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School of Computer Science & IT, Nottingham University, United Kingdom

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9 Papers

A Variable Neighbourhood Search Algorithm with Compound Neighbourhoods for VRPTW

Topics: Optimization; Routing

Authors: Binhui Chen ¹ , Rong Qu ¹ , Ruibin Bai ² and Hisao Ishibuchi ³

Affiliations: ¹ The University of Nottingham, United Kingdom , ² University of Nottingham Ningbo, China , ³ Osaka Prefecture University, Japan

Digital Object Identifier: 10.5220/0005661800250035

Proceedings of ICORES 2016, ISBN: 978-989-758-171-7

Quick Abstract

The Vehicle Routing Problem with Time Windows (VRPTW) consists of constructing least cost routes from a depot to a set of geographically scattered service points and back to the depot, satisfying service time interval and capacity constraints. A Variable Neighbourhood Search algorithm with Compound Neighbourhoods is proposed to solve VRPTW in this paper. A number of independent neighbourhood operators are composed into compound neighbourhood operators in a new way, to explore wider search area concerning two objectives (to minimize the number of vehicles and the total travel distance) simultaneously. Promising results are obtained on benchmark datasets.

Related Ontology Subjects/Areas/Topics (9)

Applications	Knowledge-Based Systems	Optimization
Artificial Intelligence	Methodologies and Technologies	Routing
Knowledge Discovery and Information Retrieval	Operational Research	Symbolic Systems

Constrained Portfolio Optimisation: The State-of-the-Art Markowitz Models

Topic: Optimization in Finance

Authors: Yan Jin , Rong Qu and Jason Atkin

Affiliation: The University of Nottingham, United Kingdom

Digital Object Identifier: 10.5220/0005758303880395

Proceedings of ICORES 2016, ISBN: 978-989-758-171-7

Quick Abstract

This paper studies the state-of-art constrained portfolio optimization models, using exact solver to identify the optimal solutions or lower bound for the benchmark instances at the OR-library with extended constraints. The effects of pre-assignment, round-lot, and class constraints based on the quantity and cardinality constrained Markowitz model are firstly investigated to gain insights of increased problem difficulty, followed by the analysis of various constraint settings including those mostly studied in the literature. The study aims to provide useful guidance for future investigations in computational algorithms.

Related Ontology Subjects/Areas/Topics (3)

Methodologies and Technologies

Operational Research

Optimization in Finance

Multi-objective Scatter Search with External Archive for Portfolio Optimization

Topics: Evolutionary Multiobjective Optimization; Evolution Strategies

Authors: Khin Lwin ¹ , Rong Qu ¹ and Jianhua Zheng ²

Affiliations: ¹ School of Computer Science and University of Nottingham, United Kingdom , ² University of Nottingham, United Kingdom

Digital Object Identifier: 10.5220/0004552501110119

Proceedings of ECTA 2013/IJCCI, ISBN: 978-989-8565-77-8

Quick Abstract

The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multiobjective Scatter Search approach. In this work, we present a hybrid multiobjective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection problem. We considered the extended mean-variance portfolio model with three practical constraints which limit the number of assets in a portfolio, restrict the proportions of assets held in the portfolio and pre-assign some specific assets in the portfolio. The proposed hybrid metaheuristic algorithm follows the basic structure of the Scatter Search and defines the reference set solutions based on Pareto dominance and crowding distance. New Subset generation and combination methods are proposed to generate efficient and diversified portfolios. Hill Climbing operation is integrated to search for improved portfolios. The performance of the proposed multiobjective Scatter Search algorithm is compared with Non-dominated Sorting Genetic Algorithm (NSGA-II). Experimental results indicate that the proposed algorithm is a promising approach for solving the constrained portfolio selection problem. Measurements by the performance metrics indicate that it outperforms NSGA-II on the solution quality within a shorter computational time.

Related Ontology Subjects/Areas/Topics (6)

Artificial Intelligence		Evolution Strategies		Evolutionary Multiobjective Optimization
Computational Intelligence		Evolutionary Computing		Soft Computing

ADAPTING WEB IMAGES FOR BLIND PEOPLE

Topic: Accessibility Issues and Technology

Authors: A. Fatzilah Misman ¹ and Peter Blanchfield ²

Affiliations: ¹ International Islamic University Malaysia, Malaysia , ² School of Computer Science and University of Nottingham, United Kingdom

Digital Object Identifier: 10.5220/0003402004300437

Proceedings of WEBIST 2011, ISBN: 978-989-8425-51-5

Quick Abstract

One way to remedy the gap that evidently exists between the image element on the web and the web user who is visually blind is by redefining connection between the image and the abundant element of the web itself i.e. text. Studies on the exploitation are done largely related to the fields like the HCI, the semantic web, the information retrieval or even a new hybrid approach. However, often many see the problem from the perspective of the third party. This position paper posits that the problem can also be seen from the fundamental reasons for an image being on a web page without neglecting the connection that develops from the web user’s perspective. Effective and appropriate image tagging may consider this view.

Related Ontology Subjects/Areas/Topics (3)

Accessibility Issues and Technology

Web Information Systems and Technologies

Web Interfaces and Applications

MEDICAL IMAGING IN A CLOUD COMPUTING ENVIRONMENT

Topics: Cloud Application Portability; SAAS, PAAS, IAAS

Authors: Louis Parsonson ¹ , Li Bai ¹ , Laurence Bourn ² , Atif Bajwa ² and Soeren Grimm ²

Affiliations: ¹ School of Computer Science and University of Nottingham, United Kingdom , ² Biotronics3D, United Kingdom

Digital Object Identifier: 10.5220/0003383803270332

Proceedings of CLOSER 2011, ISBN: 978-989-8425-52-2

Quick Abstract

In this paper we present a cloud computing environment for medical imaging which deals with the issues of scaling a traditionally single-user solution to a software-as-a-service solution. We will first introduce volume rendering for medical imaging, and the issues with volume rendering of medical images on the cloud. We will then describe our method for accelerating CPU based volume rendering on the cloud and for scaling the system to a software-as-a-service solution.

Related Ontology Subjects/Areas/Topics (5)

Cloud Application Portability		Fundamentals		SAAS, PAAS, IAAS
Cloud Computing		Platforms and Applications

LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization

Topic: Medical Image Detection, Acquisition, Analysis and Processing

Authors: David Feltell and Li Bai

Affiliation: School of Computer Science & IT, Nottingham University, United Kingdom

Digital Object Identifier: 10.5220/0001067702100217

Proceedings of BIOSIGNALS 2008/BIOSTEC, ISBN: 978-989-8111-18-0

Quick Abstract

This paper presents a novel 3D brain segmentation method based on level sets and bio-inspired methodologies. Level set segmentation methods, although highly promising, require manual selection of seed positions and thereshold parameters, along with manual reinitialisation to a new level set surface for each candidate region. Here, the use of swarm intelligent mechanisms is used to provide all the statistical data and sample points required, allowing automatic initialisation of multiple level set solvers. This is shown by segmentation of white matter, grey matter and cerebro-spinal fluid in a simulated T1 MRI scan, followed by direct comparison between a commercial level application - FMRIB’s FAST - and the ground truth anatomical model.

Related Ontology Subjects/Areas/Topics (5)

Applications and Services		Biomedical Signal Processing		Medical Image Detection, Acquisition, Analysis and Processing
Biomedical Engineering		Computer Vision, Visualization and Computer Graphics

FAST ADAPTABLE SKIN COLOUR DETECTION IN RGB SPACE

Topics: Implementation of Image and Video Processing Systems; Segmentation and Grouping; Tracking of People and Surveillance; Real-Time Vision

Authors: Martin Tosas ¹ and Steven Mills ²

Affiliations: ¹ School of Computer Science and IT, University of Nottingham, United Kingdom , ² Geospatial Research Center (NZ) Ltd, University of Canterbury, New Zealand

Digital Object Identifier: 10.5220/0002055800030010

Proceedings of Human Presence Detection for Context-aware Systems 2007/VISAPP, ISBN: 978-972-8865-75-7

Quick Abstract

This paper presents a skin colour classifier that uses a linear container in order to confine a volume of the RGB space where skin colour is likely to appear. The container can be adapted, using a single training image, to maximize the detection of a particular skin tonality. The classifier has minimum storage requirements, it is very fast to evaluate, and despite operating in the RGB space, provides equivalent illumination (brightness) independence to that of classifiers that work in the rg-plane. The performance of the proposed classifier is evaluated and compared with other classifiers. Finally, conclusions are drawn.

Related Ontology Subjects/Areas/Topics (8)

Computer Vision, Visualization and Computer Graphics	Implementation of Image and Video Processing Systems	Segmentation and Grouping
Image and Video Analysis	Motion, Tracking and Stereo Vision	Tracking of People and Surveillance
Image Formation and Preprocessing	Real-Time Vision

PARALLEL GABOR PCA WITH FUSION OF SVM SCORES FOR FACE VERIFICATION

Topic: Face and Gesture Recognition

Authors: Ángel Serrano ¹ , Cristina Conde ¹ , Isaac Martín de Diego ¹ , Enrique Cabello ¹ , Li Bai ² and Linlin Shen ²

Affiliations: ¹ Face Recognition & Artificial Vision Group, Universidad Rey Juan Carlos, Spain , ² School of Computer Science & IT, University of Nottingham, United Kingdom

Digital Object Identifier: 10.5220/0002055501490154

Proceedings of VISAPP 2007, ISBN: 978-972-8865-74-0

Quick Abstract

Here we present a novel fusion technique for support vector machine (SVM) scores, obtained after a dimension reduction with a principal component analysis algorithm (PCA) for Gabor features applied to face verification. A total of 40 wavelets (5 frequencies, 8 orientations) have been convolved with public domain FRAV2D face database (109 subjects), with 4 frontal images with neutral expression per person for the SVM training and 4 different kinds of tests, each with 4 images per person, considering frontal views with neutral expression, gestures, occlusions and changes of illumination. Each set of wavelet-convolved images is considered in parallel or independently for the PCA and the SVM classification. A final fusion is performed taking into account all the SVM scores for the 40 wavelets. The proposed algorithm improves the Equal Error Rate for the occlusion experiment compared to a Downsampled Gabor PCA method and obtains similar EERs in the other experiments with fewer coefficients after the PCA dimension reduction stage.

3D REGISTRATION AND MODELLING FOR FACE RECOGNITION

Topics: Face and Gesture Recognition; Localization and Recognition of 3D Object Features

Authors: Li Bai and Yi Song

Affiliation: School of Computer Science and Information Technology, University of Nottingham, United Kingdom

Digital Object Identifier: 10.5220/0001371002010208

Proceedings of VISAPP 2006, ISBN: 972-8865-40-6

Quick Abstract

AbstractThis paper presents a new approach to automatic 3D face recognition using a model-based approach. This work uses real 3D dense point cloud data acquired with a stereo face scanner. Since the point clouds are in varied orientations, by applying a non-iterative registration technique, we automatically transform each point cloud to a canonical position and detect facial features used for defining the frontal part of face which is to be modelled in next step. Unlike the iterative ICP algorithm, our non-iterative registration process is scale invariant. An efficient B-spline surface-fitting technique is developed to represent 3D faces in a way that allows efficient surface comparison. This is based on a novel knot vector standardisation algorithm to allow one-to-one mapping from the object space to a parameter space. Consequently, correspondence between objects is established based on shape descriptors, which can be used for recognition.