Titlebook: Image Analysis; 18th Scandinavian Co Joni-Kristian Kämäräinen,Markus Koskela Conference proceedings 2013 Springer-Verlag GmbH Germany, part

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Cascaded Random Forest for Fast Object Detectionis paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massiv

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Multiplicative Updates for Learning with Stochastic Matricesnt semantic analysis, etc. In such learning problems, the learned matrices, being stochastic matrices, are non-negative and all or part of the elements sum up to one. Conventional multiplicative updates which have been widely used for nonnegative learning cannot accommodate the stochasticity constra

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Continuous-Space Gaussian Process Regression and Generalized Wiener Filtering with Application to Les model. We study abstract continuous-space Gaussian regression problems where the training set covers the whole input space instead of consisting of a finite number of distinct points. The model can be used for analyzing theoretical properties of Gaussian process regressors. In this paper, we prese

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Approximations of Gaussian Process Uncertainties for Visual Recognition Problemsn result. This is especially useful to select informative samples in active learning and to spot samples of previously unseen classes known as novelty detection. However, the Gaussian process framework suffers from high computational complexity leading to computation times too large for practical ap

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Topology-Preserving Dimension-Reduction Methods for Image Pattern Recognitiontern recognition uses pattern recognition techniques for the classification of image data. For the numerical achievement of image pattern recognition techniques, images are sampled using an array of pixels. This sampling procedure derives vectors in a higher-dimensional metric space from image patte

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Texture Description with Completed Local Quantized Patternsses random initialization in vector quantization, this leads to losing the distribution of local patterns and costing much computational time. For reducing the unnecessary computational time of initialization, we use preselected dominant patterns as the initialization. Our experimental results show

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