01390nas a2200157 4500008004100000020002300041245004700064210004400111260003400155300001400189520091000203100001901113700002301132700000501155856007201160 2012 eng d a978-1-4673-5187-4 00aOn Order–Independent Sequential Thinning0 aOrder–Independent Sequential Thinning aKosice, Slovakia bIEEEc2012 a149 - 1543 a

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.

1 aKardos, Péter1 aPalágyi, Kálmán1 a uhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=641330501265nas a2200109 4500008004100000245005300041210005300094260001800147300001600165520092800181856004601109 2009 eng d00aRecovering planar homographies between 2D shapes0 aRecovering planar homographies between 2D shapes bIEEEc2009/// a2170 - 21763 a

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. ©2009 IEEE.

 uhttps://home.inf.u-szeged.hu/en/node/122800403nas a2200097 4500008004100000245009300041210006900134260003000203300002000233856005200253 2006 eng d00aA multi-layer MRF model for object-motion detection in unregistered airborne image-pairs0 amultilayer MRF model for objectmotion detection in unregistered  aPiscatawaybIEEEc2006/// aVI-141 - VI-144 uhttp://www.icip2007.org/Papers/AcceptedList.asp01171nas a2200109 4500008004100000245008500041210006900126260001800195300001400213520078800227856004601015 2003 eng d00aUnsupervised segmentation of color textured images using a multi-layer MRF model0 aUnsupervised segmentation of color textured images using a multi bIEEEc2003/// a961 - 9643 a

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'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.

 uhttps://home.inf.u-szeged.hu/en/node/1234