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637 results on '"Rigoll, G."'

22. Weakly supervised cell nuclei detection and segmentation on tissue microarrays of renal clear cell carcinoma

Author

Rigoll, G, Rigoll, G ( G ), Fuchs, T J, Lange, T, Wild, P J, Moch, H, Buhmann, J M, Rigoll, G, Rigoll, G ( G ), Fuchs, T J, Lange, T, Wild, P J, Moch, H, and Buhmann, J M

Abstract

Renal cell carcinoma (RCC) is one of the ten most frequent malignancies in Western societies and can be diagnosed by histological tissue analysis. Current diagnostic rules rely on exact counts of cancerous cell nuclei which are manually counted by pathologists. We propose a complete imaging pipeline for the automated analysis of tissue microarrays of renal cell cancer. At its core, the analysis system consists of a novel weakly supervised classification method, which is based on an iterative morphological algorithm and a soft-margin support vector machine. The lack of objective ground truth labels to validate the system requires the combination of expert knowledge of pathologists. Human expert annotations of more than 2000 cell nuclei from 9 different RCC patients are used to demonstrate the superior performance of the proposed algorithm over existing cell nuclei detection approaches.

Published
2008

23. Off-the-Shelf Sensor Integration for mono-SLAM on Smart Devices

Author

Tiefenbacher, P., Schulze, T., Rigoll, G., and Lehrstuhl für Mensch-Maschine-Kommunikation

Subjects
ddc
Abstract

This work proposes a fusion of inertial measurement units (IMUs) and a visual tracking system on an embedded device. The sensor-to-sensor calibration and the pose estimation are both achieved through an unscented Kalman filter (UKF). Two approaches for a UKF-based pose estimation are presented: The first uses the estimated pose of the visual SLAM system as measurement input for the UKF; The second modifies the motion model of the visual tracking system. Our results show that IMUs increase tracking accuracy even if the visual SLAM system is untouched, while requiring little computational power. Furthermore, an accelerometer-based map scale estimation is presented and discussed.

Published
2014

41. Influences of Vehicle Exterior Images on Sound Quality Ratings: German vs. Japanese Drivers

Author

Yoshida, J., Völk, F., Fastl, H., Rigoll, G., and Lehrstuhl für Mensch-Maschine-Kommunikation

Subjects
ddc
Abstract

In this study, influences of vehicle exterior images on sound quality ratings of acceleration sounds were investigated. Luxury and sporty vehicle images were presented to German or Japanese frequent drivers, while listening to acceleration sounds. Subsequently, loudness, luxury, and sportiness of the sounds were rated. The results indicate that the German drivers tended to rate loudness lower and luxury higher when the sounds were presented with images of luxury cars, compared to images of sporty vehicles. As expected, sportiness was rated higher when an image of a sporty vehicle was presented. Oppositely the Japanese drivers indicated higher loudness and lower luxury when the sounds were combined with luxury vehicle images. Further, comparable to the German drivers, they rated higher sportiness when presented with sporty vehicle images. Consequently, it appears that sound quality ratings can be affected by the vehicle exterior design, but the average tendencies suggest different effect directions for German vs. Japanese drivers.

Published
2013

44. A Clustering-Based Approach for Evaluation of EO Image Indexing, Sensors and Models in Photogrammetry and Remote Sensing

Author

Bahmanyar, R., Rigoll, G., Datcu, Mihai, Arefi, H., Sharifi, A., Reinartz, Peter, and Delavar, M.R.

Subjects
Remote Sensing, Photogrammetrie und Bildanalyse, Photogrammetry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

The volume of Earth Observation data is increasing immensely in order of several Terabytes a day. Therefore, to explore and investigate the content of this huge amount of data, developing more sophisticated Content-Based Information Retrieval (CBIR) systems are highly demanded. These systems should be able to not only discover unknown structures behind the data, but also provide relevant results to the users’ queries. Since in any retrieval system the images are processed based on a discrete set of their features (i.e., feature descriptors), study and assessment of the structure of feature space, build by different feature descriptors, is of high importance. In this paper, we introduce a clustering-based approach to study the content of image collections. In our approach, we claim that using both internal and external evaluation of clusters for different feature descriptors, helps to understand the structure of feature space. Moreover, the semantic understanding of users about the images also can be assessed. To validate the performance of our approach, we used an annotated Synthetic Aperture Radar (SAR) image collection. Quantitative results besides the visualization of feature space demonstrate the applicability of our approach.

Published
2013

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