An extension of the Munkres algorithm for the assignment problem to rectangular matrices

Bourgeois, F.; Lassalle, J. C.

doi:10.1145/362919.362945

Cited by 434 publications

(223 citation statements)

References 2 publications

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“…The linear assignment problem is solved by an extension of the Kuhn-Munkres algorithm [16] that minimizes the cost of an assignment with the minimum cost between two matching pairs. The reference set is selected among the ones with maximum number of IMFs and has the minimal total assignment cost [13].…”

Section: Reference-based Assignment Approachmentioning

confidence: 99%

Comparative study of methods for solving the correspondence problem in EMD applications

Piper

Schiecke

Witte

et al. 2016

Current Directions in Biomedical Engineering

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Abstract:We address the correspondence problem which arises when applying empirical mode decomposition (EMD) to multi-trial and multi-subject data. EMD decomposes a signal into a set of narrow-band components named intrinsic mode functions (IMFs). The number of IMFs and their signal properties can be different between trials, channels and subjects. In order to assign IMFs with similar characteristics to each other, we compare two assignment methods, unbalanced assignment and k-cardinality assignment and two clustering algorithms, namely hierarchical clustering and density-based spatial clustering of applications with noise based on heart rate variability data of children with temporal lobe epilepsy.

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Section: Reference-based Assignment Approachmentioning

confidence: 99%

Comparative study of methods for solving the correspondence problem in EMD applications

Piper

Schiecke

Witte

et al. 2016

Current Directions in Biomedical Engineering

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“…Another solution for measuring graph similarity is the Hungarian method for solving the assignment problem [188,189], which is a polynomial time solution that operates entirely on vertex attributes. However, this method produces suboptimal graph similarities because it ignores edge attributes.…”

Section: Graph Edit Distancementioning

confidence: 99%

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

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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“…The GNN solution to this problem is the one that maximizes the number of valid assignments, while minimizing the sum of distances of the assigned pairs. To this aim, we adopt the extended Munkres' algorithm (Burgeois and Lasalle, 1971), where the input is the cost matrix D, and output are the indices (row, col) of assigned track-measurement pairs.…”

Section: Multiple People Trackingmentioning

confidence: 99%

Multi-Camera People Tracking With Hierarchical Likelihood Grids

Chen

Panin

Knoll

2011

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:In this paper, we present a grid-based tracking by detection methodology, applied to 3D people tracking for multi-camera video surveillance. In particular, frame-by-frame detection is performed by means of hierarchical likelihood grids, using edge matching through the oriented distance transform on each camera view and a simple person model, followed by likelihood grids clustering in state-space. Subsequently, the tracking module performs a global nearest neighbor data association, in order to initiate, maintain and terminate tracks automatically. The proposed system can easily include additional features, such as color or background subtraction, it can be scaled to more camera views, and it can be used to track other items as well. We demonstrate it through experiments in indoor sequences, using a calibrated multi-camera setup.

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An extension of the Munkres algorithm for the assignment problem to rectangular matrices

Cited by 434 publications

References 2 publications

Comparative study of methods for solving the correspondence problem in EMD applications

Comparative study of methods for solving the correspondence problem in EMD applications

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

Multi-Camera People Tracking With Hierarchical Likelihood Grids

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