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1.
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2.
  • Andersson, Thord, et al. (författare)
  • A Fast Optimization Method for Level Set Segmentation
  • 2009
  • Ingår i: Image Analysis : 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings. - Springer Berlin/Heidelberg. - 978-3-642-02229-6 (print) - 978-3-642-02230-2 (online) ; s. 400-409
  • Konferensbidrag (refereegranskat)abstract
    • Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.
3.
  • Andersson, Thord, et al. (författare)
  • Modified Gradient Search for Level Set Based Image Segmentation
  • 2013
  • Ingår i: IEEE Transactions on Image Processing. - IEEE Signal Processing Society. - 1057-7149. ; 22:2, s. 621-630
  • Tidskriftsartikel (refereegranskat)abstract
    • Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.
4.
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5.
  • Artificial Neural Networks in Medicine and Biology
  • 2000
  • Samlingsverk (redaktörskap) (övrigt vetenskapligt)abstract
    • This book contains the proceedings of ANNIMAB-1, the first international conference on artificial neural networks in medicine and biology. Comprising a selection of papers from leading researchers in the field, it summarises the state-of-the-art, analyses the relationship between ANN techniques and other available methods and points to possible future biomedical and medical uses of ANNs.
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6.
  • Borga, Magnus, et al. (författare)
  • A Binary Competition Tree for Reinforcement Learning
  • 1994
  • Rapport (övrigt vetenskapligt)abstract
    • A robust, general and computationally simple reinforcement learning system is presented. It uses a channel representation which is robust and continuous. The accumulated knowledge is represented as a reward prediction function in the outer product space of the input- and output channel vectors. Each computational unit generates an output simply by a vector-matrix multiplication and the response can therefore be calculated fast. The response and a prediction of the reward are calculated simultaneously by the same system, which makes TD-methods easy to implement if needed. Several units can cooperate to solve more complicated problems. A dynamic tree structure of linear units is grown in order to divide the knowledge space into a sufficiently number of regions in which the reward function can be properly described. The tree continuously tests split- and prune criteria in order to adapt its size to the complexity of the problem.
7.
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8.
  • Borga, Magnus, 1965-, et al. (författare)
  • A canonical correlation approach to exploratory data analysis in fMRI
  • 2002
  • Konferensbidrag (övrigt vetenskapligt)abstract
    • A computationally efficient data-driven method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The proposed method is more robust and much more computationally efficient than independent component analysis, which previously has been applied in fMRI.
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9.
  • Borga, Magnus, et al. (författare)
  • A Survey of Current Techniques for Reinforcement Learning
  • 1992
  • Rapport (övrigt vetenskapligt)abstract
    • This survey considers response generating systems that improve their behaviour using reinforcement learning. The difference between unsupervised learning, supervised learning, and reinforcement learning is described. Two general problems concerning learning systems are presented; the credit assignment problem and the problem of perceptual aliasing. Notations and some general issues concerning reinforcement learning systems are presented. Reinforcement learning systems are further divided into two main classes; memory mapping and projective mapping systems. Each of these classes is described and some examples are presented. Some other approaches are mentioned that do not fit into the two main classes. Finally some issues not covered by the surveyed articles are discussed, and some comments on the subject are made.
10.
  • Borga, Magnus, et al. (författare)
  • A Unified Approach to PCA, PLS, MLR and CCA
  • 1997
  • Rapport (övrigt vetenskapligt)abstract
    • This paper presents a novel algorithm for analysis of stochastic processes. The algorithm can be used to find the required solutions in the cases of principal component analysis (PCA), partial least squares (PLS), canonical correlation analysis (CCA) or multiple linear regression (MLR). The algorithm is iterative and sequential in its structure and uses on-line stochastic approximation to reach an equilibrium point. A quotient between two quadratic forms is used as an energy function and it is shown that the equilibrium points constitute solutions to the generalized eigenproblem.
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