Michal Koperski

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Recent development in affordable depth sensors opens new possibilities in action recognition problem. Depth information improves skeleton detection, therefore many authors focused on analyzing pose for action recognition. But still skeleton detection is not robust and fail in more challenging scenarios, where sensor is placed outside of optimal working(More)
This paper presents an unsupervised approach for learning long-term human activities without requiring any user interaction (e.g., clipping long-term videos into short-term actions, labeling huge amount of short-term actions as in supervised approaches). First, important regions in the scene are learned via clustering trajectory points and the global(More)
Solotronics, optoelectronics based on solitary dopants, is an emerging field of research and technology reaching the ultimate limit of miniaturization. It aims at exploiting quantum properties of individual ions or defects embedded in a semiconductor matrix. It has already been shown that optical control of a magnetic ion spin is feasible using the carriers(More)
This paper addresses a problem of recognizing human actions in video sequences. Recent studies have shown that methods which use bag-of-features and space-time features achieve high recognition accuracy. Such methods extract both appearance-based and motion-based features. This paper focuses only on appearance features. We propose to model relationships(More)
Depth information improves skeleton detection, thus skeleton based methods are the most popular methods in RGB-D action recognition. But skeleton detection working range is limited in terms of distance and view-point. Most of the skeleton based action recognition methods ignore fact that skeleton may be missing. Local points-ofinterest (POIs) do not require(More)
Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal(More)
Histogram of Oriented Gradients is one of the most extensively used image descriptors in computer vision. It has successfully been applied to various vision tasks such as localization, classification and recognition. As it mainly captures gradient strengths in an image, it is sensitive to local variations in illumination and contrast. In the result, a(More)
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