classification performance. The impact on classification of some critical configuration and processing parameters is in depth analyzed to propose a baseline efficient descriptor. Based on the analysis of its cells contribution to classification, new view-dependent cell-configuration patterns are proposed, resulting in reduced descriptors which provide an excellent balance between performance and computational requirements, rendering higher verification rates than other works in the literature.

Compositional verification for object-Z

This paper presents a framework for compositional verification of Object-Z specifications. Its key feature is a proof rule based on decomposition of hierarchical Object-Z models. For each component in the hierarchy local properties are proven in a single proof step. However, we do not consider components in isolation. Instead, components are envisaged in the context of the referencing super-component and proof steps involve assumptions on properties of the sub-components. The framework is defined for Linear Temporal Logic (LTL)

A Semantic-Oriented Architecture of a Functional Module for Personalized and Adaptive Access to the Knowledge in a Multimedia Digital Library

This article presents the principal results of the doctoral thesis “Semantic-oriented Architecture and Models for Personalized and Adaptive Access to the Knowledge in Multimedia Digital Library” by Desislava Ivanova Paneva-Marinova (Institute of Mathematics and Informatics), successfully defended before the Specialised Academic Council for Informatics and Mathematical Modelling on 27 October, 2008.

Creating reusable well-structured pdf as a sequence of component object graphic (cog) elements

Portable Document Format (PDF) is a page-oriented, graphically rich format based on PostScript semantics and it is also the format interpreted by the Adobe Acrobat viewers. Although each of the pages in a PDF document is an independent graphic object this property does not necessarily extend to the components (headings, diagrams, paragraphs etc.) within a page. This, in turn, makes the manipulation and extraction of graphic objects on a PDF page into a very difficult and uncertain process. The work described here investigates the advantages of a model wherein PDF pages are created from assemblies of COGs (Component Object Graphics) each with a clearly defined graphic state. The relative positioning of COGs on a PDF page is determined by appropriate "spacer" objects and a traversal of the tree of COGs and spacers determines the rendering order. The enhanced revisability of PDF documents within the COG model is discussed, together with the application of the model in those contexts which require easy revisability coupled with the ability to maintain and amend PDF document structure.