David Hurych

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This paper addresses object detection and tracking in high-resolution omnidirectional images. The foreseen application is a visual subsystem of a rescue robot equipped with an omnidirectional camera, which demands real time efficiency and robustness against changing viewpoint. Object detectors typically do not guarantee specific frame rate. The detection(More)
We improve an object detector based on cascade of classifiers by a local alignment of the sliding window. The detector needs to operate on a relatively sparse grid in order to achieve a real time performance on high-resolution images. The proposed local alignment in the middle of the cascade improves its recognition performance whilst retaining the(More)
BACKGROUND Real-time, quantitative PCR (qPCR) methods for detecting Rhodococcus equi in feces have been developed as a noninvasive, rapid diagnostic test for R. equi pneumonia, but have not been evaluated in a large population of foals. OBJECTIVE The objective of this study was to evaluate the clinical utility of fecal PCR as a diagnostic test for R. equi(More)
This paper shows that the successively evaluated features used in a sliding window detection process to decide about object presence/absence also contain knowledge about object deformation. We exploit these detection features to estimate the object deformation. Estimated deformation is then immediately applied to not yet evaluated features to align them(More)
Loss-of-track detection (tracking validation) and automatic tracker adaptation to new object appearances are attractive topics in computer vision. We apply very efficient learnable sequential predictors in order to address both issues. Validation is done by clustering of the sequential predictor responses. No aditional object model for validation is needed.(More)
We exploit image features multiple times in order to make sequential decision process faster and better performing. In the decision process features providing knowledge about the object presence or absence in a given detection window are successively evaluated. We show that these features also provide information about object position within the evaluated(More)
This paper summarizes results of face association experiments on real low resolution data from airport and the Labeled faces in the Wild (LFW) database. The objective of experiments is to evaluate different face alignment methods and their contribution to face association as such. The first alignment method used is Sequential Learnable Linear Predictor(More)
Linear predictors (LPs) are being used for tracking because of their computational efficiency which is better than steepest descent methods (e.g. LucasKanade). The only disadvantage of LPs is the necessary learning phase which hinders the predictors applicability as a general patch tracker. We address this limitation and propose to learn a bank of LPs(More)
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