Real-time multitarget tracking for sensor-based sorting – A new implementation of the auction algorithm for graphics processing units

Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes

Operating system support to an online hardware-software co-design scheduler for heterogeneous multicore architectures

This paper aims at designing and implementing a\ud scheduler model for heterogeneous multiprocessor architectures\ud based on software and hardware. As a proof of concept, the\ud scheduler model was applied to the Linux operating system running\ud on the SPARe Leon3 processor. In this sense, performance\ud monitors have been implemented within the processors, which\ud identify demands of processes in real-time. For each process, its\ud demand is projected for the other processors in the architecture\ud and then, it is performed a balancing to maximize the total system\ud performance by distributing processes among processors. The\ud Hungarian maximization algorithm, used in balancing scheduler\ud was developed in hardware, and provides greater parallelism and\ud performance in the execution of the algorithm. The scheduler\ud has been validated through the parallel execution of several\ud benchmarks, resulting in decreased execution times compared\ud to the scheduler without the heterogeneity support

A graph-based approach for the retrieval of multi-modality medical images

Medical imaging has revolutionised modern medicine and is now an integral aspect of diagnosis and patient monitoring. The development of new imaging devices for a wide variety of clinical cases has spurred an increase in the data volume acquired in hospitals. These large data collections offer opportunities for search-based applications in evidence-based diagnosis, education, and biomedical research. However, conventional search methods that operate upon manual annotations are not feasible for this data volume. Content-based image retrieval (CBIR) is an image search technique that uses automatically derived visual features as search criteria and has demonstrable clinical benefits. However, very few studies have investigated the CBIR of multi-modality medical images, which are making a monumental impact in healthcare, e.g., combined positron emission tomography and computed tomography (PET-CT) for cancer diagnosis. In this thesis, we propose a new graph-based method for the CBIR of multi-modality medical images. We derive a graph representation that emphasises the spatial relationships between modalities by structurally constraining the graph based on image features, e.g., spatial proximity of tumours and organs. We also introduce a graph similarity calculation algorithm that prioritises the relationships between tumours and related organs. To enable effective human interpretation of retrieved multi-modality images, we also present a user interface that displays graph abstractions alongside complex multi-modality images. Our results demonstrated that our method achieved a high precision when retrieving images on the basis of tumour location within organs. The evaluation of our proposed UI design by user surveys revealed that it improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The work in this thesis advances the state-of-the-art by enabling a novel approach for the retrieval of multi-modality medical images

Bildfolgenbasierte Gewinnung und Nutzung partikelindividueller Bewegungsinformation in der optischen Schüttgutsortierung

Die sensorgestützte Sortierung ermöglicht die Trennung einzelner Partikel aus einem Materialstrom. In dieser Arbeit wird eine neue Gattung eines Schüttgutsortiersystems mit Flächenkamera erforscht. Der Einsatz von Hochgeschwindigkeitskameras als Inspektionssensorik wirft aus Sicht der Informatik spannende Forschungsfragen hinsichtlich der Gewinnung und Nutzung weitergehender Merkmale, insbesondere von Bewegungsinformation über zu sortierende Materialien, auf