Professional Driving : An Emphasis on Organizational Safety Climate

According to the literature and statistical figures, professional drivers constitute a high-risk group in traffic and should be investigated in connection with the factors related to safe driving. However, safety-related behaviours and outcomes among professional drivers have attracted very little attention from safety researchers. In addition, comparing different professional and non-professional driver groups in terms of critical on-the-road characteristics and outcomes has been indicated in the literature as being necessary for a more comprehensive understanding of driver groups and the nature of driving itself. The aim of the present study was to investigate professional driving from a safety climate stand point in relation to predominant driving-related factors and by considering the differences between driver groups. Hence, four Sub-studies were conducted according to a framework emphasizing the relationships between safety climate, driver groups, driver stress, human factors (i.e., driver behaviour and performance) and accidents. Demographic information, as well as data for driver behaviour, performance, and driver stress was collected by questionnaire. The data was analysed using factor analysis, analysis of covariance as well as hierarchical and logistic regression analysis. The results revealed multi-dimensional factor structures for the safety climate measures. Considering the relationships between variables, differences were evidenced regarding on-the-road stress reactions, risky driver behaviours and penalties, between the various professional and non-professional driver groups. Driver stress was found to be related to accidents. The results also indicated that the safety climate has positive relationships with both driver behaviour and performance, and as well as involvement in accidents. The present study has a number of critical implications resulting from the fact that the way in which the effects of safety climate on professional driving were investigated, as well as the differences between professional and non-professional driver groups, was unique. Additionally, for the first time, a safety climate scale was developed specifically for professional drivers. According to the results of the study and to previous literature, a tentative model was proposed representing a possible route for the relationships between safety climate, human factors, driver stress, driver groups and accidents, by emphasizing the effects of safety climate.

Professional non-neutrality: criticising the third party in psychotherapy

Using audio-recorded data from cognitive-constructivist psychotherapy, the article shows a particular institutional context in which successful professional action does not adhere to the pattern of affective neutrality which Parsons saw as an inherent component of medicine and psychotherapy. In our data, the professional’s non-neutrality functions as a tool for achieving institutional goals. The analysis focuses on the psychotherapist’s actions that convey a critical stance towards a third party with whom the patient has experienced problems. The data analysis revealed two practices of this kind of critique: (1) the therapist can confirm the critique that the patient has expressed or (2) return to the critique from which the patient has focused away. These actions are shown to build grounds for the therapist’s further actions that challenge the patient’s dysfunctional beliefs. The article suggests that in the case of psychotherapy, actions that as such might be seen as apparent lapses from the neutral professional role can in their specific context perform the task of the institution at hand.

Multi-objective optimization of satellite image registration using discrete Pparticle swarm optimisation

A new multi-sensor image registration technique is proposed based on detecting the feature corner points using modified Harris Corner Detector (HDC). These feature points are matched using multi-objective optimization (distance condition and angle criterion) based on Discrete Particle Swarm Optimization (DPSO). This optimization process is more efficient as it considers both the distance and angle criteria to incorporate multi-objective switching in the fitness function. This optimization process helps in picking up three corresponding corner points detected in the sensed and base image and thereby using the affine transformation, the sensed image is aligned with the base image. Further, the results show that the new approach can provide a new dimension in solving multi-sensor image registration problems. From the obtained results, the performance of image registration is evaluated and is concluded that the proposed approach is efficient.

DSA image registration using non-uniform MRF model and pivotal control points

In order to reduce the motion artifacts in DSA, non-rigid image registration is commonly used before subtracting the mask from the contrast image. Since DSA registration requires a set of spatially non-uniform control points, a conventional MRF model is not very efficient. In this paper, we introduce the concept of pivotal and non-pivotal control points to address this, and propose a non-uniform MRF for DSA registration. We use quad-trees in a novel way to generate the non-uniform grid of control points. Our MRF formulation produces a smooth displacement field and therefore results in better artifact reduction than that of registering the control points independently. We achieve improved computational performance using pivotal control points without compromising on the artifact reduction. We have tested our approach using several clinical data sets, and have presented the results of quantitative analysis, clinical assessment and performance improvement on a GPU. (C) 2013 Elsevier Ltd. All rights reserved.

On Averaging Multiview Relations for 3D Scan Registration

In this paper, we present an extension of the iterative closest point (ICP) algorithm that simultaneously registers multiple 3D scans. While ICP fails to utilize the multiview constraints available, our method exploits the information redundancy in a set of 3D scans by using the averaging of relative motions. This averaging method utilizes the Lie group structure of motions, resulting in a 3D registration method that is both efficient and accurate. In addition, we present two variants of our approach, i.e., a method that solves for multiview 3D registration while obeying causality and a transitive correspondence variant that efficiently solves the correspondence problem across multiple scans. We present experimental results to characterize our method and explain its behavior as well as those of some other multiview registration methods in the literature. We establish the superior accuracy of our method in comparison to these multiview methods with registration results on a set of well-known real datasets of 3D scans.

Co-registration of speech production datasets from electromagnetic articulography and real-time magnetic resonance imaging

This paper describes a spatio-temporal registration approach for speech articulation data obtained from electromagnetic articulography (EMA) and real-time Magnetic Resonance Imaging (rtMRI). This is motivated by the potential for combining the complementary advantages of both types of data. The registration method is validated on EMA and rtMRI datasets obtained at different times, but using the same stimuli. The aligned corpus offers the advantages of high temporal resolution (from EMA) and a complete mid-sagittal view (from rtMRI). The co-registration also yields optimum placement of EMA sensors as articulatory landmarks on the magnetic resonance images, thus providing richer spatio-temporal information about articulatory dynamics. (C) 2014 Acoustical Society of America

GLOBAL REGISTRATION OF MULTIPLE POINT CLOUDS USING SEMIDEFINITE PROGRAMMING

Consider N points in R-d and M local coordinate systems that are related through unknown rigid transforms. For each point, we are given (possibly noisy) measurements of its local coordinates in some of the coordinate systems. Alternatively, for each coordinate system, we observe the coordinates of a subset of the points. The problem of estimating the global coordinates of the N points (up to a rigid transform) from such measurements comes up in distributed approaches to molecular conformation and sensor network localization, and also in computer vision and graphics. The least-squares formulation of this problem, although nonconvex, has a well-known closed-form solution when M = 2 (based on the singular value decomposition (SVD)). However, no closed-form solution is known for M >= 3. In this paper, we demonstrate how the least-squares formulation can be relaxed into a convex program, namely, a semidefinite program (SDP). By setting up connections between the uniqueness of this SDP and results from rigidity theory, we prove conditions for exact and stable recovery for the SDP relaxation. In particular, we prove that the SDP relaxation can guarantee recovery under more adversarial conditions compared to earlier proposed spectral relaxations, and we derive error bounds for the registration error incurred by the SDP relaxation. We also present results of numerical experiments on simulated data to confirm the theoretical findings. We empirically demonstrate that (a) unlike the spectral relaxation, the relaxation gap is mostly zero for the SDP (i.e., we are able to solve the original nonconvex least-squares problem) up to a certain noise threshold, and (b) the SDP performs significantly better than spectral and manifold-optimization methods, particularly at large noise levels.