@conference {944, title = {On Order{\textendash}Independent Sequential Thinning}, booktitle = {IEEE International Conference on Cognitive Infocommunications (CogInfoCom)}, year = {2012}, month = {2012}, pages = {149 - 154}, publisher = {IEEE}, organization = {IEEE}, type = {Conference paper}, address = {Kosice, Slovakia }, abstract = {

The visual world composed by the human and computational cognitive systems strongly relies on shapes of objects. Skeleton is a widely applied shape feature that plays an important role in many fields of image processing, pattern recognition, and computer vision. Thinning is a frequently used, iterative object reduction strategy for skeletonization. Sequential thinning algorithms, which are based on contour tracking, delete just one border point at a time. Most of them have the disadvantage of order-dependence, i.e., for dissimilar visiting orders of object points, they may generate different skeletons. In this work, we give a survey of our results on order-independent thinning: we introduce some sequential algorithms that produce identical skeletons for any visiting orders, and we also present some sufficient conditions for the order-independence of templatebased sequential algorithms.

}, isbn = {978-1-4673-5187-4 }, doi = {10.1109/CogInfoCom.2012.6421971}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6413305}, author = {P{\'e}ter Kardos and K{\'a}lm{\'a}n Pal{\'a}gyi}, editor = {IEEE} } @inbook {1228, title = {Recovering planar homographies between 2D shapes}, booktitle = {12th International Conference on Computer Vision, ICCV 2009}, year = {2009}, note = {UT: 000294955300280ScopusID: 77953177385doi: 10.1109/ICCV.2009.5459474}, month = {2009///}, pages = {2170 - 2176}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Images taken from different views of a planar object are related by planar homography. Recovering the parameters of such transformations is a fundamental problem in computer vision with various applications. This paper proposes a novel method to estimate the parameters of a homography that aligns two binary images. It is obtained by solving a system of nonlinear equations generated by integrating linearly independent functions over the domains determined by the shapes. The advantage of the proposed solution is that it is easy to implement, less sensitive to the strength of the deformation, works without established correspondences and robust against segmentation errors. The method has been tested on synthetic as well as on real images and its efficiency has been demonstrated in the context of two different applications: alignment of hip prosthesis X-ray images and matching of traffic signs. {\textcopyright}2009 IEEE.

}, author = {Jozsef Nemeth and Csaba Domokos and Zoltan Kato}, editor = {IEEE} } @inbook {1210, title = {A multi-layer MRF model for object-motion detection in unregistered airborne image-pairs}, booktitle = {Proceedings - 14th International Conference on Image Processing, ICIP 2007}, year = {2006}, month = {2006///}, pages = {VI-141 - VI-144}, publisher = {IEEE}, organization = {IEEE}, address = {Piscataway}, url = {http://www.icip2007.org/Papers/AcceptedList.asp}, author = {Csaba Benedek and Tamas Sziranyi and Zoltan Kato and Josiane Zerubia}, editor = {IEEE} } @inbook {1234, title = {Unsupervised segmentation of color textured images using a multi-layer MRF model}, booktitle = {ICIP 2003: IEEE International Conference on Image Processing}, year = {2003}, note = {ScopusID: 0344666539doi: 10.1109/ICIP.2003.1247124}, month = {2003///}, pages = {961 - 964}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Herein, we propose a novel multi-layer Markov random field (MRF) image segmentation model which aims at combining color and texture features: Each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The model is quite generic and isn{\textquoteright}t restricted to a particular texture feature. Herein we will test the algorithm using Gabor and MRSAR texture features. Furthermore, the algorithm automatically estimates the number of classes at each layer (there can be different classes at different layers) and the associated model parameters.

}, author = {Zoltan Kato and Ting Chuen Pong and Song Guo Qiang}, editor = {IEEE} }