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Kovacs L.,Distributed Events Analysis Research Laboratory
Proceedings - International Workshop on Content-Based Multimedia Indexing | Year: 2013

This paper presents a method towards easier evaluation of a large number of different image/video content descriptors, by using a multiple descriptor-tree based parallel indexing scheme instead of classical index structures with high dimensional multi-feature vectors. We will show that the proposed scheme is flexible and easily extensible, and it is not just faster to build, but provides good retrieval precision as well. The primary goal is to provide a flexible and modular indexing scheme for descriptor evaluation and feature selection purposes, but it can be used for generic content-based retrieval tasks as well. © 2013 IEEE.

Barath D.,Distributed Events Analysis Research Laboratory | Barath D.,Eotvos Lorand University | Molnar J.,Hungarian Academy of Sciences | Hajder L.,Distributed Events Analysis Research Laboratory
Communications in Computer and Information Science | Year: 2016

The aim of this paper is to describe different estimation techniques in order to deal with point-wise surface normal estimation from calibrated stereo configuration. We show here that the knowledge of the affine transformation between two projections is sufficient for computing the normal vector unequivocally. The formula which describes the relationship among the cameras, normal vectors and affine transformations is general, since it works for every kind of cameras, not only for the pinhole one. However, as it is proved in this study, the normal estimation can optimally be solved for the perspective camera. Other non-optimal solutions are also proposed for the problem. The methods are tested both on synthesized data and real-world images. The source codes of the discussed algorithms are available on the web. © Springer International Publishing Switzerland 2016.

Sziranyi T.,Distributed Events Analysis Research Laboratory | Shadaydeh M.,Distributed Events Analysis Research Laboratory
Proceedings - International Workshop on Content-Based Multimedia Indexing | Year: 2013

Classifying segments and detection of changes in terrestrial areas are important and time-consuming efforts for remote-sensing image repositories. Some country areas are scanned frequently (e.g. year-by-year) to spot relevant changes, and several repositories contain multi-temporal image samples for the same area in very different quality and details. We propose a Multi-Layer Markovian adaptive fusion on Luv color images and similarity measure for the segmentation and detection of changes in a series of remote sensing images. We aim the problem of detecting details in rarely scanned remote sensing areas, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering based on a cross-image featuring, followed by multilayer MRF segmentation in the mixed dimensionality. On the base of the fused segmentation, the clusters of the single layers are trained by clusters of the mixed results. The improvement of this (partly) unsupervised method has been validated on remotely sensed image series. © 2013 IEEE.

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