Photo Sequencing

A group of people taking pictures of a dynamic event with their mobile phones is a popular sight. The set of still images obtained this way is rich in dynamic content but lacks accurate temporal information. We propose a method for photo-sequencing—temporally ordering a set of still images taken asynchronously by a set of uncalibrated cameras. Photo-sequencing is an essential tool in analyzing (or visualizing) a dynamic scene captured by still images. The first step of the method detects sets of corresponding static and dynamic feature points across images. The static features are used to determine the epipolar geometry between pairs of images, and each dynamic feature votes for the temporal order of the images in which it appears. The partial orders provided by the dynamic features are not necessarily consistent, and we use rank aggregation to combine them into a globally consistent temporal order of images. We demonstrate successful photo-sequencing on several challenging collections of images taken using a number of mobile phones.     

Tracking in a Dense Crowd Using Multiple Cameras

Tracking people in a dense crowd is a challenging problem for a single camera tracker due to occlusions and extensive motion that make human segmentation difficult. In this paper we suggest a method for simultaneously tracking all the people in a densely crowded scene using a set of cameras with overlapping fields of view. To overcome occlusions, the cameras are placed at a high elevation and only people’s heads are tracked. Head detection is still difficult since each foreground region may consist of multiple subjects. By combining data from several views, height information is extracted and used for head segmentation. The head tops, which are regarded as 2D patches at various heights, are detected by applying intensity correlation to aligned frames from the different cameras. The detected head tops are then tracked using common assumptions on motion direction and velocity. The method was tested on sequences in indoor and outdoor environments under challenging illumination conditions. It was successful in tracking up to 21 people walking in a small area (2.5 people per m²), in spite of severe and persistent occlusions.     

3D Shape Recovery of Smooth Surfaces: Dropping the Fixed-Viewpoint Assumption

We present a new method for recovering the 3D shape of a featureless smooth surface from three or more calibrated images illuminated by different light sources (three of them are independent). This method is unique in its ability to handle images taken from unconstrained perspective viewpoints and unconstrained illumination directions. The correspondence between such images is hard to compute and no other known method can handle this problem locally from a small number of images. Our method combines geometric and photometric information in order to recover dense correspondence between the images and accurately computes the 3D shape. Only a single pass starting at one point and local computation are used. This is in contrast to methods that use the occluding contours recovered from many images to initialize and constrain an optimization process. The output of our method can be used to initialize such processes. In the special case of fixed viewpoint, the proposed method becomes a new perspective photometric stereo algorithm. Nevertheless, the introduction of the multiview setup, self-occlusions, and regions close to the occluding boundaries are better handled, and the method is more robust to noise than photometric stereo. Experimental results are presented for simulated and real images.     

Application of the CALPHAD approach to Mg-alloys design

The CALPHAD method can be applied as a tool for both alloy development and process guideline determination. In this study, two Mg alloys were designed, their process parameters derived and, using the CALPHAD method, the final results simulated. These results were later confirmed using tangible experimental methods. It was found that γ$\gamma$- Mg₁₇Al₁₂ precipitates along the grain boundaries (GB), Mg₂Sn forms both along the GB and as fine precipitates in the α$\alpha$-Mg matrix and the addition of Ce mishmetal (MM) leads to the formation of elongated Al- rare earth (RE) precipitates along the GB. The microstructural stability at 200 ^°C is high, showing no decrease in microhardness for 32 days. It is shown that the CALPHAD method considerably reduces the effort of alloy design and that the reliability of the results is high.     

Using a Stochastic Complexity Measure to Check the Efficient Market Hypothesis

The weak form of the Efficient Market Hypothesis (EMH) states that current market price reflects fully the information from past prices and rules out prediction based on price data alone. No recent test of time series of stock returns rejects this weak-form hypothesis. This research offers another test of the weak form of the EHM that leads to different conclusions for some time series.The stochastic complexity of a time series is a measure of the number of bits needed to represent and reproduce the information in the time series. In an efficient market, compression of the time series is not possible, because there are no patterns and the stochastic complexity is high. In this research, Rissanen's context tree algorithm is used to identify recurring patterns in the data, and use them for compression. The weak form of the EMH is tested for 13 international stock indices and for all the stocks that comprise the Tel-Aviv 25 index (TA25), using sliding windows of 50, 75, and 100 consecutive daily returns. Statistically significant compression is detected in ten of the international stock index series. In the aggregate, 60% to 84% of the TA25 stocks tested demonstrate compressibility beyond randomness. This indicates potential market inefficiency.     

Generalization to Novel Views: Universal,Class-based,and Model-based Processing

A major problem in object recognition is that a novel image of a given object can be different from all previously seen images. Images can vary considerably due to changes in viewing conditions such as viewing position and illumination. In this paper we distinguish between three types of recognition schemes by the level at which generalization to novel images takes place: universal, class, and model-based. The first is applicable equally to all objects, the second to a class of objects, and the third uses known properties of individual objects. We derive theoretical limitations on each of the three generalization levels. For the universal level, previous results have shown that no invariance can be obtained. Here we show that this limitation holds even when the assumptions made on the objects and the recognition functions are relaxed. We also extend the results to changes of illumination direction. For the class level, previous studies presented specific examples of classes of objects for which functions invariant to viewpoint exist. Here, we distinguish between classes that admit such invariance and classes that do not. We demonstrate that there is a tradeoff between the set of objects that can be discriminated by a given recognition function and the set of images from which the recognition function can recognize these objects. Furthermore, we demonstrate that although functions that are invariant to illumination direction do not exist at the universal level, when the objects are restricted to belong to a given class, an invariant function to illumination direction can be defined. A general conclusion of this study is that class-based processing, that has not been used extensively in the past, is often advantageous for dealing with variations due to viewpoint and illuminant changes.     

Oleogels from Glycerol‐Based Lyotropic Liquid Crystals: Phase Diagrams and Structural Characterization

In the course of our studies on structured reverse lyotropic liquid crystals (LLC) as oleogels, a system was designed with the desired physical and rheology properties for cosmetic and pharmaceutical applications. The aqueous phase was partially replaced by glycerol to minimize bacteriological problems and obtain specific rheology characteristics. The constructed phase diagrams are based on ternary mixtures of glycerol monooleate (GMO), decane, water, and glycerol. The main lyotropic mesophase obtained in this study was reverse hexagonal derived from dilution line 8:2 (72 wt% GMO and 18 wt% decane) and 10 wt% water; or water:glycerol (wt ratios 3:1 and 1:1) mixture. It was found that heat‐cool fluctuation caused formation of new pseudo‐equilibrium structures with mesomorphic transformations to more fluid and less ordered mesostructures. Replacing up to 50 wt% of the water by glycerol significantly increases the gel phase region in the phase diagram, meaning more structural compositional options. The structural parameters were determined using cross‐polarized light microscopy (CPLM), differential scanning calorimeter (DSC), powder X‐ray diffraction (PXRD), and small angle X‐ray scattering (SAXS). Rheological measurements revealed viscoelastic properties of lyotropic liquid crystals with a decrease in the elasticity (G′), plasticity (G″), and complex viscosity (η*), as a function of increasing the glycerol content.     

Associative Grammar Combination Operators for Tree-Based Grammars

Polarized unification grammar (PUG) is a linguistic formalism which uses polarities to better control the way grammar fragments interact. The grammar combination operation of PUG was conjectured to be associative. We show that PUG grammar combination is not associative, and even attaching polarities to objects does not make it order-independent. Moreover, we prove that no non-trivial polarity system exists for which grammar combination is associative. We then redefine the grammar combination operator, moving to the powerset domain, in a way that guarantees associativity. The method we propose is general and is applicable to a variety of tree-based grammar formalisms.