Eran A. Edirisinghe
Bio: Eran A. Edirisinghe is an academic researcher from Loughborough University. The author has contributed to research in topics: Image compression & Codec. The author has an hindex of 17, co-authored 136 publications receiving 1167 citations. Previous affiliations of Eran A. Edirisinghe include University of South Wales & Sri Lanka Institute of Information Technology.
Papers published on a yearly basis
TL;DR: The proposed method is more accurate and performs more effectively than do the benchmark algorithms considered and compared that of three state‐of‐the‐art methods, namely, the radial gradient index filtering technique, the local mean technique, and the fractal dimension technique.
Abstract: This paper proposes a novel approach to initial lesion detection in ultrasound breast images. The objective is to automate the manual process of region of interest (ROI) labeling in computer-aided diagnosis (CAD). We propose the use of hybrid filtering, multifractal processing, and thresholding segmentation in initial lesion detection and automated ROI labeling. We used 360 ultrasound breast images to evaluate the performance of the proposed approach. Images were preprocessed using histogram equalization before hybrid filtering and multifractal analysis were conducted. Subsequently, thresholding segmentation was applied on the image. Finally, the initial lesions are detected using a rule-based approach. The accuracy of the automated ROI labeling was measured as an overlap of 0.4 with the lesion outline as compared with lesions labeled by an expert radiologist. We compared the performance of the proposed method with that of three state-of-the-art methods, namely, the radial gradient index filtering technique, the local mean technique, and the fractal dimension technique. We conclude that the proposed method is more accurate and performs more effectively than do the benchmark algorithms considered.
••04 Apr 2009
TL;DR: A project which explores mobile imaging and digital storytelling benefit in the East, to support non-textual information sharing in an Indian village is described, showing that the system was usable by a cross-section of the community and valued for its ability to express a mixture of development and community information in an accessible form.
Abstract: Mobile imaging and digital storytelling currently support a growing practice of multimedia communication in the West. In this paper we describe a project which explores their benefit in the East, to support non-textual information sharing in an Indian village. Local audiovisual story creation and sharing activities were carried out in a one month trial, using 10 customized cameraphones and a digital library of stories represented on a village display. The findings show that the system was usable by a cross-section of the community and valued for its ability to express a mixture of development and community information in an accessible form. Lessons for the role of HCI in this context are also discussed.
TL;DR: A comprehensive survey of shadow detection methods, organised in a novel taxonomy based on object/environment dependency and implementation domain is presented, and a comparative evaluation of representative algorithms, based on quantitative and qualitative metrics is presented.
TL;DR: A novel approach for efficient localization of license plates in video sequence and the use of a revised version of an existing technique for tracking and recognition is proposed, intelligent enough to automatically adjust for varying camera distances and diverse lighting conditions.
Abstract: We propose an efficient real-time automatic license plate recognition (ALPR) framework, particularly designed to work on CCTV video footage obtained from cameras that are not dedicated to the use in ALPR. At present, in license plate detection, tracking and recognition are reasonably well-tackled problems with many successful commercial solutions being available. However, the existing ALPR algorithms are based on the assumption that the input video will be obtained via a dedicated, high-resolution, high-speed camera and is/or supported by a controlled capture environment, with appropriate camera height, focus, exposure/shutter speed and lighting settings. However, typical video forensic applications may require searching for a vehicle having a particular number plate on noisy CCTV video footage obtained via non-dedicated, medium-to-low resolution cameras, working under poor illumination conditions. ALPR in such video content faces severe challenges in license plate localization, tracking and recognition stages. This paper proposes a novel approach for efficient localization of license plates in video sequence and the use of a revised version of an existing technique for tracking and recognition. A special feature of the proposed approach is that it is intelligent enough to automatically adjust for varying camera distances and diverse lighting conditions, a requirement for a video forensic tool that may operate on videos obtained by a diverse set of unspecified, distributed CCTV cameras.
TL;DR: This paper provides a comprehensive and in-depth critical analysis from literature which fulfils an identified need of fusing asset information for predictive maintenance so that decision making can be improved.
Abstract: Asset management is a process of identification, design, construction, operation, and maintenance of physical assets (Wenzler, 2005). An asset-centric approach is vital for the success of an asset intensive organisation as the effective management of assets is a major determinant of organisational success. One key issue in asset information management is the availability of information at the right time, in the right format, before the right person, against the right query, and at the right level. This paper provides a comprehensive and in-depth critical analysis from literature which fulfils an identified need of fusing asset information for predictive maintenance so that decision making can be improved. The critical literature review included also highlights the need for an expert system which integrates reliable information with effective decision-support, under the umbrella of Asset Management. Various elements of asset management were critically reviewed, highlighting the need for more robust Predictive maintenance management for assets. We argue that this is best achieved by a system that, in particular, incorporates Expert System to enhance the quality of predictive maintenance through accurate decision analysis. In addition, it should have fuzzy logic reasoning ability that assists in the decision-making process. Our analysis leads us to propose that Expert System when combined with fuzzy logic provides a better way of decision making in predictive maintenance management of assets.
01 Jan 2002
01 Jan 1994
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.
15 Oct 2004
20 Dec 2013
TL;DR: The purpose of this paper is to provide a complete survey of the traditional and recent approaches to background modeling for foreground detection, and categorize the different approaches in terms of the mathematical models used.