Julian Stottinger - Academia.edu (original) (raw)

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Papers by Julian Stottinger

2008 19th International Conference on Pattern Recognition, 2008

Proceedings of the 20th Acm International Conference, Jul 1, 2012

ABSTRACT Sorting one's own private photo collection is a time consuming and tedious task.... more ABSTRACT Sorting one's own private photo collection is a time consuming and tedious task. We demonstrate our event-centered approach to perform this task fully automatically. In the course of the demonstration, we either use our own photo collections, or invite the conference visitors to bring their own cameras and photos. We will sort the photos into a semantically meaningful hierarchy for the users within a couple of minutes. Events as a media aggregator allow a user to manage and annotate a photo collection in more convenient and natural to the human being way. Based on the recognized user behavior the application is able to reveal the nature of an event and build its hierarchy with a event/sub-event relationship. One important prerequisite of our approach is a precise GPS based spatial annotation of the photos. To accommodate for devices without GPS chips or temporary low GPS perception, we propose an approach to enrich the collection with automatically estimated GPS data by semantically interpolating possible routes of the user. We are positive that we can provide a well received service for the conference visitors, especially since the conference venue will trigger a lot of memorable photos. Large scale experimental validation showed that the approach is able to recreate a user's desired hierarchy with an F-measure of about 0.8.

Proceedings of the International Conference, 2010

2010 20th International Conference on Pattern Recognition, 2010

2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009

Proceedings of the international conference on Multimedia - MM '10, 2010

Lecture Notes in Computer Science, 2008

ABSTRACT We present and evaluate an approach for finding local interest points in images based on... more ABSTRACT We present and evaluate an approach for finding local interest points in images based on the non-minima suppression of Gradient Vector Flow (GVF) magnitude. Based on the GVF’s properties it provides the approximate centers of blob-like structures or homogeneous structures confined by gradients of similar magnitude. It results in a scale and orientation invariant interest point detector, which is highly stable against noise and blur. These interest points outperform the state of the art detectors in various respects. We show that our approach gives a dense and repeatable distribution of locations that are robust against affine transformations while they outperform state of the art techniques in robustness against lighting changes, noise, rotation and scale changes. Extensive evaluation is carried out using the Mikolajcyzk framework for interest point detector evaluation.