15356945
Implicit Profiling for Contextual Reasoning About Users' Spatial Preferences
Information overload is a well documented problem in many application domains. A way of addressing this problem is by creating user profiles and by filtering out all irrelevant information while presenting the users only with information that matches their interests. Our focus is on the spatial domain. We follow an implicit profiling approach by logging users’ mouse movements as they interact with spatial data. The logged information is analysed to support context reasoning about each user’s level of interest in the spatial features shown to him. These inferred interests are used to calculate an interest model for each individual user. Based on this interest model we can filter the information returned to the user, reducing information overload and tailoring the content to suit the users spatial preferences. In this paper we present our approach and discuss the implementation of the system we are developing for capturing users’ spatial interactions and generating user profiles.
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.
34275727
The Law of Choice and the Decision Not to Decide
Disclosed is a seismic source device, known in the industry as an air gun, having shuttle control for allowing a predetermined discharge of compressed air into water for purposes of seismic exploration. The shuttle is controlled by secondary shuttle means for closing the exhaust ports before all of the compressed air in the firing chamber of the air gun is exhausted. This point of closing the exhaust ports before virtually all of the compressed air is discharged differs from prior art air guns. By preventing further discharge of air, secondary pulses are greatly reduced, thereby enhancing the operation of the entire system. Further, by preventing all the air in the firing chamber from being discharged, the system becomes more efficient and more cost effective.
15992473
Categorizing Case-Base Maintenance: Dimensions and Directions
Experience with the growing number of large-scale CBR systems has led to increasing recognition of the importance of case-base maintenance. Multiple researchers have addressed pieces of the case-base maintenance problem, considering such issues as maintaining consistency and controlling case-base growth. However, despite the existence of these cases of case-base maintenance, there is no general framework of dimensions for describing case-base maintenance systems. Such a framework would be useful both to understand the state of the art in case-base maintenance and to suggest new avenues of exploration by identifying points along the dimensions that have not yet been studied. This paper presents a first attempt at identifying the dimensions of case-base maintenance. It shows that characterizations along such dimensions can suggest avenues for future case-base maintenance research and presents initial steps exploring one of those avenues: identifying patterns of problems that require generalized revisions and addressing them with lazy updating.
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.
2995800
Improving Case-Based Recommendation: A Collaborative Filtering Approach
Data Mining, or Knowledge Discovery as it is also known, is becoming increasingly useful in a wide variety of applications. In the following paper, we lookat its use in combating some of the traditional issues faced with recommender systems. We discuss our ongoing work which aims to enhance the performance of PTV, an applied recommender system working in the TV listings domain. This system currently combines the results of separate user-based collaborative and case-based components to recommend programs to users. Our extension to this idea operates on the theory of developing a case-based view of the collaborative component itself. By using data mining techniques to extract relationships between programme items, we can address the sparsity/maintenance problem. We also adopt a unique approach to recommendation ranking which combines user similarities and item similarities to provide more effective recommendation orderings. Experimental results corroborate our ideas, demonstrating the effectiveness of data mining in improving recommender systems by providing similarity knowledge to address sparsity, both at user-based recommendation level and recommendation ranking level.
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.
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.
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.
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.