Representation Of Orientation Research Articles

A 3D personalized cardiac myocyte aggregate orientation model using MRI data-driven low-rank basis functions.

Cardiac myocyte aggregate orientation has a strong impact on cardiac electrophysiology and mechanics. Studying the link between structural characteristics, strain, and stresses over the cardiac cycle and cardiac function requires a full volumetric representation of the microstructure. In this work, we exploit the structural similarity across hearts to extract a low-rank representation of predominant myocyte orientation in the left ventricle from high-resolution magnetic resonance ex-vivo cardiac diffusion tensor imaging (cDTI) in porcine hearts. We compared two reduction methods, Proper Generalized Decomposition combined with Singular Value Decomposition and Proper Orthogonal Decomposition. We demonstrate the existence of a general set of basis functions of aggregated myocyte orientation which defines a data-driven, personalizable, parametric model featuring higher flexibility than existing atlas and rule-based approaches. A more detailed representation of microstructure matching the available patient data can improve the accuracy of personalized computational models. Additionally, we approximate the myocyte orientation of one ex-vivo human heart and demonstrate the feasibility of transferring the basis functions to humans.

Spatial integration during active tactile sensation drives orientation perception

Active haptic sensation is critical for object identification, but its neural circuit basis is poorly understood. We combined optogenetics, two-photon imaging, and high-speed behavioral tracking in mice solving a whisker-based object orientation discrimination task. We found that orientation discrimination required animals to summate input from multiple whiskers specifically along the whisker arc. Animals discriminated the orientation of the stimulus per se as their performance was invariant to the location of the presented stimulus. Populations of barrel cortex neurons summated across whiskers to encode each orientation. Finally, acute optogenetic inactivation of the barrel cortex and cell-type-specific optogenetic suppression of layer 4 excitatory neurons degraded performance, implying that infragranular layers alone are not sufficient to solve the task. These data suggest that spatial summation over an active haptic array generates representations of an object's orientation, which may facilitate encoding of complex three-dimensional objects during active exploration.

TELEMATIC SYNERGY OF MECHATRONIC SYSTEMS IN TRANSPORT APPLICATIONS

Problem. The results of fundamental and applied research of computer science and software engineering in industry and transport are considered. Informational socialization is offered to provide knowledge and skills to persons wishing to work in the IT industry. A kind of sensory cognitive representation of the spatial-temporal orientation of transport applications and synergy (performance assessment) of development, updating of applications, support of the semantic site of the transport-road portal of automobile transfer. Goal. Increasing the competitiveness of transport and road organizations by increasing the efficiency of virtual management of the car transfer, taking into account the virtual logistics and IT industry. Methodology. The idea and basic tasks of synergetic integration of the following technologies: WEB, Internet in general, distribution of computer resources of the client and server part of the Industry 4.0 Results and originality. The most useful in the synergy of WEB implementation is the use as a feature of a dynamic site aggregator WEB portal in defined as the location of vehicles and visualization of the relevant transport situation. Practical value.It is obtaining additional computer resources in the conditions of application of WEB, which is offered for implementation, does not require significant capital expenditures. Costs associated only with the assistance of this new work of existing staff - WEB users of these solutions. The implementation of these scientific and practical results shows that the greatest benefit from using the cloud infrastructure instead of supporting its own IT infrastructure is not even technological, but financials.

Representation-Free Model Predictive Control for Dynamic Motions in Quadrupeds

This paper presents a novel Representation-Free Model Predictive Control (RF-MPC) framework for controlling various dynamic motions of a quadrupedal robot in three dimensional (3D) space. Our formulation directly represents the rotational dynamics using the rotation matrix, which liberates us from the issues associated with the use of Euler angles and quaternion as the orientation representations. With a variation-based linearization scheme and a carefully constructed cost function, the MPC control law is transcribed to the standard Quadratic Program (QP) form. The MPC controller can operate at real-time rates of 250 Hz on a quadruped robot. Experimental results including periodic quadrupedal gaits and a controlled backflip validate that our control strategy could stabilize dynamic motions that involve singularity in 3D maneuvers.

Anisotropic representation of orientation by convolutional neural networks

Anisotropic representation of orientation by convolutional neural networks

Development and Reorganization of Orientation Representation in the Cat Visual Cortex: Experience-Dependent Synaptic Rewiring in Early Life.

To date, numerous mathematical models have been proposed on the basis of some types of Hebbian synaptic learning to account for the activity-dependent development of orientation maps as well as neuronal orientation selectivity. These models successfully reproduced orientation map-like spatial patterns. Nevertheless, we still have questions: (1) How does synaptic rewiring occur in the visual cortex during the formation of orderly orientation maps in early life? (2) How does visual experience contribute to the maturation of orientation selectivity of visual cortical neurons and reorganize orientation maps? (3) How does the sensitive period for orientation plasticity end? In this study, we performed animal experiments and mathematical modeling to understand the mechanisms underlying synaptic rewiring for experience-dependent formation and reorganization of orientation maps. At first, we visualized orientation maps from the intrinsic signal optical imaging in area 17 of kittens reared under single-orientation exposure through cylindrical-lens-fitted goggles. The experiments revealed that the degree of expansion of cortical domains representing the experienced orientation depends on the age at which the single-orientation exposure starts. As a result, we obtained the sensitive period profile for orientation plasticity. Next, we refined our previously proposed mathematical model for the activity-dependent self-organization of thalamo-cortical inputs on the assumption that rewiring is caused by the competitive interactions among transient synaptic contacts on the same dendritic spine. Although various kinds of molecules have been reported to be involved in such interactions, we attempt to build a mathematical model to describe synaptic rewiring focusing on brain-derived neurotrophic factor (BDNF) and its related molecules. Performing computer simulations based on the refined model, we successfully reproduced orientation maps reorganized in kittens reared under single-orientation exposure as well as normal visual experience. We also reproduced the experimentally obtained sensitive period profile for orientation plasticity. The excellent agreement between experimental observations and theoretical reproductions suggests that the BDNF-induced competitive interaction among synaptic contacts from different axons on the same spine is an important factor for the experience-dependent formation and reorganization of orientation selectivity and orientation maps.

Adaptation to feedback representation of illusory orientation produced from flash grab effect

Adaptation is a ubiquitous property of sensory systems. It is typically considered that neurons adapt to dominant energy in the ambient environment to function optimally. However, perceptual representation of the stimulus, often modulated by feedback signals, sometimes do not correspond to the input state of the stimulus, which tends to be more linked with feedforward signals. Here we investigated the relative contributions to cortical adaptation from feedforward and feedback signals, taking advantage of a visual illusion, the Flash-Grab Effect, to disassociate the feedforward and feedback representation of an adaptor. Results reveal that orientation adaptation is exclusively dependent on the perceived rather than the retinal orientation of the adaptor. Combined fMRI and EEG measurements demonstrate that the perceived orientation of the Flash-Grab Effect is indeed supported by feedback signals in the cortex. These findings highlight the important contribution of feedback signals for cortical neurons to recalibrate their sensitivity.

ВЫСШЕЕ ОБРАЗОВАНИЕ В СИСТЕМЕ РАЗВИТИЯ ЭКОЛОГИЧЕСКОГО ПОВЕДЕНИЯ РОССИЙСКОЙ МОЛОДЕЖИ

Objective of the study is to analyze the role of higher education in the development of environmental behavior of Russian youth. The methodological basis of the research is based on the survey methods of sociological research. Research results consist in the representation of emotional and value orientations in the formation of environmental behavior of students; the representation of the evaluation and activity level of the formation of environmental behavior of students; the typologization of environmental behavior of young people. Prospects of the study are connected with the further study of the specifics of the ecologization of the social behavior of students.

Trajectory Tracking for Aerial Robots: an Optimization-Based Planning and Control Approach

In this work, we present an optimization-based trajectory tracking solution for multirotor aerial robots given a geometrically feasible path. A trajectory planner generates a minimum-time kinematically and dynamically feasible trajectory that includes not only standard restrictions such as continuity and limits on the trajectory, constraints in the waypoints, and maximum distance between the planned trajectory and the given path, but also restrictions in the actuators of the aerial robot based on its dynamic model, guaranteeing that the planned trajectory is achievable. Our novel compact multi-phase trajectory definition, as a set of two different kinds of polynomials, provides a higher semantic encoding of the trajectory, which allows calculating an optimal solution but following a predefined simple profile. A Model Predictive Controller ensures that the planned trajectory is tracked by the aerial robot with the smallest deviation. Its novel formulation takes as inputs all the magnitudes of the planned trajectory (i.e. position and heading, velocity, and acceleration) to generate the control commands, demonstrating through in-lab real flights an improvement of the tracking performance when compared with a controller that only uses the planned position and heading. To support our optimization-based solution, we discuss the most commonly used representations of orientations, as well as both the difference as well as the scalar error between two rotations, in both tridimensional and bidimensional spaces SO(3) and SO(2). We demonstrate that quaternions and error-quaternions have some advantages when compared to other formulations.

Experience-Dependent Reorganization Drives Development of a Binocularly Unified Cortical Representation of Orientation

Across sensory areas, neural microcircuits consolidate streams of information into unified representations of the external world. In the carnivore visual cortex, where eye-specific inputs first converge, it has been posited that a single, binocularly aligned modular orientation representation develops independent of sensory experience. In this study of ferret visual cortex using invivo calcium imaging, we find evidence for a different developmental process. Early in development, contralateral, ipsilateral, or binocular stimulation each yield well-organized modular representations of orientation that display features of mature cortex. However, comparison of these representations reveals considerable misalignment that is evident at both modular and cellular scales. Experience-dependent processes drive reorganization of these three representations toward a single binocularly aligned representation resembling the early binocular representation through shifts in cellular orientation preference. Thus, while orderly modular networks of orientation preference initially arise independent of visual experience, experience is critical for the alignment of these early representations.

SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage

Feature description has an important role in image matching and is widely used for a variety of computer vision applications. As an efficient synthetic basis feature descriptor, SYnthetic BAsis (SYBA) requires low computational complexity and provides accurate matching results. However, the number of matched feature points generated by SYBA suffers from large image scaling and rotation variations. In this paper, we improve SYBA’s scale and rotation invariance by adding an efficient pre-processing operation. The proposed algorithm, SR-SYBA, represents the scale of the feature region with the location of maximum gradient response along the radial direction in Log-polar coordinate system. Based on this scale representation, it normalizes all feature regions to the same reference scale to provide scale invariance. The orientation of the feature region is represented as the orientation of the vector from the center of the feature region to its intensity centroid. Based on this orientation representation, all feature regions are rotated to the same reference orientation to provide rotation invariance. The original SYBA descriptor is then applied to the scale and orientation normalized feature regions for description and matching. Experiment results show that SR-SYBA greatly improves SYBA for image matching applications with scaling and rotation variations. SR-SYBA obtains comparable or better performance in terms of matching rate compared to the mainstream algorithms while still maintains its advantages of using much less storage and simpler computations. SR-SYBA is applied to a vision-based measurement application to demonstrate its performance for image matching.

Social identities, place, mobility, and belonging: Intersectional experiences of college-educated youth

Youth are commonly understood to be a crucial population for the future viability and vibrancy of rural communities. Thus, researchers often focus on the migration intentions and patterns of youth to better understand rural community sustainability and well-being. However, the role of social identities in young people's relationship to place and the perspectives of college students are not well-represented in the literature. This paper addresses these gaps by highlighting the intersectional qualities of social identities and place and how these intersections relate to college-educated young adults' perspectives on mobility. The paper is based on data from focus groups with college students and interviews with college-educated young adults living in the Appalachian region of the United States where many communities are in economic transition following long-term declines in the coal industry. The sample was purposefully built to include a diverse representation of genders, sexual orientations, and racial identities, and the data were analyzed through an intersectional lens in order to access information about the connections among social identities, place, mobility, and belonging. The data show that the navigation of mobility and social identities has the potential to strengthen young people's relationship with place despite limiting factors. Furthermore, experiences of mobility along with institutional support can help foster this connection. Overall, this paper argues that social identities are essential components for understanding the potentially limiting and liberating aspects of the mobility imperative in the context of metronormativity.

Alternative Representation of the Shoulder Orientation Based on the Tilt-and-Torsion Angles.

The International Society of Biomechanics (ISB) has proposed standardized recommendations for recording human joint motion. The Euler angles-the orientation representation currently proposed by the ISB-have two drawbacks, namely, the issue of singularities (gimbal lock) and the difficulty to obtain clinical and interpretable orientation representation for compound movements. The orientation representation of the shoulder joint with the Euler angles is particularly challenging due to its broad range of motion. This paper proposes and evaluates an alternative orientation representation for shoulder movement based on the tilt-and-torsion representation, a method that aims at providing a more clinically interpretable solution for describing joint movements compared to the standard Euler angles. Three studies were performed to compare the different orientation representation methods. The first two studies consist in simulations of arm elevation in different planes. The third study is an experiment using inertial-measurement-units with one test subject performing shoulder elevation movements in different planes. The tilt-and-torsion representation is then compared with different Euler angle conventions. The results show that Euler angles are biased or clinically uninterpretable for compound movements. Conversely, tilt-and-torsion representation does not suffer from these limitations. Although not extensive, the experiments suggest that the tilt-and-torsion representation has the potential to better represent human movements and provide more clinically interpretable results than the Euler angles.

A Hybrid Framework Based on Warped Hierarchical Tree for Pose Estimation of Texture-Less Objects

The pose of texture-less objects is very important for intelligent manufacturing and intelligent assembly. Existing methods cannot accurately estimate pose when partial features are missing or cluttered due to shading, reflection, and occlusion. We propose a hybrid framework based on the warped hierarchical tree for pose estimation which integrates template matching and sparse representation classification in this paper. Firstly, the template is formed by the prospectively cumulated orientation feature (PCOF), which is a probabilistic representation of orientations extracted from template images. And the warped hierarchical tree can be built offline according to the parameters for projecting the 3D object and the similarity between templates. Then the online searching can be repeated through the warped hierarchical tree until the template candidates have been found. Finally, the pose corresponding to the best-fitting template can be obtained by sparse representation classification based on the dictionaries consisted of the spreading orientations of template candidate images. The experiment results show the effectiveness of our method when partial features are missing or cluttered.

Polycrystalline Microstructure Reconstruction Using Markov Random Fields and Histogram Matching

A new numerical method is presented for reconstructing three-dimensional (3D) microstructures from two-dimensional (2D) sections, imaged on orthogonal planes, by exploiting the complete red–green–blue (RGB) color space. The algorithm reconstructs 3D models through sampling voxel neighborhoods to representative 2D micrographs, based upon a Markovian assumption. The sampling is followed by an optimization procedure, ensuring smoothness across the orthogonal sections of the synthesized voxels. Previous 3D Markov random field (MRF) microstructure reconstruction techniques were restricted to traditional grayscale images only. This method now enables the use of the entire RGB spectrum, employing a histogram matching step. This paper examines the algorithm’s accurate representation of orientations and morphologies, encompassing a variety of micrographs from electron backscatter diffraction (EBSD) and polarized light microscopy.

On the Orientation Average Based on Central Orientation Density Functions for Polycrystalline Materials

The present work continues the investigation first started by Lobos et al. (J. Elast. 128(1):17–60, 2017) concerning the orientation average of tensorial quantities connected to single-crystal physical quantities distributed in polycrystals. In Lobos et al. (J. Elast. 128(1):17–60, 2017), central orientation density functions were considered in the orientation average for fourth-order tensors with certain index symmetries belonging to single-crystal quantities. The present work generalizes the results of Lobos et al. (J. Elast. 128(1):17–60, 2017) for the orientation average of tensors of arbitrary order by presenting a clear connection to the Fourier expansion of central orientation density functions and of the general orientation density function in terms of tensorial texture coefficients. The closed form of the orientation average based on a central orientation density function is represented in terms of the Fourier coefficients (referred to as texture eigenvalues) and the central orientation of the central orientation density function. The given representation requires the computation of specific isotropic tensors. A pragmatic algorithm for the automated generation of a basis of isotropic tensors is given. Applications and examples are presented to show that the representation of the orientation average offers a low-dimensional parametrization with major benefits for optimization problems in materials science. A simple implementation in Python 3 for the reproduction of all examples is offered through the GitHub repository https://github.com/mauricio-fernandez-l/centralODF-average.

Competing rhythmic neural representations of orientations during concurrent attention to multiple orientation features

When a feature is attended, all locations containing this feature are enhanced throughout the visual field. However, how the brain concurrently attends to multiple features remains unknown and cannot be easily deduced from classical attention theories. Here, we recorded human magnetoencephalography signals when subjects concurrently attended to two spatially overlapping orientations. A time-resolved multivariate inverted encoding model was employed to track the ongoing temporal courses of the neural representations of the attended orientations. We show that the two orientation representations alternate with each other and undergo a theta-band (~4 Hz) rhythmic fluctuation over time. Similar temporal profiles are also revealed in the orientation discrimination performance. Computational modeling suggests a tuning competition process between the two neuronal populations that are selectively tuned to one of the attended orientations. Taken together, our findings reveal for the first time a rhythm-based, time-multiplexing neural machinery underlying concurrent multi-feature attention.

DeepIM: Deep Iterative Matching for 6D Pose Estimation

Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the observed image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.

The geometry of masking in neural populations

The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex. Understanding the implications of normalization at the population level is hindered by the heterogeneity of cortical neurons, which differ in the composition of their normalization pools and semi-saturation constants. Here we introduce a geometric approach to investigate contextual modulation in neural populations and study how the representation of stimulus orientation is transformed by the presence of a mask. We find that population responses can be embedded in a low-dimensional space and that an affine transform can account for the effects of masking. The geometric analysis further reveals a link between changes in discriminability and bias induced by the mask. We propose the geometric approach can yield new insights into the image processing computations taking place in early visual cortex at the population level while coping with the heterogeneity of single cell behavior.

Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Our pipeline achieves state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D domain. We also evaluate on the LineMOD dataset where we can compete with other synthetically trained approaches. We further increase performance by correcting 3D orientation estimates to account for perspective errors when the object deviates from the image center and show extended results. Our code is available here https://github.com/DLR-RM/AugmentedAutoencoder.