The Experts below are selected from a list of 13317 Experts worldwide ranked by ideXlab platform
George E Atwood - One of the best experts on this subject based on the ideXlab platform.
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experiencing Selfhood is not a self
International Journal of Psychoanalytic Self Psychology, 2016Co-Authors: Robert D Stolorow, George E AtwoodAbstract:Kohut’s lasting and most important contribution to psychoanalytic clinical theory was his recognition that the experiencing of Selfhood is always constituted, both developmentally and in psychoanalytic treatment, in a context of emotional interrelatedness. The experiencing of Selfhood, he realized, or of its collapse, is context-embedded through and through. The theoretical language of self psychology with its noun, “the self,” reifies the experiencing of Selfhood and transforms it into a metaphysical entity with thing-like properties, in effect undoing Kohut’s hard-won clinical contextualizations. The language of such decontextualizing objectifications bewitches intelligence in order to evade the tragic dimension of finite human existing.
Shihong Du - One of the best experts on this subject based on the ideXlab platform.
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Learning Self-Adaptive Scales for Extracting Urban Functional Zones From Very-High-Resolution Satellite Images
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Xiuyuan Zhang, Shihong DuAbstract:Urban functional zones (e.g. commercial, residential, and industrial) are basic units for city planning and management, and play an important role in city studies. However, functional zones are difficult to extract from very-high-resolution (VHR) remote sensing images, as they are various in components, sizes, and heterogeneities, leading to different segmentation scales. To resolve this issue, this study uses Selfhood scale, a local optimum scale, to extract functional zones. Firstly, geoscene segmentation is used to delineate functional zones at multiple scales. Then, Selfhood scales are calculated to measure the local optimum scales of segmenting functional zones, based on which multiscale segmentation results can be finally assembled into one layer to generate functional-zone boundaries. The experimental results indicate this method that is effective to delineate functional zones in Beijing, adapting to local built environments.
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learning Selfhood scales for urban land cover mapping with very high resolution satellite images
Remote Sensing of Environment, 2016Co-Authors: Xiuyuan Zhang, Shihong DuAbstract:Abstract Urban land cover mapping with very-high-resolution (VHR) satellite images has raised many concerns in the fields of environmental and social investigations, but the classical per-pixel and object-based mapping results are not accurate enough to serve these applications, owing to the inappropriate analysis scales. Accordingly, this study aims to 1) propose a self-adaptive segmentation scale (i.e., “Selfhood scale”), which refers to the optimum scale for analyzing a pixel and depends on the pixel's category and surrounding contrasts; and 2) apply Selfhood scales to urban land cover mapping. For the first target, a learning mechanism is presented to estimate Selfhood scales; for the second target, two methods (i.e., “zipper merging” and “restricted forest”) using the learned Selfhood scales are proposed to respectively improve per-pixel and object-based classification results. The experimental results demonstrate that these methods achieve significant improvements in both per-pixel and object-based land cover mapping in urban areas as their overall accuracies are improved by 2.8% and 7.6% respectively. Moreover, the proposed methods are further used to generate land cover maps in Beijing and Zhuhai cities, which perform much better than the classical methods. Accordingly, it can be concluded that Selfhood scales are effective to improve land cover mapping in urban areas.
Robert D Stolorow - One of the best experts on this subject based on the ideXlab platform.
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experiencing Selfhood is not a self
International Journal of Psychoanalytic Self Psychology, 2016Co-Authors: Robert D Stolorow, George E AtwoodAbstract:Kohut’s lasting and most important contribution to psychoanalytic clinical theory was his recognition that the experiencing of Selfhood is always constituted, both developmentally and in psychoanalytic treatment, in a context of emotional interrelatedness. The experiencing of Selfhood, he realized, or of its collapse, is context-embedded through and through. The theoretical language of self psychology with its noun, “the self,” reifies the experiencing of Selfhood and transforms it into a metaphysical entity with thing-like properties, in effect undoing Kohut’s hard-won clinical contextualizations. The language of such decontextualizing objectifications bewitches intelligence in order to evade the tragic dimension of finite human existing.
Nils J Nilsson - One of the best experts on this subject based on the ideXlab platform.
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commentary on david sahner s human consciousness and Selfhood
Journal of Consciousness Exploration & Research, 2011Co-Authors: Nils J NilssonAbstract:This is my brief Commentary on David Sahner’s “Human Consciousness and Selfhood: Potential Underpinnings and Compatibility with Artificial Complex Systems” in recent issue of JCER. My main point is that if a rich sensorium and extensive experiences are required for consciousness, machines will have, at least, those necessary conditions no less than humans do.
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Commentary on David Sahner’s “Human Consciousness and Selfhood”
Journal of Consciousness Exploration & Research, 2011Co-Authors: Nils J NilssonAbstract:This is my brief Commentary on David Sahner’s “Human Consciousness and Selfhood: Potential Underpinnings and Compatibility with Artificial Complex Systems” in recent issue of JCER. My main point is that if a rich sensorium and extensive experiences are required for consciousness, machines will have, at least, those necessary conditions no less than humans do.
Xiuyuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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Learning Self-Adaptive Scales for Extracting Urban Functional Zones From Very-High-Resolution Satellite Images
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Xiuyuan Zhang, Shihong DuAbstract:Urban functional zones (e.g. commercial, residential, and industrial) are basic units for city planning and management, and play an important role in city studies. However, functional zones are difficult to extract from very-high-resolution (VHR) remote sensing images, as they are various in components, sizes, and heterogeneities, leading to different segmentation scales. To resolve this issue, this study uses Selfhood scale, a local optimum scale, to extract functional zones. Firstly, geoscene segmentation is used to delineate functional zones at multiple scales. Then, Selfhood scales are calculated to measure the local optimum scales of segmenting functional zones, based on which multiscale segmentation results can be finally assembled into one layer to generate functional-zone boundaries. The experimental results indicate this method that is effective to delineate functional zones in Beijing, adapting to local built environments.
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learning Selfhood scales for urban land cover mapping with very high resolution satellite images
Remote Sensing of Environment, 2016Co-Authors: Xiuyuan Zhang, Shihong DuAbstract:Abstract Urban land cover mapping with very-high-resolution (VHR) satellite images has raised many concerns in the fields of environmental and social investigations, but the classical per-pixel and object-based mapping results are not accurate enough to serve these applications, owing to the inappropriate analysis scales. Accordingly, this study aims to 1) propose a self-adaptive segmentation scale (i.e., “Selfhood scale”), which refers to the optimum scale for analyzing a pixel and depends on the pixel's category and surrounding contrasts; and 2) apply Selfhood scales to urban land cover mapping. For the first target, a learning mechanism is presented to estimate Selfhood scales; for the second target, two methods (i.e., “zipper merging” and “restricted forest”) using the learned Selfhood scales are proposed to respectively improve per-pixel and object-based classification results. The experimental results demonstrate that these methods achieve significant improvements in both per-pixel and object-based land cover mapping in urban areas as their overall accuracies are improved by 2.8% and 7.6% respectively. Moreover, the proposed methods are further used to generate land cover maps in Beijing and Zhuhai cities, which perform much better than the classical methods. Accordingly, it can be concluded that Selfhood scales are effective to improve land cover mapping in urban areas.
