Abstract: One of the most important and challenging tasks in bio-medical image analysis is the localization, identification, and discrimination of salient objects or structures. While to date human experts are performing these tasks manually at the expense of time and reliability, methods for automation of these processes are evidently called for. This paper outlines a novel technique for geometric clustering of related object evidence called regularized geometric hulls (RGH) and gives three exemplary real-world application scenarios. Several experiments performed on real-world data highlight a set of useful advantages, such as robustness, reliability, as well as efficient runtime behavior.

Abstract: Temporal segmentation of videos into meaningful image sequences containing some particular activities is an interesting problem in computer vision. We present a novel algorithm to achieve this semantic video segmentation. The segmentation task is accomplished through event detection in a frame-by-frame processing setup. We propose using one-class classification (OCC) techniques to detect events that indicate a new segment, since they have been proved to be successful in object classification and they allow for unsupervised event detection in a natural way. Various OCC schemes have been tested and compared, and additionally, an approach based on the temporal self-similarity maps (TSSMs) is also presented. The testing was done on a challenging publicly available thermal video dataset. The results are promising and show the suitability of our approaches for the task of temporal video segmentation.

Abstract: In various real-world applications of distributed and multi-view vision systems, ability to learn unseen actions in an online fashion is paramount, as most of the actions are not known or sufficient training data is not available at design time. We propose a novel approach which combines the unsupervised learning capabilities of Hierarchical Dirichlet Processes (HDP) with Temporal Self-Similarity Maps (SSM) representations, which have been shown to be suitable for aggregating multi-view information without further model knowledge. Furthermore, the HDP model, being almost completely data-driven, provides us with a system that works almost ``out-of-the-box''. Various experiments performed on the extensive JAR-Aibo dataset show promising results, with clustering accuracies up to 60% for a 56-class problem.