In photography, low depth of field (DOF) is an important technique to emphasize the object of interest (OOI) within an image. Thus, low DOF images are widely used in the application area of macro, portrait or sports photography. When viewing a low DOF image, the viewer implicitly concentrates on the regions that are sharper regions of the image and thus segments the image into regions of interest and non regions of interest which has a major impact on the perception of the image. Thus, a robust algorithm for the fully automatic detection of the OOI in low DOF images provides valuable information for subsequent image processing and image retrieval. In this paper we propose a robust and parameterless algorithm for the fully automatic segmentation of low DOF images. We compare our method with three similar methods and show the superior robustness even though our algorithm does not require any parameters to be set by hand. The experiments are conducted on a real world data set with high and low DOF images.

Abstract. Registering CT scans in a body atlas is an important technique for aligning and comparing different CT scans. It is also required for navigating automatically to certain regions of a scan or if sub volumes should be identified automatically. Common solutions to this problem employ landmark detectors and interpolation techniques. However, these solutions are often not applicable if the query scan is very small or consists only of a single slice. Therefore, the research community proposed methods being independent from landmark detectors which are using imaging techniques to register the slices in a generalized height scale. In this paper, we propose an improved prediction method for registering single slices. Our solution is based on specialized image descriptors and instance-based learning. The experimental evaluation shows that the new method improves accuracy and stability of comparable registration methods by using only a a single CT slice is required for the registration.

Abstract. Many applications require to determine the k-nearest neighbors for multiple query points simultaneously. This task is known as all-(k)-nearest-neighbor (AkNN) query. In this paper, we suggest a new method for efficient AkNN query processing which is based on spherical approximations for indexing and query set representation. In this setting, we propose trigonometric pruning which enables a significant decrease of the remaining search space for a query. Employing this new pruning method, we considerably speed up AkNN queries.

Automatically determining the relative position of a single CT slice within a full body scan provides several useful functionalities. For example, it is possible to validate DICOM meta-data information. Furthermore, knowing the relative position in a scan allows the efficient retrieval of similar slices from the same body region in other volume scans. Finally, the relative position is often an important information for a non-expert user having only access to a single CT slice of a scan. In this paper, we determine the relative position of single CT slices via instance-based regression without using any meta data. Each slice of a volume set is represented by several types of feature information that is computed from a sequence of image conversions and edge detection routines on rectangular subregions of the slices. Our new method is independent from the settings of the CT scanner and provides an average localization error of less than 4.5 cm using leave-one-out validation on a dataset of 34 annotated volume scans. Thus, we demonstrate that instance-based regression is a suitable tool for mapping single slices to a standardized coordinate system and that our algorithm is competitive to other volume-based approaches with respect to runtime and prediction quality, even though only a fraction of the input information is required in comparison to other approaches.

Boosted by a wide potential application spectrum, emotional speech recognition, i. e., the automatic computer-aided identification of human emotional states based on speech signals, currently describes a popular field of research. However, a variety of studies especially concentrating on the recognition of negative emotions often neglected the specific requirements of real-world scenarios, for example, robustness, real-time capability, and realistic speech corpora.Motivated by these facts, a robust, low-complex classification system for the detection of negative emotions in speech signals was implemented on the basis of a spontaneous, strongly emotionally colored speech corpus. Therefore, an innovative approach in the field of emotion recognition was applied as the core of the system -the bag-of-words approach that is originally known from text and image document retrieval applications. Thorough performance evaluations were carried out and a promising recognition accuracy of 65.6 % for the 2-class paradigm negative versus non-negative emotional states attests to the potential of bags-of-words in speech emotion recognition in the wild.