30,156 research outputs found
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
Satellite downlink scheduling problem: A case study
The synthetic aperture radar (SAR) technology enables satellites to
efficiently acquire high quality images of the Earth surface. This generates
significant communication traffic from the satellite to the ground stations,
and, thus, image downlinking often becomes the bottleneck in the efficiency of
the whole system. In this paper we address the downlink scheduling problem for
Canada's Earth observing SAR satellite, RADARSAT-2. Being an applied problem,
downlink scheduling is characterised with a number of constraints that make it
difficult not only to optimise the schedule but even to produce a feasible
solution. We propose a fast schedule generation procedure that abstracts the
problem specific constraints and provides a simple interface to optimisation
algorithms. By comparing empirically several standard meta-heuristics applied
to the problem, we select the most suitable one and show that it is clearly
superior to the approach currently in use.Comment: 23 page
When emotional intelligence affects peoples' perception of trustworthiness
By adopting social exchange theory and the affect-infusion-model, the hypothesis is made that emotional intelligence (EI) will have an impact on three perceptions of trustworthiness – ability, integrity and benevolence – at the beginning of a relationship. It was also hypothesized that additional information would gradually displace EI in forming the above perceptions. The results reveal that EI initially does not contribute to any of the perceptions of trustworthiness. As more information is revealed EI has an impact on the perception of benevolence, but not on the perceptions of ability and integrity. This impact was observed to be negative when the nature of the information was negative. On the other hand, information alone was shown to have a significant impact on the perceptions of ability and integrity, but not on the perception of benevolence. Theoretical and practical implications of the findings are addressed
Neural network-based colonoscopic diagnosis using on-line learning and differential evolution
In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to modified environmental conditions. Our approach looks at on-line training from the perspective of tracking the changing location of an approximate solution of a pattern-based, and thus, dynamically changing, error function. The proposed hybrid strategy is compared with other standard training methods that have traditionally been used for training neural networks off-line. Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy
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