A robust FLIR target detection employing an auto-convergent pulse coupled neural network

© 2019 Informa UK Limited, trading as Taylor & Francis Group. Automatic target detection (ATD) of a small target along with its true shape from highly cluttered forward-looking infrared (FLIR) imagery is crucial. FLIR imagery is low contrast in nature, which makes it difficult to discriminate the target from its immediate background. Here, pulse-coupled neural network (PCNN) is extended with auto-convergent criteria to provide an efficient ATD tool. The proposed auto-convergent PCNN (AC-PCNN) segments the target from its background in an adaptive manner to identify the target region when the target is camouflaged or contains higher visual clutter. Then, selection of region of interest followed by template matching is augmented to capture the accurate shape of a target in a real scenario. The outcomes of the proposed method are validated through well-known statistical methods and found superior performance over other conventional methods

Segmentation d'images par maximisation de l'entropie à deux dimensions basée sur le recuit microcanonique

Dans cet article, nous présentons une nouvelle méthode de segmentation d'images par analyse d'histogramme et seuillage par maximisation de l'entropie à deux dimensions. Pour remédier au défaut des algorithmes classiques, qui peuvent s'arrêter au premier maximum d'entropie rencontré, nous mettons en œuvre une métaheuristique robuste et facile à programmer, basée sur le recuit microcanonique. Dans l'espace exploré, la recherche des seuils de segmentation optimums s'effectue par paliers d'énergie décroissante en gravitant autour des meilleures solutions candidates. Les temps de convergence s'en trouvent améliorés et la reproductibilité des résultats est mieux garantie. L'algorithme est testé sur des images microscopiques biomédicales. Les résultats obtenus sont comparés à ceux de la méthode de Canny

Stochastic optimization methods for extracting cosmological parameters from CMBR power spectra

The reconstruction of the CMBR power spectrum from a map represents a major computational challenge to which much effort has been applied. However, once the power spectrum has been recovered there still remains the problem of extracting cosmological parameters from it. Doing this involves optimizing a complicated function in a many dimensional parameter space. Therefore efficient algorithms are necessary in order to make this feasible. We have tested several different types of algorithms and found that the technique known as simulated annealing is very effective for this purpose. It is shown that simulated annealing is able to extract the correct cosmological parameters from a set of simulated power spectra, but even with such fast optimization algorithms, a substantial computational effort is needed.Comment: 7 pages revtex, 3 figures, to appear in PR

Simplified tabu search with random-based searches for bound constrained global optimization

This paper proposes a simplified version of the tabu search algorithm that solely uses randomly generated direction vectors in the exploration and intensification search procedures, in order to define a set of trial points while searching in the neighborhood of a given point. In the diversification procedure, points that are inside any already visited region with a relative small visited frequency may be accepted, apart from those that are outside the visited regions. The produced numerical results show the robustness of the proposed method. Its efficiency when compared to other known metaheuristics available in the literature is encouraging.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00013/2020); FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM

Boosting content based image retrieval performance through integration of parametric & nonparametric approaches

© 2018 Elsevier Inc. The collection of digital images is growing at ever-increasing rate which rises the interest of mining the embedded information. The appropriate representation of an image is inconceivable by a single feature. Thus, the research addresses that point for content based image retrieval (CBIR) by fusing parametric color and shape features with nonparametric texture feature. The color moments, and moment invariants which are parametric methods and applied to describe color distribution and shapes of an image. The nonparametric ranklet transformation is performed to narrate the texture features. Experimentally these parametric and nonparametric features are integrated to propose a robust and effective algorithm. The proposed work is compared with seven existing techniques by determining statistical metrics across five image databases. Finally, a hypothesis test is carried out to establish the significance of the proposed work which, infers evaluated precision and recall values are true and accepted for the all image database

Some contributions to the adaptation of discrete metaheuristics for continuous optimization

International audienceno abstrac

Optimization in Signal and Image Processing- Digital, Signal and image Processing Series

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Métaheuristiques

International audienceno abstrac

Some contributions to the adaptation of discrete metaheuristics for continuous optimization

International audienceno abstrac

Metaheuristics for image segmentation

International audienceno abstrac