Representation of the Spatial Relationship Among Object Parts by Neurons in Macaque Inferotemporal Cortex

2006 ◽  
Vol 96 (6) ◽  
pp. 3147-3156 ◽  
Author(s):  
Yukako Yamane ◽  
Kazushige Tsunoda ◽  
Madoka Matsumoto ◽  
Adam N. Phillips ◽  
Manabu Tanifuji
Keyword(s):  
Object Representation ◽  
Spatial Configuration ◽  
Anterior Part ◽  
Spatial Relationship ◽  
Spatial Arrangement ◽  
Local Features ◽  
Visual Features ◽  
Global Features ◽  
Object Parts ◽  
Area Te

We investigated object representation in area TE, the anterior part of monkey inferotemporal (IT) cortex, with a combination of optical and extracellular recordings in anesthetized monkeys. We found neurons that respond to visual stimuli composed of naturally distinguishable parts. These neurons were sensitive to a particular spatial arrangement of parts but less sensitive to differences in local features within individual parts. Thus these neurons were activated when arbitrary local features were arranged in a particular spatial configuration, suggesting that they may be responsible for representing the spatial configuration of object images. Previously it has been reported that many neurons in area TE respond to visual features less complex than natural objects, but it has remained unclear whether these features are related to local features of object images or to more global features. These results indicate that TE neurons represent not only local features but also global features such as the spatial relationship among object parts.

The Activation of Scene Gist: Global Versus Local Features

2017 ◽  
Author(s):  
Laura Shields
Keyword(s):  
Real World ◽  
Visual Information ◽  
Scene Understanding ◽  
Past Research ◽  
Local Features ◽  
Visual Scene ◽  
Visual Features ◽  
Global Features ◽  
Experimental Conditions ◽  
Scene Gist

The present study investigates how human observers understand real-world scenes. Past studies have shown that individuals infer the meaning or gist of a real-world scene within a single glance. The current study examined how much visual information is needed in order to elicit an understanding of a visual scene. Sixty participants were shown a brief presentation of a scene and the amount of scene information shown was manipulated across six experimental conditions, varying from only details at the centre (local features) to the full scene (global features). Local features are objects present in a scene, or can also be visual features such as textures, colours and other surface properties important for understanding a visual scene. Global features ecompass the actual space of the scene, including the geometry, spatial layout, and scene structure. Based on past research, we anticipate scene understanding will occur where global features are available, but not in conditions where only local features are available. However, preliminary results revealed that participants understood the gist of the scene even when minimal features were available to them. This study aims to further current research on scene understanding and the visual features required to comprehend complex visual information in our environment.  

scholarly journals 3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

2019 ◽  
Author(s):  
Zhizhong Han ◽  
Xiyang Wang ◽  
Chi Man Vong ◽  
Yu-Shen Liu ◽  
Matthias Zwicker ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Large Scale ◽  
Spatial Information ◽  
Dimensional Space ◽  
Spatial Relationship ◽  
Global Features ◽  
Pattern Correlation ◽  
3D Shape Analysis ◽  
3D Shapes ◽  
Learned Features

Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling has been applied extensively. However, pooling leads to a loss of the content within views, and the spatial relationship among views, which limits the discriminability of learned features. We propose 3DViewGraph to resolve this issue, which learns 3D global features by more effectively aggregating unordered views with attention. Specifically, unordered views taken around a shape are regarded as view nodes on a view graph. 3DViewGraph first learns a novel latent semantic mapping to project low-level view features into meaningful latent semantic embeddings in a lower dimensional space, which is spanned by latent semantic patterns. Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns. Finally, all spatial pattern correlations are integrated with attention weights learned by a novel attention mechanism. This further increases the discriminability of learned features by highlighting the unordered view nodes with distinctive characteristics and depressing the ones with appearance ambiguity. We show that 3DViewGraph outperforms state-of-the-art methods under three large-scale benchmarks.

scholarly journals Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms

2020 ◽  
Vol 10 (12) ◽  
pp. 4312 ◽  
Author(s):  
Jie Xu ◽  
Haoliang Wei ◽  
Linke Li ◽  
Qiuru Fu ◽  
Jinhong Guo
Keyword(s):  
Neural Network ◽  
Spatial Attention ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Visual Features ◽  
Feature Maps ◽  
Global Features ◽  
Model Based ◽  
Video Description ◽  
Video Visualization

Video description plays an important role in the field of intelligent imaging technology. Attention perception mechanisms are extensively applied in video description models based on deep learning. Most existing models use a temporal-spatial attention mechanism to enhance the accuracy of models. Temporal attention mechanisms can obtain the global features of a video, whereas spatial attention mechanisms obtain local features. Nevertheless, because each channel of the convolutional neural network (CNN) feature maps has certain spatial semantic information, it is insufficient to merely divide the CNN features into regions and then apply a spatial attention mechanism. In this paper, we propose a temporal-spatial and channel attention mechanism that enables the model to take advantage of various video features and ensures the consistency of visual features between sentence descriptions to enhance the effect of the model. Meanwhile, in order to prove the effectiveness of the attention mechanism, this paper proposes a video visualization model based on the video description. Experimental results show that, our model has achieved good performance on the Microsoft Video Description (MSVD) dataset and a certain improvement on the Microsoft Research-Video to Text (MSR-VTT) dataset.

scholarly journals Spatial Learning with Orientation Maps: The Influence of Different Environmental Features on Spatial Knowledge Acquisition

2019 ◽  
Vol 8 (3) ◽  
pp. 149 ◽  
Author(s):  
Heinrich Löwen ◽  
Jakub Krukar ◽  
Angela Schwering
Keyword(s):  
Knowledge Acquisition ◽  
Spatial Learning ◽  
Driving Simulator ◽  
Local Features ◽  
Structural Features ◽  
Spatial Knowledge ◽  
Navigation Systems ◽  
Global Features ◽  
Environmental Features ◽  
Spatial Knowledge Acquisition

The prevalent use of GPS-based navigation systems impairs peoples’ ability to orient themselves. This paper investigates whether wayfinding maps that accentuate different types of environmental features support peoples’ spatial learning. A virtual-reality driving simulator was used to investigate spatial knowledge acquisition in assisted wayfinding tasks. Two main conditions of wayfinding maps were tested against a base condition: (i) highlighting local features, i.e., landmarks, along the route and at decision points; and (ii) highlighting structural features that provide global orientation. The results show that accentuating local features supports peoples’ acquisition of route knowledge, whereas accentuating global features supports peoples’ acquisition of survey knowledge. The results contribute to the general understanding of spatial knowledge acquisition in assisted wayfinding tasks. Future navigation systems could enhance spatial knowledge by providing visual navigation support incorporating not only landmarks but structural features in wayfinding maps.

scholarly journals Studies on Analogues of L-Cysteine and L-Cystine

Blood ◽  
1956 ◽  
Vol 11 (1) ◽  
pp. 1-10 ◽  
Author(s):  
AUSTIN S. WEISBERGER ◽  
LEIF G. SUHRLAND ◽  
JOSEPH SEIFTER
Keyword(s):  
Amino Acids ◽  
Spatial Configuration ◽  
Spatial Relationship ◽  
Inhibitory Effect ◽  
Selenium Compounds ◽  
Low Concentrations ◽  
Relationship Of ◽  
Normal Cellular Function

Abstract The amino acids L-cysteine and L-cystine appear to have an important role in the metabolism of leukocytes. Decreased availability of these amino acids may therefore have important effects on leukocytes. The possibility of decreasing the influx of radioactive L-cystine into leukemic leukocytes was investigated by exposing the leukocytes to various analogues of cysteine (cystine) prior to incubation with S35 L-cystine. It was found that a highly specific structural and spatial configuration is required to decrease the influx of S35 L-cystine. Thus unlabeled L-cysteine is effective in decreasing the incorporation of radioactive L-cystine. However, analogues of cystine in which there is modification or substitution of the sulfhydryl, amino or carboxyl group do not decrease the influx of S35 L-cystine. Furthermore, any alteration in the spatial relationship of the sulfhydryl and amino groups of L-cysteine also results in a loss of the ability of an analogue to decrease the incorporation of S35 L-cystine. Of the compounds studied and in the concentrations employed, only unlabeled L-cysteine, selenium cystine and phenyl selenium cysteine were effective. Selenium cystine is identical with cystine except that selenium replaces the sulfur in the molecule. Phenyl selenium cysteine is also closely related structurally to cysteine. The mechanism of action of selenium cystine and phenyl selenium cysteine in decreasing the influx of S35 L-cystine is not known. Other selenium compounds tested were ineffective. These compounds may exert their inhibitory effect by (a) competitive combination with specific intracellular receptors for L-cysteine (L-cystine), (b) inactivation of enzymes or compounds essential for normal cellular function, (c) alteration in membrane permeability or (d) a toxic effect of selenium. Since selenium cystine and phenyl selenium cystine are inhibitory in low concentrations in vitro, these compounds may have important effects on leukemic leukocytes in vivo.

Spatial organization of soil microaggregates

2020 ◽  
Author(s):  
Eva Lehndorff ◽  
Nele Meyer ◽  
Andrey Radionov ◽  
Lutz Plümmer ◽  
Peter Rottmann ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spatial Organization ◽  
Inorganic Compounds ◽  
Spatial Relationship ◽  
Clay Content ◽  
Spatial Arrangement ◽  
C Sequestration ◽  
Spatial Correlations ◽  
Soil Minerals

<p>The physical arrangement of soil compounds in microaggregates is important in many ways, e.g. by controlling soil stability and C sequestration. However, little is known about the spatial arrangement of organic and inorganic compounds in soil microaggregates, due to the lack of in-situ analyses in undisturbed material. Here we hypothesize that microaggregates are spatially organized, resulting in deterministic, predictable spatial patterns of different organic matter and mineral phases and that this organization depends on the abundance of specific phases such as on clay mineral content. We separated the water stable, occluded large and small microaggregate fractions from Ap horizons of a sequence of sandy to loamy Luvisols (19 to 35% clay, Scheyern, Germany) and subjected in total 60 individual aggregates to elemental mapping by electron probe micro analysis (EPMA), which recorded C, N, P, Al, Fe, Ca, K, Cl, and Si contents at µm scale resolution. Spatial arrangements of soil organic matter and soil minerals were extracted using cluster analyses. We found a pronounced heterogeneity in aggregate structure and composition, which was not reproducible and largely independent from clay content in soil. However, neighborhood analyses revealed close spatial correlations between organic matter debris (C:N app. 100:10) and microbial organic matter (C:N app. 10:1) indicating a spatial relationship between source and consumer. There was no systematic relationship between soil minerals and organic matter, suggesting that well-established macroscale correlations between contents of pedogenic oxides and clay minerals with soil organic matter storage do not apply to soil microaggregates.</p>

scholarly journals MAT-Net: Medial Axis Transform Network for 3D Object Recognition

2019 ◽  
Author(s):  
Jianwei Hu ◽  
Bin Wang ◽  
Lihui Qian ◽  
Yiling Pan ◽  
Xiaohu Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Representation ◽  
Nearest Neighbor ◽  
Medial Axis ◽  
Fixed Number ◽  
Learning Performance ◽  
Medial Axis Transform ◽  
K Nearest Neighbor ◽  
Global Features ◽  
Local Feature Extraction

3D deep learning performance depends on object representation and local feature extraction. In this work, we present MAT-Net, a neural network which captures local and global features from the Medial Axis Transform (MAT). Different from K-Nearest-Neighbor method which extracts local features by a fixed number of neighbors, our MAT-Net exploits effective modules Group-MAT and Edge-Net to process topological structure. Experimental results illustrate that MAT-Net demonstrates competitive or better performance on 3D shape recognition than state-of-the-art methods, and prove that MAT representation has excellent capacity in 3D deep learning, even in the case of low resolution.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions

2017 ◽  
Author(s):  
Janine D. Bijsterbosch ◽  
Mark W. Woolrich ◽  
Matthew F. Glasser ◽  
Emma C. Robinson ◽  
Christian F. Beckmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Spatial Arrangement ◽  
Brain Regions ◽  
Imaging Data ◽  
Functional Regions ◽  
Cortical Regions ◽  
The Relationship

AbstractBrain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used "functional connectivity fingerprints" to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

scholarly journals Landscape metrics as functional traits in plants: perspectives from a glacier foreland

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3552 ◽  
Author(s):  
Tommaso Sitzia ◽  
Matteo Dainese ◽  
Bertil O. Krüsi ◽  
Duncan McCollin
Keyword(s):  
Plant Species ◽  
Functional Traits ◽  
Landscape Metrics ◽  
Spatial Configuration ◽  
Leaf Size ◽  
Spatial Arrangement ◽  
Eastern Alps ◽  
Rlq Analysis ◽  
As Species ◽  
Species Specific

Spatial patterns of vegetation arise from an interplay of functional traits, environmental characteristics and chance. The retreat of glaciers offers exposed substrates which are colonised by plants forming distinct patchy patterns. The aim of this study was to unravel whether patch-level landscape metrics of plants can be treated as functional traits. We sampled 46 plots, each 1 m × 1 m, distributed along a restricted range of terrain age and topsoil texture on the foreland of the Nardis glacier, located in the South-Eastern Alps, Italy. Nine quantitative functional traits were selected for 16 of the plant species present, and seven landscape metrics were measured to describe the spatial arrangement of the plant species’ patches on the study plots, at a resolution of 1 cm × 1 cm. We studied the relationships among plant communities, landscape metrics, terrain age and topsoil texture. RLQ-analysis was used to examine trait-spatial configuration relationships. To assess the effect of terrain age and topsoil texture variation on trait performance, we applied a partial-RLQ analysis approach. Finally, we used the fourth-corner statistic to quantify and test relationships between traits, landscape metrics and RLQ axes. Floristically-defined relevé clusters differed significantly with regard to several landscape metrics. Diversity in patch types and size increased and patch size decreased with increasing canopy height, leaf size and weight. Moreover, more compact patch shapes were correlated with an increased capacity for the conservation of nutrients in leaves. Neither plant species composition nor any of the landscape metrics were found to differ amongst the three classes of terrain age or topsoil texture. We conclude that patch-level landscape metrics of plants can be treated as species-specific functional traits. We recommend that existing databases of functional traits should incorporate these type of data.

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