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

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

Video Shot Boundary Detection based on Multifractal Analisys

Extracting video shots is an essential preprocessing step to almost all video analysis, indexing, and other content-based operations. This process is equivalent to detecting the shot boundaries in a video. In this paper we presents video Shot Boundary Detection (SBD) based on Multifractal Analysis (MA). Low-level features (color and texture features) are extracted from each frame in video sequence. Features are concatenated in feature vectors (FVs) and stored in feature matrix. Matrix rows correspond to FVs of frames from video sequence, while columns are time series of particular FV component. Multifractal analysis is applied to FV component time series, and shot boundaries are detected as high singularities of time series above pre defined treshold. Proposed SBD method is tested on real video sequence with 64 shots, with manually labeled shot boundaries. Detection accuracy depends on number FV components used. For only one FV component detection accuracy lies in the range 76-92% (depending on selected threshold), while by combining two FV components all shots are detected completely (accuracy of 100%)