Query by example using invariant features from the double dyadic dual-tree complex wavelet transform.
ABSTRACT Widespread use of digital imagery has resulted in a need to manage large collections of images. Systems providing query by example (QBE) capability offer improved access to contents of image libraries by retrieving matches to a query image. Texture is an important feature to consider in the matching process. However, standard approaches often employ a texture feature that is scale and rotation specific, and may not perform well in libraries containing images with scaled or rotated matches to the target query. A novel approach for generating scale and rotation invariant texture features from an extension of the Dual-Tree Complex Wavelet Transform (DT-CWT) is presented herein for use in region-based QBE. An experimental comparison reveals an improved ability of the new technique in retrieving relevant images over the standard approach.
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ABSTRACT: Celiac disease (CD) is a complex autoimmune disorder in genetically predisposed individuals of all age groups triggered by the ingestion of food containing gluten. A reliable diagnosis is of high interest in view of embarking on a strict gluten-free diet, which is the CD treatment modality of first choice. The gold standard for diagnosis of CD is currently based on a histological confirmation of serology, using biopsies performed during upper endoscopy. Computer aided decision support is an emerging option in medicine and endoscopy in particular. Such systems could potentially save costs and manpower while simultaneously increasing the safety of the procedure. Research focused on computer-assisted systems in the context of automated diagnosis of CD has started in 2008. Since then, over 40 publications on the topic have appeared. In this context, data from classical flexible endoscopy as well as wireless capsule endoscopy (WCE) and confocal laser endomicrosopy (CLE) has been used. In this survey paper, we try to give a comprehensive overview of the research focused on computer-assisted diagnosis of CD. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.Computers in Biology and Medicine 02/2015; 65. DOI:10.1016/j.compbiomed.2015.02.007 · 1.46 Impact Factor
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ABSTRACT: Scale invariant texture recognition methods are applied for the computer assisted diagnosis of celiac disease. In particular, emphasis is given to techniques enhancing the scale invariance of multi-scale and multi-orientation wavelet transforms and methods based on fractal analysis. After fine-tuning to specific properties of our celiac disease imagery database, which consists of endoscopic images of the duodenum, some scale invariant (and often even viewpoint invariant) methods provide classification results improving the current state of the art. However, not each of the investigated scale invariant methods is applicable successfully to our dataset. Therefore, the scale invariance of the employed approaches is explicitly assessed and it is found that many of the analyzed methods are not as scale invariant as they theoretically should be. Results imply that scale invariance is not a key-feature required for successful classification of our celiac disease dataset.Medical image analysis 02/2013; 17(4). DOI:10.1016/j.media.2013.02.001 · 3.68 Impact Factor
Conference Paper: Towards Exploratory Video Search Using Linked Data[Show abstract] [Hide abstract]
ABSTRACT: Abstract—Keyword-based,search,in general,is particularly applicable if the searcher,really knows,what,she is looking for and,how,to find it. But in many,cases either the objectives of the searcher,are intrinsically fuzzy or she has no idea of the appropriate,keywords. One way,to solve this problem,is to navigate and explore the search space along a guided,route. In this paper we show, how Linked Open Data can be adopted to facilitate an exploratory,semantic,search for video data. We present a prototype implementation,of exploratory video search and give first results that show,how,traditional keyword-based search can be augmented,by the use of Linked Open Data.ISM 2009, 11th IEEE International Symposium on Multimedia, San Diego, California, USA, December 14-16, 2009; 01/2009