József Németh

Nonlinear Shape Registration without Correspondences

In this paper, we propose a novel framework to estimate the parameters of a diffeomorphism that aligns a known shape and its distorted observation. Classical registration methods first establish correspondences between the shapes and then compute the transformation parameters from these landmarks. Herein, we trace back the problem to the solution of a system of nonlinear equations which directly gives the parameters of the aligning transformation. The proposed method provides a generic framework to recover any diffeomorphic deformation without established correspondences. It is easy to implement, not sensitive to the strength of the deformation, and robust against segmentation errors. The method has been applied to several commonly used transformation models. The performance of the proposed framework has been demonstrated on large synthetic data sets as well as in the context of various applications.

Recovering planar homographies between 2D shapes

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.

Stimulated Gastrointestinal Hormone Release and Gallbladder Contraction During Continuous Jejunal Feeding in Patients with Pancreatic Pseudocyst Is Inhibited by Octreotide

SummaryBackground. Continuous enteral feeding, the old-new therapeutic modality in the treatment of patients with acute pancreatitis and those with complications is considered to bypass the cephalic, the gastric, and (at least in part) the intestinal phase of pancreatic secretion. The aim of this study was to test the GI hormonal changes and gallbladder motility during CJF in patients with pancreatic pseudocysts following acute pancreatitis, with or without octreotide pretreatment.Patients and Methods. In 15 patients with pancreatic pseudocysts, an 8-French (8F) nasojejunal catheter was positioned into the jejunum distal to the ligament of Treitz during duodenoscopy. On test d 1, blood samples were taken for CCK, gastrin, insulin-like immunoreactivity (IRI), glucagon, and glucose measurements prior to and at 20, 40, 60, and 120 min following jejunal saline infusion at a rate of 2 mL/min. The gallbladder volumes were determined simultaneously by ultrasonography. On test d 2, CJF (175 kcal/h) was started by the same route and at the same infusion rate. Analogous measurements were performed as indicated above. On test d 3, 100 µg of octreotide was administered subcutaneously and the previous procedure was repeated. The plasma level of CCK and glucagon and the serum levels of IRI and gastrin were determined by bioassay and radioimmunoassay (RIA), respectively.Results. Significant changes in hormone levels were not observed during jejunal saline perfusion. However, the levels of CCK (5.7±0.9 pmol), gastrin (10.6±1.3 pmol/L), IRI (27.2±5.8 µIU/mL), glucagon (322.8±32.4 pg/mL), and glucose (5.8±1.0 mmol/L) were significantly increased at 20 min during CJF vs the saline controls (2.0±0.3 pmol, 6.8±1.1 pmol/L, 7.8±0.4 µIU/mL, 172.8±33.4 pg/mL, and 4.5±0.5 mmol/L, respectively) and remained elevated at 40, 60, and 120 min. Octreotide pretreatment eliminated the increases in CCK, gastrin, IRI, and glucagon levels observed during CJF alone. The significant decrease in gallbladder volume during CJF was also prevented by octreotide pretreatment. Conclusion. Continuous jejunal feeding (CJF) elicited significant increases in gastrointestinal (GI) regulatory hormone (cholecystokinin [CCK], gastrin, IRI, and glucagon) levels and evoked a consecutive gallbladder contraction. These biological responses are eliminated by octreotide pretreatment. Further clinical studies are needed to assess the eventual therapeutic effect of octreotide during CJF in patients with pancreatic pseudocyst.

Time-dependent network algorithm for ranking in sports

In this paper a novel ranking method which may be useful in sports like tennis, table tennis or American football, etc. is introduced and analyzed. In order to rank the players or teams, a time-dependent PageRank based method is applied on the directed and weighted graph representing game results in a sport competition. The method was examined on the results of the table tennis competition of enthusiastic sport-loving researchers of the Institute of Informatics at the University of Szeged. The results of our method were compared by several popular ranking techniques. We observed that our approach works well in general and it has a good predictive power.

A multi-layer 'Gas of circles' markov random field model for the extraction of overlapping near-circular objects

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images.

Nonlinear registration of binary shapes

A novel approach is proposed to estimate the parameters of a diffeomorphism that aligns two binary images. Classical approaches usually define a cost function based on a similarity metric and then find the solution via optimization. Herein, we trace back the problem to the solution of a system of nonlinear equations which directly provides the parameters of the aligning transformation. The proposed method works without any time consuming optimization step or established correspondences. The advantage of our algorithm is that it is easy to implement, less sensitive to the strength of the deformation, and robust against segmentation errors. The efficiency of the proposed approach has been demonstrated on a large synthetic dataset as well as in the context of an industrial application.

Discrete Tomography with Unknown Intensity Levels Using Higher-Order Statistics

Discrete tomography focuses on the reconstruction of images containing only a limited number of different intensity values. Most of the methods assume that the intensities are a priori known. In practice, however, this information is usually not available. Therefore, the problem of the estimation of the intensity levels has been recently addressed by many researchers. In this paper, we present a novel approach for the tomographic reconstruction of binary images, when the two gray-levels are unknown. The problem is traced back to the minimization of an appropriate objective functional, in which a higher-order statistics-based discretization term enforces binary solutions. Instead of the gray-levels, the only parameter of this term is their mid-level value, which is iteratively approximated during the optimization process. Experiments on synthetic phantom images as well as on real data show that the proposed graduated optimization scheme can efficiently minimize the objective functional and the method provides accurate reconstructions. Compared to some of the state-of-the-art algorithms, the proposed method provides competitive results, while it requires less parameter settings, thus it can be considered as a valid alternative.

A new model for the linear 1-dimensional online clustering problem

Abstract In this study, a mathematical model is presented for an online data clustering problem. Data clustering plays an important role in many applications like handling the data acknowledgment problem and data stream management in real-time locating systems. The inputs in these problems are data sequences, each containing several data elements. Each data element has an arrival time and a weight that reflects its importance. The arrival times are not known in advance, and some data elements never arrive. Hence the system should decide which moment is optimal for forwarding the collected data for processing. This requires finding a good trade-off between the amount of collected information and the waiting time, which may be regarded as a minimization problem. Here, we investigate several online algorithms and present their competitive analysis and average case studies. Experimental results, based on simulations using artificially generated data, are also presented and they confirm the efficiency of our methods.

Restoration of Blurred Binary Images Using Discrete Tomography

Enhancement of degraded images of binary shapes is an important task in many image processing applications, e.g. to provide appropriate image quality for optical character recognition. Although many image restoration methods can be found in the literature, most of them are developed for grayscale images. In this paper we propose a novel binary image restoration algorithm. As a first step, it restores the projections of the shape using 1-dimensional deconvolution, then reconstructs the image from these projections using a discrete tomography technique. The method does not require any parameter setting or prior knowledge like an estimation of the signal-to-noise ratio. Numerical experiments on a synthetic dataset show that the proposed algorithm is robust to the level of the noise. The efficiency of the method has also been demonstrated on real out-of-focus alphanumeric images.

Recovering projective transformations between binary shapes

Binary image registration has been addressed by many authors recently however most of the proposed approaches are restricted to affine transformations. In this paper a novel approach is proposed to estimate the parameters of a general projective transformation (also called homography) that aligns two shapes. Recovering such projective transformations is a fundamental problem in computer vision with various applications. While classical approaches rely on established point correspondences the proposed solution does not need any feature extraction, it works only with the coordinates of the foreground pixels. The two-step method first estimates the perspective distortion independently of the affine part of the transformation which is recovered in the second step. As experiments on synthetic as well on real images show that the proposed method less sensitive to the strength of the deformation than other solutions. The efficiency of the method has also been demonstrated on the traffic sign matching problem.