Andere Kunden interessierten sich auch für
![The Information Content of Canadian Implied Volatility Indexes]( "The Information Content of Canadian Implied Volatility Indexes")
The Information Content of Canadian Implied Volatility Indexes32,99 €![Image Denoising using Switching Adaptive Decision Based Algorithm]( "Image Denoising using Switching Adaptive Decision Based Algorithm")
Image Denoising using Switching Adaptive Decision Based Algorithm32,99 €![Transformation Behavior and Shape Memory Property of Ti-Ni-Cu Alloys]( "Transformation Behavior and Shape Memory Property of Ti-Ni-Cu Alloys")
Transformation Behavior and Shape Memory Property of Ti-Ni-Cu Alloys45,99 €![Brain MRI Segmentation using Texture Features]( "Brain MRI Segmentation using Texture Features")
Brain MRI Segmentation using Texture Features32,99 €![Colored Image Compression by using EZW, SPIHT, EBCOT]( "Colored Image Compression by using EZW, SPIHT, EBCOT")
Colored Image Compression by using EZW, SPIHT, EBCOT24,99 €![Brand Image influence on customer behavior in UK Commercial Banks]( "Brand Image influence on customer behavior in UK Commercial Banks")
Brand Image influence on customer behavior in UK Commercial Banks24,99 €![Use of image processing through Emotion detection and fuzzy filter]( "Use of image processing through Emotion detection and fuzzy filter")
Use of image processing through Emotion detection and fuzzy filter26,99 €
The field of content-based image retrieval (CBIR) focuses on the analysis of image content and the development of tools to represent the visual content in a way that can be efficiently searched and compared. The main aim of this work is to improve the retrieval performance of medical images retrieval methodology that is based on various types of visual features (such as color, texture and shape). The improvement is based on a new multi-level retrieval scheme that deals with different types of medical databases. Within the two phases of the system (i.e., enrollment and retrieval) some new features are introduced. Also, to reach high precision and recall levels, various methods are combined; such as the similarity measures based on Euclidian distance and the artificial neural network trained using back propagation algorithm.