19 resultados para interest-based negotiation

em Cambridge University Engineering Department Publications Database


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This paper presents a novel approach using combined features to retrieve images containing specific objects, scenes or buildings. The content of an image is characterized by two kinds of features: Harris-Laplace interest points described by the SIFT descriptor and edges described by the edge color histogram. Edges and corners contain the maximal amount of information necessary for image retrieval. The feature detection in this work is an integrated process: edges are detected directly based on the Harris function; Harris interest points are detected at several scales and Harris-Laplace interest points are found using the Laplace function. The combination of edges and interest points brings efficient feature detection and high recognition ratio to the image retrieval system. Experimental results show this system has good performance. © 2005 IEEE.

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Tunable materials with high anisotropy of refractive index and low loss are of particular interest in the microwave and terahertz range. Nematic liquid crystals are highly sensitive to electric and magnetic fields and may be designed to have particularly high birefringence. In this paper we investigate birefringence and absorption losses in an isothiocyanate based liquid crystal (designed for high anisotropy) in a broad range of the electromagnetic spectrum, namely 0.1-4 GHz, 30 GHz, 0.5-1.8 THz, and in the visible and near-infrared region (400 nm-1600 nm). We report high birefringence (Δn = 0.19-0.395) and low loss in this material. This is attractive for tunable microwave and terahertz device applications.

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This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location database. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the LVSM is fast, but not accurate enough, whereas localization from the location database using a voting algorithm is relatively slow, but more accurate. The integration of coarse and fine stages makes fast and reliable localization possible. If necessary, the localization result can be verified by epipolar geometry between the representative view in the database and the view to be localized. In addition, the localization system recovers the position of the camera by essential matrix decomposition. The localization system has been tested in indoor and outdoor environments. The results show that our approach is efficient and reliable. © 2006 IEEE.

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This paper presents a novel coarse-to-fine global localization approach that is inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by SIFT descriptors are used as natural land-marks. These descriptors are indexed into two databases: an inverted index and a location database. The inverted index is built based on a visual vocabulary learned from the feature descriptors. In the location database, each location is directly represented by a set of scale invariant descriptors. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the inverted index is fast but not accurate enough; whereas localization from the location database using voting algorithm is relatively slow but more accurate. The combination of coarse and fine stages makes fast and reliable localization possible. In addition, if necessary, the localization result can be verified by epipolar geometry between the representative view in database and the view to be localized. Experimental results show that our approach is efficient and reliable. ©2005 IEEE.

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Chemical looping combustion (CLC) is a means of combusting carbonaceous fuels, which inherently separates the greenhouse gas carbon dioxide from the remaining combustion products, and has the potential to be used for the production of high-purity hydrogen. Iron-based oxygen carriers for CLC have been subject to considerable work; however, there are issues regarding the lifespan of iron-based oxygen carriers over repeated cycles. In this work, haematite (Fe2O3) was reduced in an N2+CO+CO2 mixture within a fluidised bed at 850°C, and oxidised back to magnetite (Fe3O4) in a H2O+N2 mixture, with the subsequent yield of hydrogen during oxidation being of interest. Subsequent cycles started from Fe3O4 and two transition regimes were studied; Fe3O4↔Fe0.947O and Fe 3O4↔Fe. Particles were produced by mechanical mixing and co-precipitation. In the case of co-precipitated particles, Al was added such that the ratio of Fe:Al by weight was 9:1, and the final pH of the particles during precipitation was investigated for its subsequent effect on reactivity. This paper shows that co-precipitated particles containing additives such as Al may be able to achieve consistently high H2 yields when cycling between Fe3O4 and Fe, and that these yields are a function of the ratio of [CO2] to [CO] during reduction, where thermodynamic arguments suggest that the yield should be independent of this ratio. A striking feature with our materials was that particles made by mechanical mixing performed much better than those made by co-precipitation when cycling between Fe3O4 and Fe0.947O, but much worse than co-precipitated particles when cycling between Fe3O 4 and Fe.

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This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of depth sensors and the diminishing returns to be had from appearance alone have seen a surge in shape-based methods. In this work we investigate the performance of several detectors of interest points in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application. © 2011 IEEE.

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A computer can assist the process of design by analogy by recording past designs. The experience these represent could be much wider than that of designers using the system, who therefore need to identify potential cases of interest. If the computer assists with this lookup, the designers can concentrate on the more interesting aspect of extracting and using the ideas which are found. However, as the knowledge base grows it becomes ever harder to find relevant cases using a keyword indexing scheme without knowing precisely what to look for. Therefore a more flexible searching system is needed.

If a similarity measure can be defined for the features of the designs, then it is possible to match and cluster them. Using a simple measure like co-occurrence of features within a particular case would allow this to happen without human intervention, which is tedious and time- consuming. Any knowledge that is acquired about how features are related to each other will be very shallow: it is not intended as a cognitive model for how humans understand, learn, or retrieve information, but more an attempt to make effective, efficient use of the information available. The question remains of whether such shallow knowledge is sufficient for the task.

A system to retrieve information from a large database is described. It uses co-occurrences to relate keywords to each other, and then extends search queries with similar words. This seems to make relevant material more accessible, providing hope that this retrieval technique can be applied to a broader knowledge base.

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This paper provides an overview of the rationale behind the significant interest in polymer-based on-board optical links together with a brief review of recently reported work addressing certain challenges in this field. Polymer-based optical links have garnered considerable research attention due to their important functional attributes and compelling cost-benefit advantages in on-board optoelectronic systems as they can be cost-effectively integrated on conventional printed circuit boards. To date, significant work on the polymer materials, their fabrication process and their integration on standard board substrates have enabled the demonstration of numerous high-speed on-board optical links. However, to be deployed in real-world systems, these optoelectronic printed circuit boards (OE PCBs) must also be cost-effective. Here, recent advances in the integration process focusing on simple direct end-fire coupling schemes and the use of low-cost FR4 PCB substrates are presented. Performance of two proof-of-principle 10 Gb/s systems based on this integration method are summarised while work in realising more complex yet compact planar optical components is outlined. © 2011 IEEE.

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This paper presents an agenda-based user simulator which has been extended to be trainable on real data with the aim of more closely modelling the complex rational behaviour exhibited by real users. The train-able part is formed by a set of random decision points that may be encountered during the process of receiving a system act and responding with a user act. A sample-based method is presented for using real user data to estimate the parameters that control these decisions. Evaluation results are given both in terms of statistics of generated user behaviour and the quality of policies trained with different simulators. Compared to a handcrafted simulator, the trained system provides a much better fit to corpus data and evaluations suggest that this better fit should result in improved dialogue performance. © 2010 Association for Computational Linguistics.

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Modelling dialogue as a Partially Observable Markov Decision Process (POMDP) enables a dialogue policy robust to speech understanding errors to be learnt. However, a major challenge in POMDP policy learning is to maintain tractability, so the use of approximation is inevitable. We propose applying Gaussian Processes in Reinforcement learning of optimal POMDP dialogue policies, in order (1) to make the learning process faster and (2) to obtain an estimate of the uncertainty of the approximation. We first demonstrate the idea on a simple voice mail dialogue task and then apply this method to a real-world tourist information dialogue task. © 2010 Association for Computational Linguistics.

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We present a matching framework to find robust correspondences between image features by considering the spatial information between them. To achieve this, we define spatial constraints on the relative orientation and change in scale between pairs of features. A pairwise similarity score, which measures the similarity of features based on these spatial constraints, is considered. The pairwise similarity scores for all pairs of candidate correspondences are then accumulated in a 2-D similarity space. Robust correspondences can be found by searching for clusters in the similarity space, since actual correspondences are expected to form clusters that satisfy similar spatial constraints in this space. As it is difficult to achieve reliable and consistent estimates of scale and orientation, an additional contribution is that these parameters do not need to be determined at the interest point detection stage, which differs from conventional methods. Polar matching of dual-tree complex wavelet transform features is used, since it fits naturally into the framework with the defined spatial constraints. Our tests show that the proposed framework is capable of producing robust correspondences with higher correspondence ratios and reasonable computational efficiency, compared to other well-known algorithms. © 1992-2012 IEEE.

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The effectiveness of ventilation flows is considered from the perspective of buoyancy (or heat) removal from a space. This perspective is distinct from the standard in which the effectiveness is based on the concentrations of a neutrally buoyant contaminant/passive tracer. Three new measures of effectiveness are proposed based on the ability of a flow to flush buoyancy from a ventilated space. These measures provide estimates of instantaneous and time-averaged effectiveness for the entire space, and local effectiveness at any height of interest. From a generalisation of the latter, a vertical profile of effectiveness is defined. These measures enable quantitative comparisons to be made between different flows and they are applicable when there is a difference in density (as is typical due to temperature differences) between the interior environment and the replacement air. Applications, therefore, include natural ventilation, hybrid ventilation and a range of forced ventilation flows. Finally, we demonstrate how the ventilation effectiveness of a room may be assessed from simple traces of temperature versus time. © 2006 Elsevier Ltd. All rights reserved.

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This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of 3D shape acquisition techniques and the diminishing returns to be had from appearance alone have seen a surge in 3D shape-based methods. In this work, we investigate the performance of several state of the art interest points detectors in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application. © 2012 Springer Science+Business Media, LLC.

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Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full-and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios. © 1992-2012 IEEE.