This paper presents a data-driven, simple cluster-and-label approach using optimized count-based methods for word-level language identification for a large domain-specific multilingual diachronic corpus of periodicals published at least yearly between 1864 and 2014 in Switzerland. Our system requires no annotated data or training, only minimal human effort in evaluating and labeling 50 clusters for a corpus of almost 40 million tokens. Despite being unsupervised, our results show an accuracy that is comparable to the corpus annotations which result from an existing code switching algorithm and the combined usage of two supervised systems using character and byte n-gram models (Volk and Clematide, 2014).
An important, potential application of image-based techniques is to create photo-realistic image-based environments for interactive walkthrough. However, existing image-based studies are based on different assumptions with different focuses. There is a lack of a general framework or architecture for evaluation and development of a practical image-based system. In this paper, we propose an architecture to unify different image-based methods. Based on the architecture, we propose an image-based system to support interactive walkthrough of scalable environments. In particular, we introduce the concept of angular range, which is useful for designing a scalable configuration, recovering geometric proxy as well as rendering. We also propose a new method to recover geometry information even from outdoor scenes and a new rendering method to address the problem of abrupt visual changes in a scalable environment.
Omni-directional images are widely used in image-based walkthrough applications, in which camera pose recovery is one of the initial and important processes. Existing methods may recover camera pose of omnidirectional images from lines. However, it may not work well when there is insufficient scene structure in the images. In addition, existing methods involve non-linear optimization and iterative algorithms, which may lead to the convergence problem and high computational cost.In this paper, we propose an automatic camera pose recovery method for a network of omni-directional images. Our method only requires 2D point correspondences as input. We divide the problem into the orientation and position components, and determine them separately. The relative rotations between adjacent views are aggregated to estimate the global orientations. An algorithm is developed to adjust the rotations for avoiding global inconsistency and error accumulation. For position recovery, we have derived a linear global formulation for establishing correspondences and positions among multiple views. Globally optimized positions can be obtained simply by solving the formulation. We demonstrate the performance of our method with some experiments.
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