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Standard learning techniques can be difficult to apply in a setting where instances are sets of features, varying in cardinality and with additional geometric structure. Kernel-based classification methods can be effective in this situation as they avoid explicitly representing the instances. We describe a kernel function which attempts to establish correspondences between local features while also...
Inspired by the image de-noising techniques using learned dictionaries and sparse representation, we present a fabric defect detection scheme via sparse dictionary reconstruction. Fabric defects can be regarded as local anomalies against the relatively homogeneous texture background. Following from the flexibility of sparse representation, normal fabric samples can be efficiently represented using...
Cell tracking is a crucial component of many biomedical image analysis applications. Many available cell tracking systems assume high precision of the cell detection module. Therefore low performance in cell detection can heavily affect the tracking results. Unfortunately cell segmentation modules often have significant errors, especially in the case of phase-contrast imaging. In this paper we propose...
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...
Cell segmentation is a crucial step in many bio-medical image analysis applications and it can be considered as an important part of a tracking system. Segmentation in phase-contrast images is a challenging task since in this imaging technique, the background intensity is approximately similar to the cell pixel intensity. In this paper we propose an interactive automatic pixel level segmentation algorithm,...
We present a new efficient algorithm for maximizing energy functions with higher order potentials suitable for MAP inference in discrete MRFs. Initially we relax integer constraints on the problem and obtain potential label assignments using higher-order (tensor) power method. Then we utilise an ascent procedure similar to the classic ICM algorithm to converge to a solution meeting the original integer...
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