Component based recognition of objects in an office environment

We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. In experiments we evaluate different sizes and types of components and three standard techniques for component selection. The component classifiers are finally compared to global classifiers on a database of four objects.

Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases

In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.

Matching Interest Points Using Projective Invariant Concentric Circles

We present a new method to perform reliable matching between different images. This method exploits a projective invariant property between concentric circles and the corresponding projected ellipses to find complete region correspondences centered on interest points. The method matches interest points allowing for a full perspective transformation and exploiting all the available luminance information in the regions. Experiments have been conducted on many different data sets to compare our approach to SIFT local descriptors. The results show the new method offers increased robustness to partial visibility, object rotation in depth, and viewpoint angle change.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

On the way to solve lighting problems in underwater imaging

A major obstacle to processing images of the ocean floor comes from the absorption and scattering effects of the light in the aquatic environment. Due to the absorption of the natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion, and, as the vehicle moves, induce shadows in the scene. For this reason, the first step towards application of standard computer vision techniques to underwater imaging requires dealing first with these lighting problems. This paper analyses and compares existing methodologies to deal with low-contrast, nonuniform illumination in underwater image sequences. The reviewed techniques include: (i) study of the illumination-reflectance model, (ii) local histogram equalization, (iii) homomorphic filtering, and, (iv) subtraction of the illumination field. Several experiments on real data have been conducted to compare the different approaches

An ATM distributed simulator for network management research

Due to the high cost of a large ATM network working up to full strength to apply our ideas about network management, i.e., dynamic virtual path (VP) management and fault restoration, we developed a distributed simulation platform for performing our experiments. This platform also had to be capable of other sorts of tests, such as connection admission control (CAC) algorithms, routing algorithms, and accounting and charging methods. The platform was posed as a very simple, event-oriented and scalable simulation. The main goal was the simulation of a working ATM backbone network with a potentially large number of nodes (hundreds). As research into control algorithms and low-level, or rather cell-level methods, was beyond the scope of this study, the simulation took place at a connection level, i.e., there was no real traffic of cells. The simulated network behaved like a real network accepting and rejecting SNMP ones, or experimental tools using the API node

On optimal computation of MPLS label binding for multipoint-to-point connections

Most network operators have considered reducing Label Switched Routers (LSR) label spaces (i.e. the number of labels that can be used) as a means of simplifying management of underlaying Virtual Private Networks (VPNs) and, hence, reducing operational expenditure (OPEX). This letter discusses the problem of reducing the label spaces in Multiprotocol Label Switched (MPLS) networks using label merging - better known as MultiPoint-to-Point (MP2P) connections. Because of its origins in IP, MP2P connections have been considered to have tree- shapes with Label Switched Paths (LSP) as branches. Due to this fact, previous works by many authors affirm that the problem of minimizing the label space using MP2P in MPLS - the Merging Problem - cannot be solved optimally with a polynomial algorithm (NP-complete), since it involves a hard- decision problem. However, in this letter, the Merging Problem is analyzed, from the perspective of MPLS, and it is deduced that tree-shapes in MP2P connections are irrelevant. By overriding this tree-shape consideration, it is possible to perform label merging in polynomial time. Based on how MPLS signaling works, this letter proposes an algorithm to compute the minimum number of labels using label merging: the Full Label Merging algorithm. As conclusion, we reclassify the Merging Problem as Polynomial-solvable, instead of NP-complete. In addition, simulation experiments confirm that without the tree-branch selection problem, more labels can be reduced

Partial disjoint path for multi-layer protection in GMPLS networks

In this paper, different recovery methods applied at different network layers and time scales are used in order to enhance the network reliability. Each layer deploys its own fault management methods. However, current recovery methods are applied to only a specific layer. New protection schemes, based on the proposed partial disjoint path algorithm, are defined in order to avoid protection duplications in a multi-layer scenario. The new protection schemes also encompass shared segment backup computation and shared risk link group identification. A complete set of experiments proves the efficiency of the proposed methods in relation with previous ones, in terms of resources used to protect the network, the failure recovery time and the request rejection ratio

Enhanced multi-layer protection in multi-service GMPLS networks

This paper focuses on QoS routing with protection in an MPLS network over an optical layer. In this multi-layer scenario each layer deploys its own fault management methods. A partially protected optical layer is proposed and the rest of the network is protected at the MPLS layer. New protection schemes that avoid protection duplications are proposed. Moreover, this paper also introduces a new traffic classification based on the level of reliability. The failure impact is evaluated in terms of recovery time depending on the traffic class. The proposed schemes also include a novel variation of minimum interference routing and shared segment backup computation. A complete set of experiments proves that the proposed schemes are more efficient as compared to the previous ones, in terms of resources used to protect the network, failure impact and the request rejection ratio

Adding new components to the knowledge plane GMPLS over WDM networks

A recent study defines a new network plane: the knowledge plane. The incorporation of the knowledge plane over the network allows having more accurate information of the current and future network states. In this paper, the introduction and management of the network reliability information in the knowledge plane is proposed in order to improve the quality of service with protection routing algorithms in GMPLS over WDM networks. Different experiments prove the efficiency and scalability of the proposed scheme in terms of the percentage of resources used to protect the network

Computer-mediated communication and time pressure induce higher cardiovascular responses in the preparatory and execution phases of cooperative tasks

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Nano Electronics and Computer Science Research Group

This is the website for the Nano Research group based at the University of Southampton ECS department, and details current research topics and the people connected with these. It shows some of the current research topics undertaken at the center, and gives an outline of what can be done for post graduate courses.