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.
921515
Combining CBR with Interactive Knowledge Acquisition, Manipulation and Reuse
Because of the complexity of aerospace design, intelligent systems to support and amplify the abilities of aerospace designers have the potential for profound impact on the speed and reliability of design generation. This article describes a framework for supporting the interactive capture of design cases and their application to new problems, illustrating the approach with a discussion of its use in a support system for aircraft design. The project integrates case-based reasoning with interactive tools for capturing expert design knowledge through "concept mapping." Concept mapping tools provide crucial functions for interactively generating and examining design cases and navigating their hierarchical structure, while CBR techniques provide capabilities to facilitate retrieval and to aid interactive adaptation of designs. The project aims simultaneously to develop a useful design aid and more generally to develop practical interactive approaches to fundamental issues of case acquisition and representation, context-sensitive retrieval, and case adaptation.
42745554
Context-Oriented Image Retrieval
In order to help address problems of information overload in digital imagery task domains, we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and the domain tasks that they support by monitoring the interactive manipulation and annotation of task-relevant imagery. In particular, a strong focus on task context serves to ground image annotations in domain specific goals. This contrasts with prevalent annotation schemes that focus on what individual images contain but that provide no context for which, if any, of those aspects are important to users. Our work attempts to leverage a measure of the user's intentions with regard to tasks that they address. We analyze human-computer interaction information that enables us to infer why image contents are important in a particular context and how specific images have been used to address particular domain goals.
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.
15894358
Using Collaborative Filtering Data in Case-Based Recommendation
In the context of PTV, an applied recommender system operating in the TV listings domain, we are examining the potential benefits in merging case-based and collaborative filtering (CF) recommendation techniques by developing case-based reasoning (CBR) methods that employ collaborative filtering style ratings profiles directly as cases. Doing so presents a number of challenges, both in applying a case-based perspective to collaborative filtering, and in addressing the sparsity problem that plagues many collaborative filtering systems. This paper expands on earlier CBR views of collaborative filtering, identifies problems and opportunities for similarity maintenance therein, and proposes and evaluates methods for mining and applying new similarity knowledge.
26290427
Knowledge Capture and Reuse for Geo-spatial Imagery Tasks
The continuously increasing amount and availability of geospatial image data is giving rise to problems of information overload in organisations that rely on digital geo-spatial imagery. Intelligent support for relevant image retrieval is needed in order to help manage large geospatial image libraries. Moreover, managing the knowledge implicit in using geo-spatial imagery to address particular tasks is crucial for capturing and making the most effective use of organisational knowledge assets. We are developing case-based knowledge-management support for large geo-spatial image repositories, which incorporates sketch-based querying for image retrieval; image manipulation and annotation tools for highlighting and composing relevant aspects of task-relevant imagery; and automatic context-based querying for retrieving relevant previous task experiences. This paper describes our approach to knowledge capture and reuse through task-based image annotation, and it introduces the environment we are developing for capture and reuse of task knowledge involving geo-spatial imagery.
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.
18154290
An analytic technique for router comparison
Design automation of electronic systems is generally separated into a number of distinct areas of effort. Breuer [1] has divided design automation into the areas of logic synthesis, gate simulation, partitioning, placement, routing, and fault detection and diagnosis. While this separation may not be complete or entirely accurate, these functions generally must be performed.
13385001
An experimental comparison of force directed placement techniques
Force-directed placement algorithms are experimentally compared using several sample problems. Significant differences are noted in the computational efficiency of the algorithms, and in the relationship of the placement solution to the routability of the resulting board.
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.