Browsing by Author "Hauptmann, A."

Now showing 1 - 4 of 4

Open Access
Comparison and combination of two novel commercial detection methods
(IEEE, 2004-06) Duygulu, Pınar; Chen, M.-Y.; Hauptmann, A.
Detection and removal of commercials plays an important role when searching for important broadcast news video material. In this study, two novel approaches are proposed based on two distinctive characteristics of commercials, namely, repetitive use of commercials over time and distinctive color and audio features. Furthermore, proposed strategies for combining the results of the two methods yield even better performance. Experiments show over 90% recall and precision on a test set of 5 hours of ABC and CNN broadcast news data.
Open Access
Smart computing for large scale visual data sensing and processing
(Elsevier, 2016) Zhang, L.; Duygulu, P.; Zuo, W.; Shan, S.; Hauptmann, A.
Open Access
Snippet based trajectory statistics histograms for assistive technologies
(Springer, 2014-09) İscen, Ahmet; Wang Y.; Duygulu, Pınar; Hauptmann, A.
Due to increasing hospital costs and traveling time, more and more patients decide to use medical devices at home without traveling to the hospital. However, these devices are not always very straight-forward for usage, and the recent reports show that there are many injuries and even deaths caused by the wrong use of these devices. Since human supervision during every usage is impractical, there is a need for computer vision systems that would recognize actions and detect if the patient has done something wrong. In this paper, we propose to use Snippet Based Trajectory Statistics Histograms descriptor to recognize actions in two medical device usage problems; inhaler device usage and infusion pump usage. Snippet Based Trajectory Statistics Histograms encodes the motion and position statistics of densely extracted trajectories from a video. Our experiments show that by using Snippet Based Trajectory Statistics Histograms technique, we improve the overall performance for both tasks. Additionally, this method does not require heavy computation, and is suitable for real-time systems. © Springer International Publishing Switzerland 2015.
Open Access
What's news, what's not? Associating news videos with words
(Springer, 2004) Duygulu, P.; Hauptmann, A.
Text retrieval from broadcast news video is unsatisfactory, because a transcript word frequently does not directly 'describe' the shot when it was spoken. Extending the retrieved region to a window around the matching keyword provides better recall, but low precision. We improve on text retrieval using the following approach: First we segment the visual stream into coherent story-like units, using a set of visual news story delimiters. After filtering out clearly irrelevant classes of shots, we are still left with an ambiguity of how words in the transcript relate to the visual content in the remaining shots of the story. Using a limited set of visual features at different semantic levels ranging from color histograms, to faces, cars, and outdoors, an association matrix captures the correlation of these visual features to specific transcript words. This matrix is then refined using an EM approach. Preliminary results show that this approach has the potential to significantly improve retrieval performance from text queries. © Springer-Verlag 2004.