In this paper, region based stereo matching algorithms are developed for extraction depth information from two colour stereo image pair of waste bin. Depth calculation technique from a disparity map is a key part of this study. A filter eliminating unreliable disparity estimation was used for...

The research reported in the paper aims the development of some methodologies for noise removal in image restoration. In real life, there is always some kind of noise present in the observed data. Therefore, it has been proposed that the ICA model used in image restoration should include noise...

The detection of remote homologies is of major importance for molecular biology applications like drug discovery. The problem is still very challenging even for state-of-the-art probabilistic models of protein families, namely Profile HMMs. In order to improve remote homology detection we...

Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to...

This research extends a method previously applied to music and philosophy (Vilson Vieira et al., 2012), representing the evolution of art as a time-series where relations like dialectics are measured quantitatively. For that, a corpus of paintings of 12 well-known artists from baroque and...

Analyzing the vibration signals of wind turbine usually requires feature extraction. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. In this paper, a new denoising method based on adaptive...

Plant species are living things and are generally categorized in terms of Domain, Kingdom, Phylum, Class, Order, Family, Genus and name of Species in a hierarchical fashion. This paper formulates the taxonomic leaf categorization problem as the hierarchical classification task and provides a...

This paper presents a data mining (DM) based approach to developing ensemble models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy. This approach mainly consists of three steps. Firstly, outlier detection, which merges feature...

In statistical practice multicollinearity of predictor variables is rather the rule than the exception and appropriate models are needed to avoid instability of predictions. Feature extraction methods reflect the idea that latent variables not measurable directly are underlying the original...

Empirical modeling of the industrial antibiotic fed-batch fermentation process is discussed in this paper. Several methods including neural networks, genetic algorithms and feature selection are combined with prior knowledge in the research methodology. A linear model, a radial basis function...