4859988
A Multimodal Interface for Personalising Spatial Data in Mobile GIS
Recently the availability and usage of more advanced mobile devices has significantly increased, with many users accessing information and applications while on the go. However, for users to truly accept and adopt such technologies it is necessary to address human-computer interaction challenges associated with such devices. We are interested in exploring these issues within the context of mobile GIS applications. Current mobile GIS interfaces suffer from two major problems: interface complexity and information overload. We have developed a novel system that addresses both of these issues. Firstly, our system allows GIS users to interact multimodally, providing them with the flexibility of choosing their preferred mode of interaction for specific tasks in specific contexts. Secondly, it records all such interactions, analyses them and uses them to build individual user profiles. Based on these, our system returns personalised spatial data to users, and hence eliminates superfluous information that might be otherwise presented to them. In this paper we describe the system we have developed that combines multimodal interaction with personalised services, and that can be used by mobile users, whether they are novices or professionals within the field of GIS. The advantages of our multimodal GIS interface approach are demonstrated through a user interaction study.
18310211
A Case Study of Case-Based CBR
Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for developing CBR systems, it is appealing to acquire it by learning, and CBR is a promising learning method to apply. This observation suggests developing case-based CBR systems, CBR systems whose components themselves use CBR. However, despite early interest in case-based approaches to CBR, this method has received comparatively little attention. Open questions include how case-based components of a CBR system should be designed, the amount of knowledge acquisition effort they require, and their effectiveness. This paper investigates these questions through a case study of issues addressed, methods used, and results achieved by a case-based planning system that uses CBR to guide its case adaptation and similarity assessment. The paper discusses design considerations and presents empirical results that support the usefulness of case-based CBR, that point to potential problems and tradeoffs, and that directly demonstrate the overlapping roles of different CBR knowledge sources. The paper closes with general lessons about case-based CBR and areas for future research.
1721968
A Study of Spatial Interaction Behaviour for Improved Delivery of Web-Based Maps
As access to large quantities of detailed spatial information becomes more commonplace through popular Web-based map browsing systems, the need for automatic spatial information filtering processes grows. Particulary in the case of wireless access, a suitable filtering mechanism can assist the user by personalising the map contents, giving priority to the most relevant content and eliding less relevant content. We have developed a generic solution for implicitly obtaining an insight into a user's interests and intentions based on their mouse interactions with a Web-based map browsing interface. Results of a previously reported experiment showed visual patterns in interface interaction across multiple users. This has led us to develop a new categorisation of the user interactions that can serve as a basis for more accurately weighting user interest profiles. This paper proposes a classification scheme for user interactions according to their functional properties by describing a `mouse movement hierarchy'. The value of such a weighting is demonstrated by employing the hierarchy to make a new experimental analysis of a dataset of previously logged user interactions. By properly understanding sequences of interactions, implicit inferences about the user's interests can be made, leading to improved map personalisation, ultimately improving the user's experience by delivering the most relevant content to the user in a timely fashion.
30290579
Introduction to the Special Issue on Maintaining Case-Based Reasoning Systems
9734730
Using sketches and knowledge bases for geo-spatial image retrieval
Abstract This paper presents research in the field of knowledge management for geo-spatial imagery including scanned aerial photos and satellite images. We have developed a web-based system that allows users to query a database of images not only using metadata, but also drawing sketches of configurations of objects they are interested in as well as inputting textual descriptions of their intended task. Our system integrates case-based reasoning techniques to form a knowledge base from previously issued queries that can be exploited to improve future query processing and to build organizational memory through experience capture. The effective design and implementation of a user-friendly graphic user interface plays an important role for the system to provide improved human–computer interaction and decision support.
9824069
Data Mining Support for Case-Based Collaborative Recommendation
This paper describes ongoing research which aims to enhance collaborative recommendation techniques in the context of PTV, an applied recommender system for the TV listings domain. We have developed a case-based perspective on PTV's collaborative recommendation component, viewing the sparsity problem in collaborative filtering as one of updating and maintaining similarity knowledge for case-based systems. Our approach applies data mining techniques to extract relationships between program items that can be used to address the sparsity/ maintenance problem, as well as employing recommendation ranking that combines user similarities and item similarities to deliver more effective recommendation orderings.
9645862
Aq-Series Approach to Deblurring the Discrete Gaussian
A method is presented for deblurring an image blurred by the discrete Gaussian. The method, based on classical theorems of Jacobi and Ramanujan, not only provides exact formulas for the deblurring, but also condition numbers and error bounds estimating the agreement between the original and reconstructed image. The use of the Jacobi Triple Product Theorem provides a convenient factorization of the formulas used in the inversion process into a product of three infinite series. These three series correspond to a constant together with a Toeplitz operator and its transpose. In the finite setting this factorization corresponds to the factorization of a matrix into the product of Toeplitz matrices, where the entries can be computed using simple recursion formulas. For selected choices of ?, condition numbers are calculated for these operators. The results are similar to a method developed by Kimia and Zucker.
46257718
Comparison of Traditional Brain Segmentation Tools with 3D Self-Organizing Maps
Algorithm-assisted 3D MR brain segmentation may be significantly faster than manual methods and produce visually pleasing results. We tested two- and three-dimensional region growing (2DRG and 3DRG) and self-organizing map (SOM) algorithms for segmentation of the cerebral ventricles. The SOM algorithm provides the greatest times savings, 12∶1, over manual segmentation. Concern for reproducibility of algorithm-assisted segmentation motivated an intra-operator comparative study of these and manual segmentation methods. One of us, DK, segmented the cerebral ventricles from S 3D MR-scan data sets three times manually and with the three algorithms. When variability is measured as the shape variance of derived landmarks sets, the three algorithm-assisted methods show less intra-operator variability than manual segmentation. The 2DRG and 3DRG segmentations show more variability than SOM. Of the 4 methods, SOM segmentation requires the fewest operator decisions.
35531897
Decomposition methods for convolution operators
This paper presents methods for the decomposition of two-dimensional convolution operators of size m × n into sums and products of 3 × 3 operators. If m = n 2k + 1, then the maximum number of 3 × 3 operators needed in the decomposition is k2 + k − 1. In particular, every 5 × 5 operator can be decomposed into the sum and product of at most five 3 × 3 operators. A characterization is given for 5 × 5 operators which can be decomposed into the sum and product of at most three 3 × 3 operators. With operators exhibiting symmetry with respect to both axes and a diagonal, the maximum number of operators needed is k(k + 1)2.
206561425
Biomedical Image Analysis
The papers assembled in this special issue of Computer Vision and Image Understanding represent a selection from among those presented at the First IEEE/ SIAM Joint Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). This meeting represented a blending of the talents and interests of individuals who attended the IEEE Workshop on Biomedical Image Analysis held in Seattle in 1994 and the Mathematical Methods in Medical Imaging Conferences previously held under SPIE sponsorship in San Diego during 1992–1994. The fulllength papers submitted to the workshop were reviewed by at least two members of the program committee and the results were compiled to produce 32 papers presented in a one-track forum on June 21–22, 1996, in San Francisco. In addition, the meeting included two plenary talks by Fred Bookstein and Demetri Terzopoulos. The primary focus of the research presented at the meeting was on the development of new methods for segmentation, registration, shape representation, and motion tracking for biomedical image analysis. The international flavor of these efforts is indicated by the fact that the individuals making the presentations represented France, Japan, Netherlands, Spain, United Kingdom, and the United States. The papers presented in this issue of Computer Vision and Image Understanding focus primarily on the task of extracting information from medical images acquired from X-ray devices, MR images, nuclear medicine scanners, and ultrasound. These are to be distinguished from images of biological structures acquired through light or confocal microscopy or the scanning electron microscope. Analysis of this type of image has expanded rapidly in scope in recent years, driven in part by advances in the parent disciplines of image formation and reconstruction as well as computer vision and shape modeling. However, the expansion owes in part to the steadily larger availability of 3D image data, which provide a playground for volumetric modeling of tissues, without dealing with projection operators encountered in optics. In order to extract information in images in a more automated, accurate, and reproducible way, methods are needed that combine the underlying image physics with explicit or implicit data structures that effectively account for data originating from living tissues. Also, since the objects under investigation in biomedical images represent the large anatomical and biological variability inherent in nature, the researcher is forced to produce methods which can process the degrees of variability in the data. The issue begins with Bookstein’s summary of developments in morphometrics (the multivariate statistics of shape) that are relevant for medical image analysis and image understanding. The second paper, by Okatani and Deguchi, offers a novel approach to the analysis of the endoscopic data increasingly arising in the course of optically guided surgery. The third and fourth papers approach the classic problem of ridge (or skeleton) extraction from medical images. They represent two distinct approaches: one more traditionally algorithmic and one set instead in the context of image evolutions.
204991687
Interpretation of a basic hypergeometric identity with Lie characters and Young tableaux
A combinatorial interpretation involving semistandard tableaux is provided for a four parameter terminating sum identity for the basic hypergeometric series 6?5. This identity is first produced by taking the principal specialization of a tensor product identity for two GL(n) characters.