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Classified 3d Model Retrieval Based on Cascaded Fusion of Local Descriptors

Konstantinos Mochament
2016 International Journal of Computer Graphics & Animation  

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https://web.archive.org/web/20180604045556/http://www.aircconline.com/ijcga/V6N1/6116ijcga02.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL. The file type is application/pdf.

One of the core tasks in order to perform fast and accurate retrieval results in a content-based search and retrieval 3D system is to determine an efficient and effective method for matching similarities between the 3D models. In this paper the "cascaded fusion of local descriptors" is proposed for efficient retrieval of classified 3D models, based on a 2D coloured logo retrieval methodological approach, suitably modified for the purpose of 3D search and retrieval tasks that are widely used in
more » ... he augmented reality (AR) and virtual reality (VR) fields. Initially, features from Key points are extracted using different state of the art local descriptor algorithms and then they are joined to constitute the feature tuple for the respective key point. Additionally, a feature vocabulary for each descriptor is created that maps those tuples to the respective vocabularies using distance functions that applied among the newly created tuples of each Point Cloud. Subsequently, an inverted index table is formed that maps the 3D models to each tuple respectively. Therefore, for every query 3D model only the corresponding 3D models are retrieved as these were previously mapped in the inverted index table. Finally, from the retrieved list by comparing the local features frequency of appearance to the first vocabulary, the final re ranked list of the most similar 3D models is produced.
doi:10.5121/ijcga.2016.6102 fatcat:kyhcmfgnifbufpwdgthqsmplwi
Citation

Konstantinos Mochament. "Classified 3d Model Retrieval Based on Cascaded Fusion of Local Descriptors." International Journal of Computer Graphics & Animation 6.1 (2016) 11-21

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia

Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis, Lesley-Ann Sutton, Xiao-Jie Yan, Vasilis Bikos, Anna Vardi, Konstantinos Mochament, Nicholas Chiorazzi, Chrysoula Belessi, Richard Rosenquist, Paolo Ghia (+7 others)
2018 BMC Bioinformatics  

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https://web.archive.org/web/20190427091237/http://orbit.dtu.dk/files/161891707/10.1186_2Fs12859_018_2381_1.pdf

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Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the
more » ... y of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. Results: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. Conclusions: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.
doi:10.1186/s12859-018-2381-1 fatcat:7mnozo63kbbffhyw2m2exuhfau
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Citation

Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis, Lesley-Ann Sutton, Xiao-Jie Yan, Vasilis Bikos, Anna Vardi, Konstantinos Mochament, Nicholas Chiorazzi, Chrysoula Belessi, Richard Rosenquist, Paolo Ghia, Kostas Stamatopoulos, Panayiotis Vlamos, Anna Chailyan, Nanna Overby, Paolo Marcatili, Anastasia Hatzidimitriou, Dimitrios Tzovaras. "Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia." BMC Bioinformatics 19.S14 (2018)