The Experts below are selected from a list of 24291 Experts worldwide ranked by ideXlab platform
Radoslaw Martin Cichy - One of the best experts on this subject based on the ideXlab platform.
-
dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks
NeuroImage, 2017Co-Authors: Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Aude OlivaAbstract:Abstract Human scene recognition is a rapid multistep process evolving over time from single scene image to Spatial Layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. Spatial Layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for Layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how Spatial Layout representations emerge in the human brain.
-
dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks
bioRxiv, 2015Co-Authors: Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Aude OlivaAbstract:Human scene recognition is a rapid multistep process evolving over time from single scene image to Spatial Layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. Spatial Layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative explanation that captures the complexity of scene recognition, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for Layout processing in humans, and a novel quantitative model of how Spatial Layout representations may emerge in the human brain.
Senjian An - One of the best experts on this subject based on the ideXlab platform.
-
a Spatial Layout and scale invariant feature representation for indoor scene classification
IEEE Transactions on Image Processing, 2016Co-Authors: Munawar Hayat, Salman H Khan, Mohammed Bennamoun, Senjian AnAbstract:Unlike standard object classification, where the image to be classified contains one or multiple instances of the same object, indoor scene classification is quite different since the image consists of multiple distinct objects. Furthermore, these objects can be of varying sizes and are present across numerous Spatial locations in different Layouts. For automatic indoor scene categorization, large-scale Spatial Layout deformations and scale variations are therefore two major challenges and the design of rich feature descriptors which are robust to these challenges is still an open problem. This paper introduces a new learnable feature descriptor called “Spatial Layout and scale invariant convolutional activations” to deal with these challenges. For this purpose, a new convolutional neural network architecture is designed which incorporates a novel “Spatially unstructured” layer to introduce robustness against Spatial Layout deformations. To achieve scale invariance, we present a pyramidal image representation. For feasible training of the proposed network for images of indoor scenes, this paper proposes a methodology, which efficiently adapts a trained network model (on a large-scale data) for our task with only a limited amount of available training data. The efficacy of the proposed approach is demonstrated through extensive experiments on a number of data sets, including MIT-67, Scene-15, Sports-8, Graz-02, and NYU data sets.
Aude Oliva - One of the best experts on this subject based on the ideXlab platform.
-
dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks
NeuroImage, 2017Co-Authors: Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Aude OlivaAbstract:Abstract Human scene recognition is a rapid multistep process evolving over time from single scene image to Spatial Layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. Spatial Layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for Layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how Spatial Layout representations emerge in the human brain.
-
dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks
bioRxiv, 2015Co-Authors: Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Aude OlivaAbstract:Human scene recognition is a rapid multistep process evolving over time from single scene image to Spatial Layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. Spatial Layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative explanation that captures the complexity of scene recognition, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for Layout processing in humans, and a novel quantitative model of how Spatial Layout representations may emerge in the human brain.
Jian Gong - One of the best experts on this subject based on the ideXlab platform.
-
human footprint in tibet assessing the Spatial Layout and effectiveness of nature reserves
Science of The Total Environment, 2018Co-Authors: Jian GongAbstract:Humanity is causing dramatic changes to the Earth, and we may be entering a human-dominated era referred to as the Anthropocene. Mapping the human footprint and assessing the Spatial Layout and effectiveness of protected areas facilitate sustainable development. As the core region of the third pole, Tibet is an important area for biodiversity and the provision of ecosystem services. In this study, five categories of human pressure were summed cumulatively to map the human footprint in Tibet for 1990 and 2010, and the Spatial relationship between the human footprint and national and provincial nature reserves (NRs) in Tibet was analyzed. In addition, the human footprint map was also used to evaluate the effectiveness of national and provincial NRs for reducing the impact of human activities. A comprehensive assessment was undertaken for the Yarlung Zangbo Grand Canyon (YZGC) NR. There were several key findings from this study. First, the human footprint scores (HFS) in Tibet for 1990 and 2010 were low, and increased by 32.35% during 1990-2010, which was greater than the global value of 9% for 1993-2009, indicating that Tibet is seriously threatened by human pressure. Grazing intensity and road disturbance intensity contributed significantly to the increase in the HFS. Second, the average HFS for 1990 in NRs was lower than that for the entire Tibet, but the Spatial Layout and extent of some reserves (e.g., the Qomolangma NR) needs to be optimized further. Third, the establishment of NRs in Tibet was effective in reducing human activities. No leakage phenomena were identified in the regions surrounding the YZGC reserve. However, the management of NRs in Tibet is still challenging in terms of reducing human activities.
Udo Ernst - One of the best experts on this subject based on the ideXlab platform.
-
modeling Spatial and temporal aspects of visual backward masking
Psychological Review, 2008Co-Authors: Frouke Hermens, Michael H Herzog, Gediminas Luksys, Wulfram Gerstner, Udo ErnstAbstract:Visual backward masking is a versatile tool for understanding principles and limitations of visual information processing in the human brain. However, the mechanisms underlying masking are still poorly understood. In the current contribution, the authors show that a structurally simple mathematical model can explain many Spatial and temporal effects in visual masking, such as Spatial Layout effects on pattern masking and B-type masking. Specifically, the authors show that lateral excitation and inhibition on different length scales, in combination with the typical time scales, are capable of producing a rich, dynamic behavior that explains this multitude of masking phenomena in a single, biophysically motivated model.
