The Classical Theory of Invariants and Object Recognition Using Algebraic Curve and Surfaces

Combining implicit polynomials and algebraic invariants for representing and recognizing complicated objects proves to be a powerful technique. In this paper, we explore the findings of the classical theory of invariants for the calculation of algebraic invariants of implicit curves and surfaces, a theory largely disregarded in the computer vision community by a shadow of skepticism. Here, the symbolic method of the classical theory is described, and its results are extended and implemented as an algorithm for computing algebraic invariants of projective, affine, and Euclidean transformations. A list of some affine invariants of 4th degree implicit polynomials generated by the proposed algorithm is presented along with the corresponding symbolic representations, and their use in recognizing objects represented by implicit polynomials is illustrated through experiments. An affine invariant fitting algorithm is also proposed and the performance is studied.     

International Nuclear Safety Center Database on Thermophysical Properties of Reactor Materials

The International Nuclear Safety Center (INSC) database has been established at Argonne National Laboratory to provide easily accessible data and information necessary to perform nuclear safety analyses and to promote international collaboration through the exchange of nuclear safety information. The INSC database, located on the World Wide Web at http://www.insc.anl.gov, contains critically assessed recommendations for reactor-material properties for normal operating conditions, transients, and severe accidents. The initial focus of the database is on thermodynamic and transport properties of materials for water reactors. Materials that are being included in the database are fuel, absorbers, cladding, structural materials, coolants, and liquid mixtures of combinations of UO₂, ZrO₂, Zr, stainless steel, absorber materials, and concrete. For each property, the database includes (1) a summary of recommended equations with uncertainties; (2) a detailed data assessment giving the basis for the recommendations, comparisons with experimental data and previous recommendations, and uncertainties; (3) graphs showing recommendations, uncertainties, and comparisons with data and other equations; and (4) property values tabulated as a function of temperature.     

Estimation of storage time of yogurt with artificial neural network modeling

Changes in the physical, chemical, and microbiological structure of yogurt determine the storage and shelf life of the product. In this study, microbial counts and pH values of yogurt during storage were determined at d 1, 7, and 14. Simultaneously, image processing of yogurt was digitized by using a machine vision system (MVS) to determine color changes during storage, and the obtained data were modeled with an artificial neural network (ANN) for prediction of shelf life of set-type whole-fat and low-fat yogurts. The ANN models were developed using back-propagation networks with a single hidden layer and sigmoid activation functions. The input variables of the network were pH; total aerobic, yeast, mold, and coliform counts; and color analysis values measured by the machine vision system. The output variable was the storage time of the yogurt. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R² = 0.9996) showing that the developed model was able to analyze nonlinear multivariant data with very good performance, fewer parameters, and shorter calculation time. The model might be an alternative method to control the expiration date of yogurt shown in labeling and provide consumers with a safer food supply.     

Bacterial diversity dynamics of traditional Turkish Ezine Cheese as evaluated by PCR‐DGGE and SSCP analysis

The aim of this study was to evaluate the bacterial ecosystem of milk and Ezine cheese by PCR amplification of the V3 region of the bacterial 16S rRNA gene followed by denaturing gradient gel electrophoresis (DGGE) and by monitoring the bacterial diversity dynamics using PCR single‐strand conformation polymorphism (SSCP) analysis. PCR‐DGGE analysis revealed that 17 different bands and strains belonging to the Lactococcus lactis group and Streptococcus thermophilus were predominant during manufacturing and ripening. SSCP analysis revealed that the bacterial profiles of the two cheese samples were similar.     

Real-time feature-based image morphing for memory-efficient impostor rendering and animation on GPU

Real-time rendering of large animated crowds consisting of thousands of virtual humans is important for several applications including simulations, games, and interactive walkthroughs but cannot be performed using complex polygonal models at interactive frame rates. For that reason, methods using large numbers of precomputed image-based representations, called impostors, have been proposed. These methods take advantage of existing programmable graphics hardware to compensate for computational expense while maintaining visual fidelity. Thanks to these methods, the number of different virtual humans rendered in real time is no longer restricted by computational power but by texture memory consumed for the variety and discretization of their animations. This work proposes a resource-efficient impostor rendering methodology that employs image morphing techniques to reduce memory consumption while preserving perceptual quality, thus allowing higher diversity or resolution of the rendered crowds. Results of the experiments indicated that the proposed method, in comparison with conventional impostor rendering techniques, can obtain 38 % smoother animations or 87 % better appearance quality by reducing the number of key-frames required for preserving the animation quality via resynthesizing them with up to 92 % similarity on real time.