TRIDIMENSIONAL REGRESSION

Shape analysis is useful for a wide variety of disciplines and has many applications. There are many different approaches to shape analysis, one of which focuses on the analysis of shapes that are represented by the coordinates of predefined landmarks on the object. This paper introduces Tridimensional Regression, a technique that can be used for mapping images and shapes that are represented by sets of three-dimensional landmark coordinates. The degree of similarity between shapes can be quantified using the tridimensional coefficient of determination (R2). An experiment was conducted to evaluate the effectiveness of this technique to correctly match the image of a face with another image of the same face. These results were compared to the R2 values obtained when only two dimensions are used, and show using three dimensions increases the ability to correctly discriminate between faces

Tridimensional Regression for Comparing and Mapping 3D Anatomical Structures

Shape analysis is useful for a wide variety of disciplines and has many applications. There are many approaches to shape analysis, one of which focuses on the analysis of shapes that are represented by the coordinates of predefined landmarks on the object. This paper discusses Tridimensional Regression, a technique that can be used for mapping images and shapes that are represented by sets of three-dimensional landmark coordinates, for comparing and mapping 3D anatomical structures. The degree of similarity between shapes can be quantified using the tridimensional coefficient of determination (R2). An experiment was conducted to evaluate the effectiveness of this technique to correctly match the image of a face with another image of the same face. These results were compared to the R2 values obtained when only two dimensions are used and show that using three dimensions increases the ability to correctly match and discriminate between faces

Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

Potential physiological stress biomarkers in human sweat

Emotional sweating occurs in response to affective stimuli like fear, anxiety, or stress and is more evident in specific parts of the body such as the palms, soles, and axillae. During emotional sweating, humans release many volatile organic compounds (VOCs) that could play a crucial role as possible com-municative signals of specific emotions. In this preliminary study, we investigated seven volatiles belonging to the chemical class of acids and released from the armpit as possible stress biomarkers. To this aim, we processed sweat VOCs and physiological stress correlates such as heart rate variability (HRV), electrodermal activity, and thermal imaging during a Stroop color-word test. Particularly, we modelled the variability of well-known stress markers extracted from the physiological signals as a function of the acid VOCs by means of LASSO regression. LASSO results revealed that the dodecanoic acid was the only selected regressor and it was able to significantly explain more than 64 % of the variance of both the mean temperature of the tip of the nose (p=0.018, R2=0.64) and of the mean HRV (p=0.011, R2=0.67). Although preliminary, our results suggest that dodecanoic acid could be a marker of the sympathetic nervous system response to stress stimuli, opening for the detection of new biomarkers of stress

Adaptación y comparación de dos metodologías de reconocimiento facial aplicados a la detección de somnolencia en conductores

Esta tesis busca realizar una comparación entre dos metodologías de reconocimiento de expresiones faciales: Viola-Jones y Regression Based Facial Landmark Detection, los cuales han sido adaptados para la detección de somnolencia, para conocer cuál de ellas es la óptima y se adecua mejor a las condiciones variables de: oclusión, rotación de rostro e iluminación

Content Analysis of Acculturation Research in Counseling and Counseling Psychology: A 22-Year Review

The authors conducted a 22-year (1988–2009) content analysis of quantitative empirical research that included acculturation and/or enculturation as a study variable(s). A total of 138 studies in 134 articles were systematically evaluated from 5 major American Psychological Association and American Counseling Association journals in counseling and counseling psychology, including Journal of Counseling Psychology, The Counseling Psychologist, Journal of Counseling and Development, Journal of Multicultural Counseling and Development, and Cultural Diversity and Ethnic Minority Psychology. To guide the analysis, the authors conceptualized acculturation/enculturation as a “bilinear” (i.e., developing cultural orientations to both majority and ethnic cultures) and “multidimensional” (i.e., across multiple areas such as behaviors, values, identity, and knowledge) cultural socialization process that occurs in interaction with “social contexts” (e.g., home, school, work, West Coast, Midwest). Findings include the patterns and trends of acculturation/enculturation research in (a) conceptualization and use of acculturation/enculturation variable(s), (b) research designs (e.g., sample characteristics, instruments, data collection, and analysis methods), (c) content areas, and (d) changes in total publications and trends over time. Additionally, meta-analyses were conducted on the relationship of acculturation/enculturation and a few key variables of mental health, adjustment, and well-being. Major findings and directions for future research are discussed

Effects of Orientation Change on Spatial Learning of Novel Environments on Younger and Older Adults

Yamamoto and DeGirolamo (2012) found that increasing age has unequal effects of impairment on spatial learning dependent on the perspective in which an environment is learned. Further, the learned condition of ground-level perspective (first-person exploratory) showed greater decline in elderly participants than was found in aerial (map reading) conditions. These results supported previous research involving fMRI scans implicating the medial temporal lobe (MTL) role in exploratory navigation of novel environments and MRI scans indicating MTL atrophy with age. However, Yamamoto and DeGirolamo (2012) did not consider the effects of conducting the experiment with one condition being presented with changing orientation (ground-level) and the other condition having fixed orientation (aerial). Utilizing new research revealing the MTL\u27s role in orientation processing, the present study reexamined Yamamoto and DeGirolamo (2012) findings with the introduction of the condition aerial-with-turns (map reading with changing orientation). The findings of this experiment suggest changing orientation in the learning condition has greater impact on elderly participants\u27 performance of spatial learning tasks than that of the perspective in which the learning condition is i