Ivica Kopriva

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Many people are trying to be smarter every day. How's about you? There are many ways to evoke this case you can find knowledge and lesson everywhere you want. However, it will involve you to get what call as the preferred thing. When you need this kind of sources, the following book can be a great choice. kernel based algorithms for mining huge data sets(More)
Sub-band decomposition independent component analysis (SDICA) assumes that wide-band source signals can be dependent but some of their sub-components are independent. Thus, it extends applicability of standard independent component analysis (ICA) through the relaxation of the independence assumption. In this paper, firstly, we introduce novel wavelet(More)
A single-frame multichannel blind image deconvolution technique has been formulated recently as a blind source separation problem solved by independent component analysis (ICA). The attractive feature of this approach is that neither origin nor size of the spatially invariant blurring kernel has to be known. To enhance the statistical independence among the(More)
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Preface Swarm intelligence is an innovative computational way to solving hard problems. This(More)
In this paper, we investigate the application of independent component analysis (ICA) to remotely sensed hyperspectral image classification. We focus on the performance of Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm, although the proposed method is applicable to other popular ICA algorithms. The major advantage of using ICA is its(More)
A novel approach to single frame multichannel blind image deconvolution is formulated recently as non-negative matrix factorization (NMF) problem with sparseness constraint imposed on the unknown mixing vector. Unlike most of the blind image deconvolution algorithms, the NMF approach requires no a priori knowledge about the blurring kernel and original(More)
Polynomial rooting direction finding (DF) algorithms are a computationally efficient alternative to search based DF algorithms and are particularly suitable for uniform linear arrays (ULA) of physically identical elements provided mutual interaction among the array elements can be either neglected or compensated for. A popular algorithm in such situations(More)
Airborne and spaceborne remote sensors can acquire invaluable information about earth surface, which have many important applications. The acquired information usually is represented as two-dimensional grids, i.e. images. One of techniques to processing such images is Independent Component Analysis (ICA), which is particularly useful for classifying objects(More)
Unsupervised decomposition of static linear mixture model (SLMM) with ill-conditioned basis matrix and statistically dependent sources is considered. Such situation arises when low-dimensional low-intensity multi-spectral image of the tumour in the early stage of development is represented by the SLMM, wherein tumour is spectrally similar to the surrounding(More)
This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms,(More)