Analysis of Fractional Difference Schemes with Application to Radiographic Images

Visual inspection of radiographic images by radiologists is a regular practice in making a diagnosis. Thus the enhancement of details in radiographs can improve inspection and diagnosis certainty. Through this paper we perform the analysis of the fractional gradient for visual improvement of chest radiographs. Two implementations of the fractional derivative operator, based on central fractional differences, are evaluated. Also we tested two norms for calculation of the magnitude of the fractional gradient, Euclidean and infimum norm, and the conducted tests for both norms are consistent

High Dynamic Range Mapping for Synthetic Aperture Radar Images

Luminance compression is often performed for high dynamic range images (still images and videos). A nonlinear tone mapping is applied for the compression in order to reproduce high dynamic range images using devices with a more limited (low) dynamic range. The images obtained after mapping may provide significant content differences in comparison to original data. This can be found for both optical and non-optical images. In this paper, we consider non-optical high dynamic range images, such as synthetic aperture radar images. Particularly, luminance compression may produce unwanted effects. Artificial objects found in an image and speckle noise may significantly affect the quality after tone mapping. In this paper, we consider several examples related to synthetic aperture radar images, as well as several global and a local luminance reduction method. The experimental analysis includes a comparison of several quality assessment methods

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

Route Selection Problem Based on Hopfield Neural Network

Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc.) and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social) with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems

Local contrast enhancement in digital mammography by using mathematical morphology

A new algorithm for both local contrast enhancement and background texture suppression in digital mammographic images is. proposed. The algorithm is based on mathematical morphology applied to gray-scale image processing. Several examples demonstrate the efficiency of the new algorithm in enhancing the details, object extraction and detection of microcalcifications in digital mammograms

Local contrast enhancement in digital mammography by using mathematical morphology

A new algorithm for both local contrast enhancement and background texture suppression in digital mammographic images is. proposed. The algorithm is based on mathematical morphology applied to gray-scale image processing. Several examples demonstrate the efficiency of the new algorithm in enhancing the details, object extraction and detection of microcalcifications in digital mammograms

Electrical Network Functions of Common-Ground Uniform Passive RLC

In the paper are presented the expressions for all network functions of common-ground, uniform passive ladders having, in general, complex terminations at both their ends. The Elmore's delay and rise times calculated for selected types of RLC ladders have indicated their slight deviation from delay and rise times obtained according to their classical definitions. For common-ground, integrating type RC ladder with voltage-step input, the Elmore's delay- and rise-times are produced in closed-form, both for ladder nodes and points. Furthermore, it is proposed a particular common-ground, uniform RLC ladder being amenable to application as delay line for pulsed and analog input signals. For this ladder, the Elmore's delay and rise times relating to their node voltages are produced in a closed-form, enabling thus with the realization of artificial (a) pulse delay line with arbitrarily and independently specified overall Elmore's delay and rise times and (b) true delay line with arbitrarily specified delay time for frequency bounded analog and/or pulsed input signals. In cases (a) and (b), precise procedures are formulated for calculation of ladder length and of all its RLC parameters. The obtained results are illustrated with several practical examples and are, also, verified through pspice simulation