Efficient high order matching

We present a computational approach to high-order matching of data sets in IR(d). Those are matchings based on data affinity measures that score the matching of more than two pairs of points at a time. High-order affinities are represented by tensors and the matching is then given by a rank-one approximation of the affinity tensor and a corresponding discretization. Our approach is rigorously justified by extending Zass and Shashua's hypergraph matching to high-order spectral matching. This paves the way for a computationally efficient dual-marginalization spectral matching scheme. We also show that, based on the spectral properties of random matrices, affinity tensors can be randomly sparsified while retaining the matching accuracy. Our contributions are experimentally validated by applying them to synthetic as well as real data sets.     

Textures on Rank‐1 Lattices

Storing textures on orthogonal tensor product lattices is predominant in computer graphics, although it is known that their sampling efficiency is not optimal. In two dimensions, the hexagonal lattice provides the maximum sampling efficiency. However, handling these lattices is difficult, because they are not able to tile an arbitrary rectangular region and have an irrational basis. By storing textures on rank‐1 lattices, we resolve both problems: Rank‐1 lattices can closely approximate hexagonal lattices, while all coordinates of the lattice points remain integer. At identical memory footprint texture quality is improved as compared to traditional orthogonal tensor product lattices due to the higher sampling efficiency. We introduce the basic theory of rank‐1 lattice textures and present an algorithmic framework which easily can be integrated into existing off‐line and real‐time rendering systems.     

Clustering in ordered dissimilarity data

This paper presents a new technique for clustering either object or relational data. First, the data are represented as a matrix D of dissimilarity values. D is reordered to D * using a visual assessment of cluster tendency algorithm. If the data contain clusters, they are suggested by visually apparent dark squares arrayed along the main diagonal of an image I( D *) of D *. The suggested clusters in the object set underlying the reordered relational data are found by defining an objective function that recognizes this blocky structure in the reordered data. The objective function is optimized when the boundaries in I( D *) are matched by those in an aligned partition of the objects. The objective function combines measures of contrast and edginess and is optimized by particle swarm optimization. We prove that the set of aligned partitions is exponentially smaller than the set of partitions that needs to be searched if clusters are sought in D . Six numerical examples are given to illustrate various facets of the algorithm. © 2009 Wiley Periodicals, Inc.     

Identification of statistical patterns in complex systems via symbolic time series analysis

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.     

High-level user interfaces for transfer function design with semantics

Many sophisticated techniques for the visualization of volumetric data such as medical data have been published. While existing techniques are mature from a technical point of view, managing the complexity of visual parameters is still difficult for non-expert users. To this end, this paper presents new ideas to facilitate the specification of optical properties for direct volume rendering. We introduce an additional level of abstraction for parametric models of transfer functions. The proposed framework allows visualization experts to design high-level transfer function models which can intuitively be used by non-expert users. The results are user interfaces which provide semantic information for specialized visualization problems. The proposed method is based on principal component analysis as well as on concepts borrowed from computer animation.     

Relational mountain (density) clustering method and web log analysis

The mountain clustering method and the subtractive clustering method are useful methods for finding cluster centers based on local density in object data. These methods have been extended to shell clustering. In this article, we propose a relational mountain clustering method (RMCM), which produces a set of (proto) typical objects as well as a crisp partition of the objects generating the relation, using a new concept that we call relational density. We exemplify RMCM by clustering several relational data sets that come from object data. Finally, RMCM is applied to web log analysis, where it produces useful user profiles from web log data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 375–392, 2005.     

Snakes on the watershed

We present a new approach for object boundary extraction, called the watersnake. It is a two-step snake algorithm whose energy functional is minimized by the dynamic programming method. It is more robust to local minima because it finds the solution by searching the entire energy space. To reduce the complexity of the minimization process, the watershed transformation and a coarse-to-fine strategy are used. The new technique is compared to standard methods for accuracy in synthetic data and is applied to segmentation of white blood cells in bone marrow images     

The morphological mechanism of the development of myosatellitocytes from the structural elements of the muscle fiber during increased functional activity of the skeletal muscles

Under increased muscular activity in some muscular fibers disintegration areas of myofibrillar apparatus has been revealed. Migration of myonuclei into these microregions starts the mechanism of their segregation due to plasmolemma produced from the reticulum sarcoplasmaticum and triad systems surface. After plasmolemma production in "sarcocytes" intensive development and differentiation of organellae occur. As a result of differentiation "sarcocytes" transform in to myosatellitocytes of type-2 and migrate under lamina externa muscular fibers. So, a hypothesis about formation of myogenic tissue's cellular phase from the myosymplastic one has been confirmed.     

The supercooling dependence of the initial fold length of polyethylene crystallized from the melt: unification of melt and solution crystallization

Following the previous recognition [1], reached with the aid of real time low angle X-ray diffraction (using a synchrotron X-ray source) that in melt crystallized polyethylene the initial (primary) lamellar thickness is much smaller than hitherto envisaged, we have proceeded to construct the full relationship between primary fold length (I _g ^*) and supercooling (T) covering a wide range of crystallization temperature (T _c). The principal result of this work is the identification of supercooling as the sole factor which determinesI _g ^*. Comparison with crystallization from solution [2–4] has revealed that theI _g ^* against T curves are completely superposable thus removing the gap which has existed up to the present between melt and solution crystallization, bringing about a welcome unification of these two separate (at least as far as fold length was concerned) aspects of polymer crystallization. Further, we show that while T determinesI _g ^*, subsequent thickening is determined by the absolute temperature. Isothermal thickening in particular proceeds first by a large discontinuous step followed by a continuous logarithmic increase with time. The importance of these findings and in particular the affirmation of the unique role of supercooling for chain folding and lamellar crystallization in general is emphasized.