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: 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; · 3.09 Impact Factor
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ABSTRACT: In this paper, scale invariant features are extracted from different variants of the Dual-Tree Complex Wavelet Transform (DT-CWT) in order to classify high-magnification colon endoscopy imagery with respect to the pit pattern scheme. To enhance the scale invariance, the Discrete Cosine Transform is applied to the feature vectors, that are achieved from a DT-CWT variant. The feature vectors either consist of the means and standard deviations of the subbands from a DTC-WT variant or of the Weibull parameter of these subbands. Superior results as compared to techniques described previously in literature are reported.01/2010;
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ABSTRACT: This paper presents a comprehensive overview on the area of energy-aware common content distribution over wireless networks with mobile-to-mobile cooperation. It is assumed that a number of mobile terminals (MTs) that are geographically close to each other are interested in downloading the same content from a server via a base station using a long-range wireless technology. Selected MTs download the content directly from the base station and transmit it to other MTs using a short-range wireless technology. This cooperation can lead to significant performance gains since short-range wireless technologies are energy efficient and provide higher data rates due to the geographical proximity among the MTs. In this paper, we highlight the main alternatives that shape the design of cooperative content distribution architectures with focus on energy efficiency. These include content segmentation, long-range and short-range distribution strategies, grouping of the MTs into cooperating clusters, single hop and multihop communications among the MTs, resource allocation, fairness considerations, and network dynamics. We also discuss various methods commonly utilized for developing content distribution algorithms and evaluating network performance. Finally, we present sample results for selected network scenarios, discuss related standardization activities, and highlight future research directions.IEEE Communications Surveys & Tutorials 01/2013; 15(4):1736-1760. · 6.49 Impact Factor