Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification.

Driver recognition based on dynamic handgrip pattern on steeling wheel

This paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect handgrip data exerted by the hands of drivers who intend to start the vehicle. Then, the likelihood-ratio-based classifier is designed to distinguish rightful driver of a car after analyzing their inherent dynamic features of grasping. The experimental results obtained in this study show that mean acceptance rates of 85.4% for the trained subjects and mean rejection rates of 82.65% for the un-trained ones are achieved by the classifier in the two batches of testing. It can be concluded that the driver verification approach based on dynamic handgrip recognition on steering wheel is a promising biometric technology and will be further explored in the near future in smart car design.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

Dynamic recrystallization of austenite in Ni-30 Pct Fe model alloy : microstructure and texture evolution

The microstructure and crystallographic texture development in an austenitic Ni-30 pct Fe model alloy was investigated within the dynamic recrystallization (DRX) regime using hot torsion testing. The prominent DRX nucleation mechanism was strain-induced grain boundary migration accompanied by the formation of large-angle sub-boundaries and annealing twins. The increase in DRX volume fraction occurred through the formation of multiple twinning chains. With increasing strain, the pre-existing Σ3 twin boundaries became gradually converted to general boundaries capable of acting as potent DRX nucleation sites. The texture characteristics of deformed grains resulted from the preferred consumption of high Taylor factor components by new recrystallized grains. Similarly, the texture of DRX grains was dominated by low Taylor factor components as a result of their lower consumption rate during the DRX process. The substructure of deformed grains was characterized by “organized,” banded subgrain arrangements, while that of the DRX grains displayed “random,” more equiaxed subgrain/cell configurations.

Texture and substructure development during dynamic recrystallization in Ni-30Fe austenite

An austenitic Ni-30%Fe model alloy was employed to investigate the texture and substructure development within the deformed matrix and dynamically recrystallized (DRX) grains during hot torsion deformation. Both the deformed matrix and DRX grains predominantly displayed the crystallographic texture components expected for simple shear deformation. The characteristics of the deformed matrix texture evolution during deformation largely resulted from the preferred consumption of high Taylor factor components by new recrystallized grains. Likewise, the comparatively weaker crystallographic texture of DRX grains became increasingly dominated by low Taylor factor components as a result of their easier nucleation and lower consumption rate during DRX. There was a significant difference in the substructure formation mechanism between the deformed matrix and DRX grains for a given texture component. The deformed matrix substructure was largely characterized by “organized”, banded subgrain arrangements with alternating misorientations, while the substructure of DRX grains was more “random” in character and displayed complex, more equiaxed subgrain/cell arrangements characterized by a local accumulation of misorientations. Substructure characteristics of individual orientation components were principally consistent with the corresponding Taylor factor values.

Texture and substructure characteristics of dynamic recrystallization in a Ni–30%Fe austenitic model alloy

The substructure and texture development during dynamic recrystallization (DRX) of an austenitic Ni–30%Fe model alloy was investigated using hot torsion testing. The current results revealed that the DRX texture was dominated by grains with a low Taylor factor component. This was related to the preferred nucleation and lower consumption rates of these grains during DRX. The substructure of DRX grains was ‘‘random” in character and displayed complex subgrain/cell arrangements that largely depended on grain orientation.

Texture and substructure development during post-dynamic softening in Ni-30%Fe austenite

The texture and substructure development during post-dynamic annealing of an austenitic Ni-30%Fe model alloy after complete dynamic recrystallization was investigated using electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM). A novel mechanism of metadynamic softening is proposed based on the experimental investigation of the grain structure, crystallographic texture and dislocation substructure evolution. The initial softening stage involved rapid growth of the dynamically formed nuclei and migration of the mobile boundaries. The subboundaries within DRX grains progressively disintegrated through dislocation climb and dislocation annihilation, which ultimately led to the formation of dislocation-free grains, while the grain boundary migration gradually became slower. As a result, the DRX texture was largely preserved throughout the annealing process.

Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Online context recognition in multisensor systems using Dynamic Time Warping

In this paper, we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy.

Dynamic price discrimination with customer recognition

This paper studies a general two-period model of product line pricing with customer recognition. Specifically, we consider a monopolist who can sell vertically differentiated products over two periods to heterogeneous consumers. Each consumer demands one unit of the product in each period. In the second period, the monopolist can condition the price-quality offers on the observed purchasing behavior in the first period. In this setup, the monopolist can price discriminate consumers in two dimensions: by quality as well as by purchase history. We fully characterize the monopolist's optimal pricing strategy when there are two types of consumers. When the type space is a continuum, we show that there is no fully separating equilibrium, and some properties of the optimal contracts (price-quality pairs) are characterized within the class of partitional perfect Bayesian equilibria.

Influence of deformation conditions and texture on the high temperature flow stress of magnesium AZ31

The evolution of hot working flow stress with strain is examined in torsion, uniaxial compression and channel die compression. The flow stress was found to be strongly dependent on texture and deformation mode. At low strains this dependency accounted for a difference in flow stress of up to a factor of two. At higher strains the influence of texture and deformation mode was less marked. The stresses corresponding to an equivalent strain of 0.5 were modelled using a power law expression with an activation energy of 147 kJ/mol and a strain rate exponent of 0.15. The influence of texture and deformation mode on flow stress is rationalised in terms of the influence of prismatic slip, twinning and dynamic recrystallisation on deformation stress and structure.

The influence of solute carbon in cold-rolled steels on shear band formation and recrystallization texture

Two experiments were conducted to clarify the roles of grain size, solute carbon and strain in determining the recrystallization textures of cold-rolled and annealed steels. In the first experiment, samples of coarse-grained low-carbon (LC) and interstitial-free (IF) steels were cold-rolled to a 75% reduction in thickness. One sample from each steel was polished and cold-rolled an additional 5%, while the remaining samples were annealed for various times at 650°C. In the second experiment, three samples from a commercial LC steel sheet were rolled 70% at 300°C. Two of the samples were given a further rolling reduction of 5% of the original thickness, with one of the samples being given this additional reduction at 300°C and the other at room temperature. Goss recrystallization textures are strengthened by coarse initial grain sizes, the presence of solute carbon and rolling at a temperature where dynamic strain ageing occurs, but are weakened by additional rolling beyond a reduction of 70%, especially when this extra rolling is conducted at a temperature where dynamic strain ageing does not occur. Characterization of key features of the deformed and recrystallized steels using optical microscopy, scanning electron microscopy (SEM) and electron back-scatter diffraction (EBSD) supports a rationale for these effects based on the repeated activation and deactivation of shear bands and the influence of solute carbon and dynamic strain ageing on the operating life of the bands and the accumulation of strain within them.

A dynamic ratio-based model for signalling corporate collapse

The recognition of behavioural elements in finance has caused major shifts in the analytic framework pertaining to ratio-based modeling of corporate collapse. The modeling approach so far has been based on the classical rational theory in behavioural economics, which assumes that the financial ratios (i.e., the predictors of collapse) are static over time. The paper argues that, in the absence of rational economic theory, a static model is flawed, and that a suitable model instead is one that reflects the heuristic behavioural framework, which is what characterises behavioural attributes of company directors and in turn influences the accounting numbers used in calculating the financial ratios. This calls for a dynamic model: dynamic in the sense that it does not rely on a coherent assortment of financial ratios for signaling corporate collapse over multiple time periods. This paper provides empirical evidence, using a data set of Australian publicly listed companies, to demonstrate that a dynamic model consistently outperforms its static counterpart in signaling the event of collapse. On average, the overall predictive power of the dynamic model is 86.83% compared to an average overall predictive power of 69.35% for the static model.