Author
Stanislav Kovačič
Bio: Stanislav Kovačič is an academic researcher from University of Ljubljana. The author has contributed to research in topics: Image registration & Motion estimation. The author has an hindex of 25, co-authored 60 publications receiving 1847 citations.
Papers published on a yearly basis
Papers
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TL;DR: A probabilistic play model is applied to the player-trajectory data in order to segment the play into game phases (offense, defense, time out) and the activity is recognized by comparing its semantic description with the descriptions of manually defined templates, stored in a database.
142 citations
TL;DR: A novel measure of camera focus based on the Bayes spectral entropy of an image spectrum, which outperformed the reference measures by exhibiting a wider working range and a smaller failure rate.
113 citations
TL;DR: The baseline of the approach consists of sacrificing much of the spatial accuracy and temporal resolution of widely used biomechanical measurement systems, to obtain data on human movement that span large areas and long intervals of time.
112 citations
TL;DR: An original non-rigid image registration approach, which tends to improve the registration by establishing a symmetric image interdependence by measuring the image similarity in both registration directions.
100 citations
TL;DR: A new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured on board a USV, and outperforms the related approaches, while requiring a fraction of computational effort.
Abstract: Obstacle detection plays an important role in unmanned surface vehicles (USVs). The USVs operate in a highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken on board. This paper addresses the problem of online detection by constrained, unsupervised segmentation. To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured on board a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and comfortably runs in real time. The algorithm is tested on a new, challenging, dataset for segmentation, and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.
92 citations
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TL;DR: This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
4,233 citations
TL;DR: This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling, and to quantify the similarity of templates derived from different subgroups.
3,491 citations
TL;DR: This survey reviews recent trends in video-based human capture and analysis, as well as discussing open problems for future research to achieve automatic visual analysis of human movement.
2,738 citations
Columbia University1, University of Oxford2, Nathan Kline Institute for Psychiatric Research3, New York University4, University College London5, University of Pennsylvania6, University of California, Los Angeles7, University of Iowa8, McGill University9, French Institute of Health and Medical Research10, French Institute for Research in Computer Science and Automation11, John Radcliffe Hospital12, Imperial College London13, Mauna Kea Technologies14
TL;DR: This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted and suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols.
2,214 citations
08 Oct 2016
TL;DR: A new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error are presented to help accelerate progress in multi-target, multi-camera tracking systems.
Abstract: To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.
1,775 citations