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Sparse coding seeks for over-complete bases to obtain the high-level image representation for image analysis. In many applications, the image data might reside on a low dimensional manifold embedded in high dimensional ambient space. However, standard sparse coding cannot exploit the manifold structure. In this paper, we propose a novel structured sparse coding method based on the L1-graph, in which...
Numerous geometric problems in computer vision involve the solution of systems of polynomial equations. This is true for problems with minimal information, but also for finding stationary points for overdetermined problems. The state-of-the-art is based on the use of numerical linear algebra on the large but sparse coefficient matrix that represents the expanded original equation set. In this paper...
Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with...
This paper proposes a novel automated approach for the categorization of cells in fluorescence microscopy images. Our supervised classification method aims at recognizing patterns of unlabeled cells based on an annotated dataset. First, the cell images need to be indexed by encoding them in a feature space. For this purpose, we propose tailored bio-inspired features relying on the distribution of...
This paper presents a practical method for pool-based active learning that is robust to annotation noise. Our work is inspired by recent approaches to active learning in two different noise-free settings: importance-weighted methods for streams and unbiased pool-based techniques. In our proposed method, we employ an ensemble of classifiers to guide the label requests from a pool of unlabeled training...
Prevention of genocide is one of the most important challenges before the international community. In this paper we apply recent machine learning techniques to forecast the onset of political instability and genocide. Specifically, we employ sparse additive models which are both flexible and maintain interpretability of the results. Our model demonstrates a reasonable degree of forecasting performance...
This paper exploits the problem of fitting special forms of annuli that correspond to 4-connected digital circles to a given set of points in 2D images in the presence of noise by maximizing the number of inliers, namely the consensus set. We prove that the optimal solutions can be described by solutions with three points on the annulus boundary. These solutions correspond to vertices of the preimage...
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