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Machine Learning for Medical Image Reconstruction [[electronic resource] ] : Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye
| Machine Learning for Medical Image Reconstruction [[electronic resource] ] : Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (ix, 266 pages) |
| Disciplina | 610.28563 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Education—Data processing Application software Bioinformatics Optical data processing Health informatics Artificial Intelligence Computers and Education Computer Appl. in Social and Behavioral Sciences Computational Biology/Bioinformatics Image Processing and Computer Vision Health Informatics |
| ISBN | 3-030-33843-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction. |
| Record Nr. | UNISA-996466288703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning for Medical Image Reconstruction : Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye
| Machine Learning for Medical Image Reconstruction : Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (ix, 266 pages) |
| Disciplina |
610.28563
006.31 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Education—Data processing Application software Bioinformatics Optical data processing Medical informatics Artificial Intelligence Computers and Education Computer Appl. in Social and Behavioral Sciences Computational Biology/Bioinformatics Image Processing and Computer Vision Health Informatics |
| ISBN | 3-030-33843-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction. |
| Record Nr. | UNINA-9910349269003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning for Medical Image Reconstruction [[electronic resource] ] : First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert
| Machine Learning for Medical Image Reconstruction [[electronic resource] ] : First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (X, 158 p. 67 illus.) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Optical data processing Computer communication systems Logic design Health informatics Artificial Intelligence Image Processing and Computer Vision Computer Communication Networks Logic Design Health Informatics |
| ISBN | 3-030-00129-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep learning for magnetic resonance imaging -- Deep learning for computed tomography -- Deep learning for general image reconstruction. |
| Record Nr. | UNISA-996466192303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning for Medical Image Reconstruction : First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert
| Machine Learning for Medical Image Reconstruction : First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Florian Knoll, Andreas Maier, Daniel Rueckert |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (X, 158 p. 67 illus.) |
| Disciplina |
616.07540285
006.31 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Optical data processing Computer networks Logic design Medical informatics Artificial Intelligence Image Processing and Computer Vision Computer Communication Networks Logic Design Health Informatics |
| ISBN |
9783030001292
3030001296 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep learning for magnetic resonance imaging -- Deep learning for computed tomography -- Deep learning for general image reconstruction. |
| Record Nr. | UNINA-9910349407103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops : LDTM 2024, MMMI/ML4MHD 2024, ML-CDS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6–10, 2024, Proceedings / / edited by Anna Schroder, Xiang Li, Tanveer Syeda-Mahmood, Neil P. Oxtoby, Alexandra Young, Alessa Hering, Tejas S. Mathai, Pritam Mukherjee, Sven Kuckertz, Tiantian He, Isaac Llorente-Saguer, Andreas Maier, Satyananda Kashyap, Hayit Greenspan, Anant Madabhushi
| Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops : LDTM 2024, MMMI/ML4MHD 2024, ML-CDS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6–10, 2024, Proceedings / / edited by Anna Schroder, Xiang Li, Tanveer Syeda-Mahmood, Neil P. Oxtoby, Alexandra Young, Alessa Hering, Tejas S. Mathai, Pritam Mukherjee, Sven Kuckertz, Tiantian He, Isaac Llorente-Saguer, Andreas Maier, Satyananda Kashyap, Hayit Greenspan, Anant Madabhushi |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XIX, 262 p. 99 illus., 95 illus. in color.) |
| Disciplina | 006 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-84525-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | LDTM Workshop -- Disease Progression Modelling and Stratification for detecting sub-trajectories in the natural history of pathologies: application toParkinson’s Disease trajectory modelling -- Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series -- Individualized multi-horizon MRI trajectory prediction for Alzheimer’s Disease -- Toward, for the Alzheimer’s Disease Neuroimaging Initiative Towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling -- BachCuadraSegHeD: Segmentation of Heterogeneous Data for Multiple SclerosisLesions with Anatomical Constraints -- Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting -- Registration of Longitudinal Liver Examinations for Tumor ProgressAssessment -- Tracking lesion evolution using a Boundary Enhanced Approach for MS change segmentation (BEAMS) -- A Radiological-based Coordinate System for the Human Body: A Proof-of-Concept -- MMMI-ML4MHD Workshop -- Language Models Meet Anomaly Detection for Better Interpretabilityand Generalizability -- A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation -- Predicting Human Brain States with Transformer -- Modality Image Quality Prediction for Time-Resolved CT fromBreathing Signals -- RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs -- HyperMM : Robust Multimodal Learning with Varying-sized Inputs -- EMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator -- Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis -- ML-CDS Workshop -- MedPromptX: Grounded Multimodal Prompting for Chest X-rayDiagnosis -- Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning -- Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Resultsfrom a Novel Dataset and Deep-learning Experiments -- Multimodality Frequency Feature Customized Learning for PediatricVentricular Septal Defects Identification. |
| Record Nr. | UNINA-9910996484603321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops : LDTM 2024, MMMI/ML4MHD 2024, ML-CDS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6–10, 2024, Proceedings / / edited by Anna Schroder, Xiang Li, Tanveer Syeda-Mahmood, Neil P. Oxtoby, Alexandra Young, Alessa Hering, Tejas S. Mathai, Pritam Mukherjee, Sven Kuckertz, Tiantian He, Isaac Llorente-Saguer, Andreas Maier, Satyananda Kashyap, Hayit Greenspan, Anant Madabhushi
| Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops : LDTM 2024, MMMI/ML4MHD 2024, ML-CDS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6–10, 2024, Proceedings / / edited by Anna Schroder, Xiang Li, Tanveer Syeda-Mahmood, Neil P. Oxtoby, Alexandra Young, Alessa Hering, Tejas S. Mathai, Pritam Mukherjee, Sven Kuckertz, Tiantian He, Isaac Llorente-Saguer, Andreas Maier, Satyananda Kashyap, Hayit Greenspan, Anant Madabhushi |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XIX, 262 p. 99 illus., 95 illus. in color.) |
| Disciplina | 006 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-84525-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | LDTM Workshop -- Disease Progression Modelling and Stratification for detecting sub-trajectories in the natural history of pathologies: application toParkinson’s Disease trajectory modelling -- Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series -- Individualized multi-horizon MRI trajectory prediction for Alzheimer’s Disease -- Toward, for the Alzheimer’s Disease Neuroimaging Initiative Towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling -- BachCuadraSegHeD: Segmentation of Heterogeneous Data for Multiple SclerosisLesions with Anatomical Constraints -- Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting -- Registration of Longitudinal Liver Examinations for Tumor ProgressAssessment -- Tracking lesion evolution using a Boundary Enhanced Approach for MS change segmentation (BEAMS) -- A Radiological-based Coordinate System for the Human Body: A Proof-of-Concept -- MMMI-ML4MHD Workshop -- Language Models Meet Anomaly Detection for Better Interpretabilityand Generalizability -- A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation -- Predicting Human Brain States with Transformer -- Modality Image Quality Prediction for Time-Resolved CT fromBreathing Signals -- RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs -- HyperMM : Robust Multimodal Learning with Varying-sized Inputs -- EMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator -- Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis -- ML-CDS Workshop -- MedPromptX: Grounded Multimodal Prompting for Chest X-rayDiagnosis -- Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning -- Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Resultsfrom a Novel Dataset and Deep-learning Experiments -- Multimodality Frequency Feature Customized Learning for PediatricVentricular Septal Defects Identification. |
| Record Nr. | UNISA-996655268903316 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical Imaging Systems [[electronic resource] ] : An Introductory Guide / / edited by Andreas Maier, Stefan Steidl, Vincent Christlein, Joachim Hornegger
| Medical Imaging Systems [[electronic resource] ] : An Introductory Guide / / edited by Andreas Maier, Stefan Steidl, Vincent Christlein, Joachim Hornegger |
| Autore | Maier Andreas |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Springer Nature, 2018 |
| Descrizione fisica | 1 online resource (X, 259 p. 167 illus.) |
| Disciplina | 006.6 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Computer Imaging, Vision, Pattern Recognition and Graphics |
| Soggetto non controllato |
Computer science
Optical data processing |
| ISBN | 3-319-96520-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- System Theory -- Image Processing -- Endoscopy -- Microscopy -- Magnetic Resonance Imaging -- X-ray Imaging -- Computed Tomography -- X-ray Phase Contrast: Research on a Future Imaging Modality -- Emission Tomography -- Ultrasound -- Optical Coherence Tomography -- Acronyms. . |
| Record Nr. | UNISA-996466308003316 |
Maier Andreas
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| Springer Nature, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical Imaging Systems : An Introductory Guide / / edited by Andreas Maier, Stefan Steidl, Vincent Christlein, Joachim Hornegger
| Medical Imaging Systems : An Introductory Guide / / edited by Andreas Maier, Stefan Steidl, Vincent Christlein, Joachim Hornegger |
| Autore | Maier Andreas |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Springer Nature, 2018 |
| Descrizione fisica | 1 online resource (X, 259 p. 167 illus.) |
| Disciplina | 006.6 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN |
9783319965208
3319965204 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- System Theory -- Image Processing -- Endoscopy -- Microscopy -- Magnetic Resonance Imaging -- X-ray Imaging -- Computed Tomography -- X-ray Phase Contrast: Research on a Future Imaging Modality -- Emission Tomography -- Ultrasound -- Optical Coherence Tomography -- Acronyms. . |
| Record Nr. | UNINA-9910349415703321 |
|
Maier Andreas
|
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| Springer Nature, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Scale Matters : The Quality of Quantity in Human Culture and Sociality / / ed. by M. Dores Cruz, Thomas Widlok
| Scale Matters : The Quality of Quantity in Human Culture and Sociality / / ed. by M. Dores Cruz, Thomas Widlok |
| Pubbl/distr/stampa | Bielefeld : , : transcript Verlag, , [2022] |
| Descrizione fisica | 1 online resource (232 p.) |
| Collana | Edition Kulturwissenschaft |
| Soggetto topico | SOCIAL SCIENCE / Popular Culture |
| Soggetto non controllato |
Cultural Anthropology
Cultural Complexity Cultural Studies Cultural Theory Culture Ethnic Groups Ethnology Hunter-Gatherer Studies Science Social Relations Sociality Sociology of Science |
| ISBN | 3-8394-6099-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Introduction: Why scale matters -- How do we scale hunter-gatherers’ social networks? -- What good is archaeology? -- Upscaling forager mobility and broadening forager relations -- Scales of interaction -- A large-scale view on ‘small-scale societies’ -- Socioecological factors influence hunter-gatherers -- Scale and Inuit social relations -- Mikea, Malagasy, or hunter-gatherers? -- Scaling an island of hunter-gatherers -- Authors’ biographies |
| Record Nr. | UNISA-996478968603316 |
| Bielefeld : , : transcript Verlag, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Scale Matters : The Quality of Quantity in Human Culture and Sociality / / ed. by M. Dores Cruz, Thomas Widlok
| Scale Matters : The Quality of Quantity in Human Culture and Sociality / / ed. by M. Dores Cruz, Thomas Widlok |
| Pubbl/distr/stampa | Bielefeld : , : transcript Verlag, , [2022] |
| Descrizione fisica | 1 online resource (232 p.) |
| Disciplina | 306 |
| Collana | Edition Kulturwissenschaft |
| Soggetto topico | SOCIAL SCIENCE / Popular Culture |
| Soggetto non controllato |
Cultural Anthropology
Cultural Complexity Cultural Studies Cultural Theory Culture Ethnic Groups Ethnology Hunter-Gatherer Studies Science Social Relations Sociality Sociology of Science |
| ISBN |
9783839460993
3839460999 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Introduction: Why scale matters -- How do we scale hunter-gatherers’ social networks? -- What good is archaeology? -- Upscaling forager mobility and broadening forager relations -- Scales of interaction -- A large-scale view on ‘small-scale societies’ -- Socioecological factors influence hunter-gatherers -- Scale and Inuit social relations -- Mikea, Malagasy, or hunter-gatherers? -- Scaling an island of hunter-gatherers -- Authors’ biographies |
| Record Nr. | UNINA-9910831813603321 |
| Bielefeld : , : transcript Verlag, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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