Search results for "Complex."
showing 10 items of 5824 documents
An advanced variant of an interpolatory graphical display algorithm
2004
In this paper an advanced interpolatory graphical display algorithm based on cardinal B-spline functions is provided. It is well-known that B-spline functions are a flexible tool to design various scale rapresentations of a signal. The proposed method allows to display without recursion a function at any desiderable resolution so that only initial data and opportune vectors weight are involved. In this way the structure of the algorithm is independent across the scale and a computational efficiency is reached. In this paper mono and bi-dimensional vectors weight generated by means of centered cubic cardinal B-spline functions have been supplied. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Wei…
Attentional vs computational complexity measures in observing paintings
2009
Because of the great heterogeneity of subjects and styles, esthetic perception delineates a special and elusive field of research in vision, which represents an interesting challenge for cognitive science tools. With specific regard to the role of visual complexity, in this paper we present an experiment aimed to measure this dimension in a heterogeneous set of paintings. We compared perceived time complexity measures - based on a temporal estimation paradigm - with physical and statistical properties of the paintings, obtaining a strong correlation between psychological and computational results.
Convolutional Regression Tsetlin Machine: An Interpretable Approach to Convolutional Regression
2021
The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has shown competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST pattern classification benchmarks, both in terms of accuracy and memory footprint. In this paper, we propose the Convolutional Regression Tsetlin Machine (C-RTM) that extends the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution operation as in the CTM and then maps the identified patterns into a real…
Low-Rate Reduced Complexity Image Compression using Directionlets
2006
The standard separable two-dimensional (2-D) wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to capture efficiently one-dimensional (1-D) discontinuities, like edges and contours, that are anisotropic and characterized by geometrical regularity along different directions. In our previous work, we proposed a construction of critically sampled perfect reconstruction anisotropic transform with directional vanishing moments (DVM) imposed in the corresponding basis functions, called directionlets. Here, we show that the computational complexity of our transform is comparable to the co…
A Variational Approach for Denoising Hyperspectral Images Corrupted by Poisson Distributed Noise
2014
Poisson distributed noise, such as photon noise is an important noise source in multi- and hyperspectral images. We propose a variational based denoising approach, that accounts the vectorial structure of a spectral image cube, as well as the poisson distributed noise. For this aim, we extend an approach for monochromatic images, by a regularisation term, that is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a Split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared to a marginal approach on synthetic and real data.
Space-Frequency Quantization using Directionlets
2007
In our previous work we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. Here, we combine the directionlets with the space-frequency quantization (SFQ) image compression method, originally based on the standard two-dimensional (2-D) wavelet transform (WT). We show that our new compression method outperforms the standard SFQ as well as the state-of-the-art compression methods, like SPIHT and JPEG-2000, in terms of the quality of compressed images, especially in a low-rate compression regime. We also show that the order of comp…
Datorzinātne un informācijas tehnoloģijas: Datu bāzes un informācijas sistēmas: doktorantu konsorcijs. Sestā Starptautiskā Baltijas konference Baltic…
2004
The Baltic Conference on Databases and Information Systems is a biannual international forum for technical discussion among researchers and developers of database and information systems. The objective of the conference is to bring together researchers as well as practitioners and PhD students in the field of computing research that will improve the construction of future information systems. On the other hand, the conference is giving opportunities to developers, users and researchers of advanced IS technologies to present their work and to exchange their ideas and at the same time providing a feedback to database community.
Financial Fragility and Interacting Units: an Exercise
2010
This paper assumes that financial fluctuations are the result of the dynamic interaction between liquidity and solvency conditions of individual financial units. The framework is designed as a heterogeneous agent model which proceeds through discrete time steps within a finite time horizon. The interaction at the microlevel between financial units and the market maker, who is in charge of clearing the market, produces interesting complex dynamics. The model is analyzed by means of numerical simulations and agent-based computational economics (ACE) approach. The behaviour and evolution of financial units are studied for different parameter regimes in order to show the importance of the param…
BELM: Bayesian Extreme Learning Machine
2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning
2016
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies n…