scholarly journals A Riemannian Geometry Theory of Three-Dimensional Binocular Visual Perception

Vision ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 43
Author(s):  
Peter Neilson ◽  
Megan Neilson ◽  
Robin Bye
Keyword(s):  
Riemannian Geometry ◽  
Vector Bundles ◽  
Three Dimensional ◽  
Geometric Theory ◽  
Visual Space ◽  
Human Movement ◽  
Image Features ◽  
Covariant Derivatives ◽  
Christoffel Symbols ◽  
Distance Relationship

We present a Riemannian geometry theory to examine the systematically warped geometry of perceived visual space attributable to the size–distance relationship of retinal images associated with the optics of the human eye. Starting with the notion of a vector field of retinal image features over cortical hypercolumns endowed with a metric compatible with that size–distance relationship, we use Riemannian geometry to construct a place-encoded theory of spatial representation within the human visual system. The theory draws on the concepts of geodesic spray fields, covariant derivatives, geodesics, Christoffel symbols, curvature tensors, vector bundles and fibre bundles to produce a neurally-feasible geometric theory of visuospatial memory. The characteristics of perceived 3D visual space are examined by means of a series of simulations around the egocentre. Perceptions of size and shape are elucidated by the geometry as are the removal of occlusions and the generation of 3D images of objects. Predictions of the theory are compared with experimental observations in the literature. We hold that the variety of reported geometries is accounted for by cognitive perturbations of the invariant physically-determined geometry derived here. When combined with previous description of the Riemannian geometry of human movement this work promises to account for the non-linear dynamical invertible visual-proprioceptive maps and selection of task-compatible movement synergies required for the planning and execution of visuomotor tasks.

scholarly journals A Riemannian Geometry Theory of Synergy Selection for Visually-Guided Movement

Vision ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 26
Author(s):  
Peter D. Neilson ◽  
Megan D. Neilson ◽  
Robin T. Bye
Keyword(s):  
Riemannian Geometry ◽  
Degrees Of Freedom ◽  
Geometric Theory ◽  
Visual Space ◽  
Human Movement ◽  
The Body ◽  
Body Parts ◽  
Visually Guided ◽  
Visuomotor Task ◽  
Low Dimensional

Bringing together a Riemannian geometry account of visual space with a complementary account of human movement synergies we present a neurally-feasible computational formulation of visuomotor task performance. This cohesive geometric theory addresses inherent nonlinear complications underlying the match between a visual goal and an optimal action to achieve that goal: (i) the warped geometry of visual space causes the position, size, outline, curvature, velocity and acceleration of images to change with changes in the place and orientation of the head, (ii) the relationship between head place and body posture is ill-defined, and (iii) mass-inertia loads on muscles vary with body configuration and affect the planning of minimum-effort movement. We describe a partitioned visuospatial memory consisting of the warped posture-and-place-encoded images of the environment, including images of visible body parts. We depict synergies as low-dimensional submanifolds embedded in the warped posture-and-place manifold of the body. A task-appropriate synergy corresponds to a submanifold containing those postures and places that match the posture-and-place-encoded visual images that encompass the required visual goal. We set out a reinforcement learning process that tunes an error-reducing association memory network to minimize any mismatch, thereby coupling visual goals with compatible movement synergies. A simulation of a two-degrees-of-freedom arm illustrates that, despite warping of both visual space and posture space, there exists a smooth one-to-one and onto invertible mapping between vision and proprioception.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

scholarly journals Filtering Biomechanical Signals in Movement Analysis

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4580
Author(s):  
Francesco Crenna ◽  
Giovanni Battista Rossi ◽  
Marta Berardengo
Keyword(s):  
Measurement Uncertainty ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Movement Analysis ◽  
Analytical Signal ◽  
Human Movement ◽  
Reference Points ◽  
Biomechanical Analysis ◽  
Method Performance ◽  
Acceleration Signal

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.

scholarly journals Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5136
Author(s):  
Xiaoxin Fang ◽  
Qiwu Luo ◽  
Bingxing Zhou ◽  
Congcong Li ◽  
Lu Tian
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Three Dimensional ◽  
Image Features ◽  
Stereoscopic Vision ◽  
Research Progress ◽  
Inspection System ◽  
Two Dimensional ◽  
Surface Defect Detection ◽  
Planar Materials

The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

scholarly journals Latent 3D Volume for Joint Depth Estimation and Semantic Segmentation from a Single Image

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5765 ◽  
Author(s):  
Seiya Ito ◽  
Naoshi Kaneko ◽  
Kazuhiko Sumi
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Image Features ◽  
Feature Representation ◽  
Single Image ◽  
Feature Vectors ◽  
3D Space ◽  
3D Volume

This paper proposes a novel 3D representation, namely, a latent 3D volume, for joint depth estimation and semantic segmentation. Most previous studies encoded an input scene (typically given as a 2D image) into a set of feature vectors arranged over a 2D plane. However, considering the real world is three-dimensional, this 2D arrangement reduces one dimension and may limit the capacity of feature representation. In contrast, we examine the idea of arranging the feature vectors in 3D space rather than in a 2D plane. We refer to this 3D volumetric arrangement as a latent 3D volume. We will show that the latent 3D volume is beneficial to the tasks of depth estimation and semantic segmentation because these tasks require an understanding of the 3D structure of the scene. Our network first constructs an initial 3D volume using image features and then generates latent 3D volume by passing the initial 3D volume through several 3D convolutional layers. We apply depth regression and semantic segmentation by projecting the latent 3D volume onto a 2D plane. The evaluation results show that our method outperforms previous approaches on the NYU Depth v2 dataset.

scholarly journals The three-dimensional lattice as an architectural space

2018 ◽  
Vol 212 ◽  
pp. 04005
Author(s):  
Andrey Bolshakov
Keyword(s):  
Three Dimensional ◽  
Human Movement ◽  
Theoretical Understanding ◽  
Architectural Space ◽  
Spatial Lattice ◽  
Spatial Grids ◽  
Relationship Of ◽  
The Relationship ◽  
Main Material

Space is the main material with which the architect works. The space organized by the means of architecture is an art environment for the life of society. With many aspects of the organization of space, which are studied in the literature, the problem of their assembly and integration remains unsolved. The paper proposes a method of assembling spatial representations in architecture-the correlation of the spatial lattice and the factors of its form-formation, considered in the system, i.e. together. The approach is that in a broad overview of the world architecture, both in its theory and in practice, from historical to modern, examples of modification of spatial grids under the influence of one or a group of dominant factors are revealed. As a result, provisions on the relationship of the geometry of spatial grids with the following factors have been revealed: publicity and privacy; architectonics, the influence of the lattice on the differentiation of the streams of human movement; the architecture’s ability to carry a message; navigation properties, evaluation of connectivity and centrality of places in the spatial lattice; evaluation of the quality of the composition through the identification of ways of order and randomness in the elements of the spatial lattice. The results of the work can be used both for theoretical understanding of the architectural space in the study and design of architecture, and in architectural education.

A Conceptual Design of a Measuring System of Tennis Batting Motion Attitude Based on MEMS Sensors

2012 ◽  
Vol 198-199 ◽  
pp. 1053-1056
Author(s):  
Liang Han ◽  
Jing Song Jin ◽  
Wei Zhang
Keyword(s):  
Measurement System ◽  
Position Control ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Human Movement ◽  
Sports Injury ◽  
Measuring System ◽  
Attitude Measurement ◽  
Instructional Role ◽  
System Composition

Tennis is a very elegant sport, with a strong sense of competitiveness and appreciation, which has gained more and more attentions in our country, and it tends to be a fashion. This project is to achieve the measurement of tennis batting motion attitude in three dimensional space using a combination of the three-axis MEMS(Micro-electromechanical Systems) sensors, and make research on the principle of measurement system, composition and data acquisition. Body posture measurement system is to measure the attitude measurement in human movement, it can be applied to study the movement posture or to meet the requirements of position control, which provides theoretical foundation for scientific training and prevention of sports injury and also plays a significant and instructional role in improving the training levels of tennis playing and generalizing nationwide fitness campaign.

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