Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Wavelets and the generalization of the variogram

The experimental variogram computed in the usual way by the method of moments and the Haar wavelet transform are similar in that they filter data and yield informative summaries that may be interpreted. The variogram filters out constant values; wavelets can filter variation at several spatial scales and thereby provide a richer repertoire for analysis and demand no assumptions other than that of finite variance. This paper compares the two functions, identifying that part of the Haar wavelet transform that gives it its advantages. It goes on to show that the generalized variogram of order k=1, 2, and 3 filters linear, quadratic, and cubic polynomials from the data, respectively, which correspond with more complex wavelets in Daubechies's family. The additional filter coefficients of the latter can reveal features of the data that are not evident in its usual form. Three examples in which data recorded at regular intervals on transects are analyzed illustrate the extended form of the variogram. The apparent periodicity of gilgais in Australia seems to be accentuated as filter coefficients are added, but otherwise the analysis provides no new insight. Analysis of hyerpsectral data with a strong linear trend showed that the wavelet-based variograms filtered it out. Adding filter coefficients in the analysis of the topsoil across the Jurassic scarplands of England changed the upper bound of the variogram; it then resembled the within-class variogram computed by the method of moments. To elucidate these results, we simulated several series of data to represent a random process with values fluctuating about a mean, data with long-range linear trend, data with local trend, and data with stepped transitions. The results suggest that the wavelet variogram can filter out the effects of long-range trend, but not local trend, and of transitions from one class to another, as across boundaries.

Recategorization and subgroup identification: predicting and preventing threats from common ingroups

Much work has supported the idea that recategorization of ingroups and outgroups into a superordinate category can have beneficial effects for intergroup relations. Recently, however, increases in bias following recategorization have been observed in some contexts. It is argued that such unwanted consequences of recategorization will only be apparent for perceivers who are highly committed to their ingroup subgroups. In Experiments 1 to 3, the authors observed, on both explicit and implicit measures, that an increase in bias following recategorization occurred only for high subgroup identifiers. In Experiment 4, it was found that maintaining the salience of subgroups within a recategorized superordinate group averted this increase in bias for high identifiers and led overall to the lowest levels of bias. These findings are discussed in the context of recent work on the Common Ingroup Identity Model.

Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.

Facial feature extraction and selection by Gabor wavelets and boosting

In this paper, we present a feature selection approach based on Gabor wavelet feature and boosting for face verification. By convolution with a group of Gabor wavelets, the original images are transformed into vectors of Gabor wavelet features. Then for individual person, a small set of significant features are selected by the boosting algorithm from a large set of Gabor wavelet features. The experiment results have shown that the approach successfully selects meaningful and explainable features for face verification. The experiments also suggest that for the common characteristics such as eyes, noses, mouths may not be as important as some unique characteristic when training set is small. When training set is large, the unique characteristics and the common characteristics are both important.

Towards an integrated script for risk and value management: insights from the dramaturgical metaphor

It is contended that the current conceptual distinction between risk management and value management is unsustainable. The origins of the two traditions are reviewed and critiqued from a postmodernist perspective. It is concluded that they differ primarily in terms of their rhetoric, rather than their substantive content. Insights into the current practice of risk and value management are provided by considering their enactment in terms of ‘performance’. The scripts for such performances are seen to be provided by the accepted methodologies which determine the language to be used and the roles to be acted out. A coherent integrated script for risk and value management can be provided by the methodology known as strategic choice, which replaces the language of ‘risk’ and ‘value’ with that of ‘uncertainty’. The benefits of adopting this alternative script are illustrated through six case studies.

Meta-analysis of the INSIG2 association with obesity including 74,345 individuals: does heterogeneity of estimates relate to study design?

The INSIG2 rs7566605 polymorphism was identified for obesity (BMI> or =30 kg/m(2)) in one of the first genome-wide association studies, but replications were inconsistent. We collected statistics from 34 studies (n = 74,345), including general population (GP) studies, population-based studies with subjects selected for conditions related to a better health status ('healthy population', HP), and obesity studies (OB). We tested five hypotheses to explore potential sources of heterogeneity. The meta-analysis of 27 studies on Caucasian adults (n = 66,213) combining the different study designs did not support overall association of the CC-genotype with obesity, yielding an odds ratio (OR) of 1.05 (p-value = 0.27). The I(2) measure of 41% (p-value = 0.015) indicated between-study heterogeneity. Restricting to GP studies resulted in a declined I(2) measure of 11% (p-value = 0.33) and an OR of 1.10 (p-value = 0.015). Regarding the five hypotheses, our data showed (a) some difference between GP and HP studies (p-value = 0.012) and (b) an association in extreme comparisons (BMI> or =32.5, 35.0, 37.5, 40.0 kg/m(2) versus BMI<25 kg/m(2)) yielding ORs of 1.16, 1.18, 1.22, or 1.27 (p-values 0.001 to 0.003), which was also underscored by significantly increased CC-genotype frequencies across BMI categories (10.4% to 12.5%, p-value for trend = 0.0002). We did not find evidence for differential ORs (c) among studies with higher than average obesity prevalence compared to lower, (d) among studies with BMI assessment after the year 2000 compared to those before, or (e) among studies from older populations compared to younger. Analysis of non-Caucasian adults (n = 4889) or children (n = 3243) yielded ORs of 1.01 (p-value = 0.94) or 1.15 (p-value = 0.22), respectively. There was no evidence for overall association of the rs7566605 polymorphism with obesity. Our data suggested an association with extreme degrees of obesity, and consequently heterogeneous effects from different study designs may mask an underlying association when unaccounted for. The importance of study design might be under-recognized in gene discovery and association replication so far.

The projected hand illusion: component structure in a community sample, and association with demographics, cognition and psychotic-like experiences

The projected hand illusion (PHI) is a variant of the rubber hand illusion (RHI), and both are commonly used to study mechanisms of self-perception. A questionnaire was developed by Longo et al. (2008) to measure qualitative changes in the RHI. Such psychometric analyses have not yet been conducted on the questionnaire for the PHI. The present study is an attempt to validate minor modifications of the questionnaire of Longo et al. to assess the PHI in a community sample (n = 48) and to determine the association with selected demographic (age, sex, years of education), cognitive (Digit Span), and clinical (psychotic-like experiences) variables. Principal components analysis on the questionnaire data extracted four components: Embodiment of “Other” Hand, Disembodiment of Own Hand, Deafference, and Agency—in both synchronous and asynchronous PHI conditions. Questions assessing “Embodiment” and “Agency” loaded onto orthogonal components. Greater illusion ratings were positively associated with being female, being younger, and having higher scores on psychotic-like experiences. There was no association with cognitive performance. Overall, this study confirmed that self-perception as measured with PHI is a multicomponent construct, similar in many respects to the RHI. The main difference lies in the separation of Embodiment and Agency into separate constructs, and this likely reflects the fact that the “live” image of the PHI presents a more realistic picture of the hand and of the stroking movements of the experimenter compared with the RHI.