Novel precision target detection with adaptive thresholding for dynamic image segmentation

A new adaptive thresholding algorithm concerning extraction of targets from the background in a given image sequence is proposed. The conventional histogram-based or fixed-value thresholdings are deficient in detecting targets due to the poor contrast between targets and the background, or to the change of illumination. This research solves the problems mentioned above by learning the characteristics of the background from the given images and determines the proper thresholds based on this information. Experiments show that the proposed algorithm is superior to the optimal layering algorithm in target detection and tracking. Received: 28 December 1999 / Accepted: 8 August 2000     

Stop word location and identification for adaptive text recognition

Abstract. We propose a new adaptive strategy for text recognition that attempts to derive knowledge about the dominant font on a given page. The strategy uses a linguistic observation that over half of all words in a typical English passage are contained in a small set of less than 150 stop words. A small dictionary of such words is compiled from the Brown corpus. An arbitrary text page first goes through layout analysis that produces word segmentation. A fast procedure is then applied to locate the most likely candidates for those words, using only widths of the word images. The identity of each word is determined using a word shape classifier. Using the word images together with their identities, character prototypes can be extracted using a previously proposed method. We describe experiments using simulated and real images. In an experiment using 400 real page images, we show that on average, eight distinct characters can be learned from each page, and the method is successful on 90% of all the pages. These can serve as useful seeds to bootstrap font learning. Received October 8, 1999 / Revised March 29, 2000     

Long lived adaptive splitter and applications

Summary. Long-lived and adaptive implementations of mutual exclusion and renaming in the read/write shared memory model are presented. An implementation of a task is adaptive if the step complexity of any operation in the implementation is a function of the number of processes that take steps concurrently with the operation. The renaming algorithm assigns a new unique id in the range to any process whose initial unique name is taken from a set of size N, for an arbitrary N and where k is the number of processes that actually take steps or hold a name while the new name is being acquired. The step complexity of acquiring a new name is , while the step complexity of releasing a name is 1. The space complexity of the algorithm is where n is an upper bound on the number of processes that may be active at the same time (acquiring or holding new names), which could be N in the worst case. Both the system response time and the worst case number of operations per process in the presented mutual-exclusion algorithm are adaptive. Both algorithms rely on the basic building block of a long-lived and adaptive splitter. While the adaptive-splitter satisfies a slightly different set of properties than the Moir-Anderson splitter [MA95], it is adaptive and long-lived. In addition, the new splitter properties enable the construction of a non-blocking long-lived (2k-1)-renaming algorithm (which is optimal in the size of the new name space). We believe that the mechanisms introduced in our splitter implementation are interesting on their own, and might be used in other adaptive and long-lived constructions. Received: March 2000 / Accepted July 2001     

Design and evaluation of an adaptive virtual guide for Web applications

Most applications accessible through the Web suffer from a noticeable lack of support for adapting to the different information needs that different users may have regarding a certain topic. However, completely automatic adaptive support can still be confusing for users who may not understand the reasons for the dynamic change in the behaviour of an application. In this paper, we present a possible solution to provide adaptive support that does not disorient the user. The solution integrates a virtual January 18, assistant that is able to provide adaptive support in an adaptable application. We discuss an example of the application of this approach involving the support of Web visits to virtual museums. We then present the results of an empirical usability test of such an application. Published online: 29 January 2002     

An adaptive visual environment for digital libraries

CDL (Corporate Digital Library) is a prototypical intelligent digital library service that is currently being developed at the University of Bari, as an evolution of a previous project named IDL (Intelligent Digital Library). Among the characterizing features of CDL there are a retrieval engine and several facilities available for the library users. In this paper, we present the web-based visual environment we have developed with the aim of improving user-library interaction. The CDL environment is equipped with some novel visual tools that are primarily intended for inexperienced users, who represent most of the users that usually have access to digital libraries. Machine Learning techniques have been exploited in CDL for document analysis, classification, and understanding, as well as for building a user modeling module, which is the basic component for providing CDL with user interface adaptivity. This feature is also discussed in the paper. Received: 15 December 1997 / Revised: June 1999     

An adaptive collect algorithm with applications

Summary. In a shared-memory distributed system, n independent asynchronous processes communicate by reading and writing to shared variables. An algorithm is adaptive (to total contention) if its step complexity depends only on the actual number, k, of active processes in the execution; this number is unknown in advance and may change in different executions of the algorithm. Adaptive algorithms are inherently wait-free, providing fault-tolerance in the presence of an arbitrary number of crash failures and different processes' speed. A wait-free adaptive collect algorithm with O(k) step complexity is presented, together with its applications in wait-free adaptive alg orithms for atomic snapshots, immediate snapshots and renaming. Received: August 1999 / Accepted: August 2001     

User Environments and Individuals: Experience with Adaptive Interface Agents

The traditional style of working with computers generally revolves around the computer being used as a tool, with individual users directly initiating operations and waiting for the results of them. A more recent paradigm of human-computer interaction, based on the indirect management of computing resources, is agent-based interaction. The idea of delegation plays a key part in this approach to computer-based work, which allows individuals to relinquish the routine, mechanistic parts of their everyday tasks, having them performed automatically instead. Adaptive interfaces combine elements of both these approaches, where the goal is to have the interface adapt to its users rather than the reverse. This paper addresses some of the issues arising from a practical software development process which aimed to support individuals using this style of interaction. This paper documents the development of a set of classes which implement an architecture for adaptive interfaces. These classes are intended to be used as part of larger user interface systems which are to exhibit adaptive behaviour. One approach to the implementation of an adaptive interface is to use a set of software “agents”– simple processes which effectively run “in the background”– to decompose the task of implementing the interface. These agents form part of a larger adaptive interface architecture, which in turn forms a component of the adaptive system.     

Yoda,an adaptive soft classification model: content-based similarity queries and beyond

Abstract. Providing a customized result set based upon a user preference is the ultimate objective of many content-based image retrieval systems. There are two main challenges in meeting this objective: First, there is a gap between the physical characteristics of digital images and the semantic meaning of the images. Secondly, different people may have different perceptions on the same set of images. To address both these challenges, we propose a model, named Yoda, that conceptualizes content-based querying as the task of soft classifying images into classes. These classes can overlap, and their members are different for different users. The “soft” classification is hence performed for each and every image feature, including both physical and semantic features. Subsequently, each image will be ranked based on the weighted aggregation of its classification memberships. The weights are user-dependent, and hence different users would obtain different result sets for the same query. Yoda employs a fuzzy-logic based aggregation function for ranking images. We show that, in addition to some performance benefits, fuzzy aggregation is less sensitive to noise and can support disjunctive queries as compared to weighted-average aggregation used by other content-based image retrieval systems. Finally, since Yoda heavily relies on user-dependent weights (i.e., user profiles) for the aggregation task, we utilize the users' relevance feedback to improve the profiles using genetic algorithms (GA). Our learning mechanism requires fewer user interactions, and results in a faster convergence to the user's preferences as compared to other learning techniques. Correspondence to: Y.-S. Chen (E-mail: yishinc@usc.edu) This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC) and IIS-0082826, NIH-NLM R01-LM07061, DARPA and USAF under agreement nr. F30602-99-1-0524, and unrestricted cash gifts from NCR, Microsoft, and Okawa Foundation.     

A review and reappraisal of information technologies within a conceptual framework for individuals with disabilities

With over thirty million individuals with disabilities being reported in the United States, and with information systems permeating nearly every aspect of society, there is a growing urgency to ensure that everyone has equal access to technology. To render information technologies universally accessible, researchers must have at their disposal an accurate and reliable user model. Through the use of the conceptual framework presented here, accurate assumptions can be generated from knowledge of an individual’s impairments, and functional abilities to influence the creation of a user profile. Ultimately, user profiles serve as a vital attribute of user models applied to the design of technology in an effort to achieve universal access. Published online: 18 May 2001     

Traffic and video quality with adaptive neural compression

Video sequences are major sources of traffic for broadband ISDN networks, and video compression is fundamental to the efficient use of such networks. We present a novel neural method to achieve real-time adaptive compression of video. This tends to maintain a target quality of the decompressed image specified by the user. The method uses a set of compression/decompression neural networks of different levels of compression, as well as a simple motion-detection procedure. We describe the method and present experimental data concerning its performance and traffic characteristics with real video sequences. The impact of this compression method on ATM-cell traffic is also investigated and measurement data are provided.     

Extending keyboard adaptability: An investigation

One common typing error is the overlap error, in which two keys are pressed at once. The existing keyboard accessibility filters do not directly address overlap errors. Several techniques for automatic correction of overlap errors are compared in an offline analysis. Leveraging keystroke timing characteristics is shown to achieve a 50% to 75% reduction in errors for study participants with relatively high error rates. A simple heuristic for estimating the accuracy improvement for an individual using this filter is presented and considerations for live implementation and further work are discussed. Published online: 6 November 2002     

CATALOG: a system for detection and rendering of internal log defects using computer tomography

This paper describes the design and implementation of a machine vision system CATALOG for detection and classification of some important internal defects in hardwood logs via analysis of computer axial tomography (CT or CAT) images. The defect identification and classification in CATALOG consists of two phases. The first phase comprises of the segmentation of a single CT image slice, which results in the extraction of 2D defect-like regions from the CT image slice. The second phase comprises of the correlation of the 2D defect-like regions across CT image slices in order to establish 3D support. The segmentation algorithm for a single CT image is a complex form of multiple-value thresholding that exploits both, the prior knowledge of the wood structure within the log and the gray-level characteristics of the image. The algorithm for extraction of 2D defect-like regions in a single CT image first locates the pith of the log cross section, groups the pixels in the segmented image on the basis of their connectivity and classifies each 2D region as either a defect-like region or a defect-free region using shape, orientation and morphological features. Each 2D defect-like region is classified as a defect or non-defect via correlation across corresponding 2D defect-like regions in neighboring CT image slices. The 2D defect-like regions with adequate 3D support are labeled as true defects. The current version of CATALOG is capable of 3D reconstruction and rendering of the log and its internal defects from the individual CT image slices. CATALOG is also capable of simulation and rendering of key machining operations such as sawing and veneering on the 3D reconstructions of the logs. The current version of CATALOG is intended as a decision aid for sawyers and machinists in lumber mills and also as an interactive training tool for novice sawyers and machinists. Received: 1 August 1997 / Accepted: 25 August 1999     

Coarse-to-fine adaptive masks for appearance matching of occluded scenes

In this paper, we discuss an appearance-matching approach to the difficult problem of interpreting color scenes containing occluded objects. We have explored the use of an iterative, coarse-to-fine sum-squared-error method that uses information from hypothesized occlusion events to perform run-time modification of scene-to-template similarity measures. These adjustments are performed by using a binary mask to adaptively exclude regions of the template image from the squared-error computation. At each iteration higher resolution scene data as well as information derived from the occluding interactions between multiple object hypotheses are used to adjust these masks. We present results which demonstrate that such a technique is reasonably robust over a large database of color test scenes containing objects at a variety of scales, and tolerates minor 3D object rotations and global illumination variations. Received: 21 November 1996 / Accepted: 14 October 1997     

The use of cursor measures for motion-impaired computer users

“Point and click” interactions remain one of the key features of graphical user interfaces (GUIs). People with motion-impairments, however, can often have difficulty with accurate control of standard pointing devices. This paper discusses work that aims to reveal the nature of these difficulties through analyses that consider the cursor’s path of movement. A range of cursor measures was applied, and a number of them were found to be significant in capturing the differences between able-bodied users and motion-impaired users, as well as the differences between a haptic force feedback condition and a control condition. The cursor measures found in the literature, however, do not make up a comprehensive list, but provide a starting point for analysing cursor movements more completely. Six new cursor characteristics for motion-impaired users are introduced to capture aspects of cursor movement different from those already proposed. Published online: 6 November 2002     

Dealing with forward and backward jumps in workflow management systems

Workflow management systems (WfMS) offer a promising technology for the realization of process-centered application systems. A deficiency of existing WfMS is their inadequate support for dealing with exceptional deviations from the standard procedure. In the ADEPT project, therefore, we have developed advanced concepts for workflow modeling and execution, which aim at the increase of flexibility in WfMS. On the one hand we allow workflow designers to model exceptional execution paths already at buildtime provided that these deviations are known in advance. On the other hand authorized users may dynamically deviate from the pre-modeled workflow at runtime as well in order to deal with unforeseen events. In this paper, we focus on forward and backward jumps needed in this context. We describe sophisticated modeling concepts for capturing deviations in workflow models already at buildtime, and we show how forward and backward jumps (of different semantics) can be correctly applied in an ad-hoc manner during runtime as well. We work out basic requirements, facilities, and limitations arising in this context. Our experiences with applications from different domains have shown that the developed concepts will form a key part of process flexibility in process-centered information systems. Received: 6 October 2002 / Accepted: 8 January 2003 Published online: 27 February 2003 This paper is a revised and extended version of [40]. The described work was partially performed in the research project “Scalability in Adaptive Workflow Management Systems” funded by the Deutsche Forschungsgemeinschaft (DFG).     

Effects of Airborne Data Link Communication on Demands, Workload and Situation Awareness

An airborne air-to-ground data link communication interface was evaluated in a multi-sector-planning scenario using an Airbus A 340 full flight simulator. In a close-to-reality experimental setting, eight professional crews performed a flight mission in a mixed voice/data link environment. Experimental factors were the medium (voice vs. data link), workload (low vs. high) and the role in the cockpit (pilot flying vs. pilot non-flying). Data link communication and the usability of the newly developed communication interface were rated positively by the pilots, but there is a clear preference for using a data link only in the phase of cruise. Cognitive demands were determined for selected sections of en-route flight. Demands are affected mainly by increased communication needs. In the pilots’ view, although a data link has no effect on safety or the possibilities of intervention, it causes more problems. The subjective workload, as measured with the NASA Task Load Index, increased moderately under data link conditions. A data link has no general effect on pilots’ situation awareness although flight plan negotiations with a data link cause a distraction of attention from monitoring tasks. The use of a data link has an impact on air-to-ground as well as intra-crew communication. Under data link conditions the pilot non-flying plays a more active role in the cockpit. Before introducing data link communication, several aspects of crew resource management have to be reconsidered. Correspondence and offprint requests to: T. Müller, Technical University of Berlin, Institute of Psychology and Ergonomics, Department of Human–Machine Systems, Jebensstrasse 1, 10623 Berlin, Germany.     

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper, we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent, self-reliant file system that aims to optimize striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces. Received May 17, 1994 / Accepted June 9, 1997     

Texture-based volume rendering of adaptive mesh refinement data

Many phenomena in nature and engineering happen simultaneously on rather diverse spatial and temporal scales. In other words, they exhibit a multi-scale character. A special numerical multilevel technique associated with a particular hierarchical data structure is adaptive mesh refinement (AMR). This scheme achieves locally very high spatial and temporal resolutions. Due to its popularity, many scientists are in need of interactive visualization tools for AMR data. In this article, we present a 3D texture-based volume-rendering algorithm for AMR data that directly utilizes the hierarchical structure. Thereby fast rendering performance is achieved even for high-resolution data sets. To avoid multiple rendering of regions that are covered by grids of different levels of resolution, we propose a space partitioning scheme to decompose the volume into axis-aligned regions of equal-sized cells. Furthermore the problems of interpolation artifacts, opacity corrections, and texture memory limitations are addressed. Published online: November 6, 2002 Correspondence to: R. K?hler