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Research data keyboard_double_arrow_right Dataset 2020 United KingdomPublisher:The University of Sheffield Funded by:UKRI | RoboPatient - Robot assis..., UKRI | Morphological computation...UKRI| RoboPatient - Robot assisted learning of constrained haptic information gain ,UKRI| Morphological computation of perception and actionHerzig, Nicolas; He, Liang; Maiolino, Perla; Guaman, Sara Abad; Nanayakkara, Thrishantha;These data are complementing the following publication: [1] N. Herzig, L. He, P. Maiolino, S-A Abad, and T. Nanayakkara, Conditioned Haptic Perception for 3D localization of Nodules in Soft Tissue Palpation with a Variable Stiffness Probe. PLoS One. DOI: 10.1371/journal.pone.0237379 These data support our research on a Variable Stiffness Palpation Probe and its control strategy to palpate and detect the location of stiff inclusions in soft tissues. The folder contains a ReadMe file and a binary Matlab file. For more details about the content of the binary file and the data structure, please read the ReadMe file. These data are complementing the following publication: [1] N. Herzig, L. He, P. Maiolino, S-A Abad, and T. Nanayakkara, Conditioned Haptic Perception for 3D localization of Nodules in Soft Tissue Palpation with a Variable Stiffness Probe. PLoS One. DOI: 10.1371/journal.pone.0237379 These data support our research on a Variable Stiffness Palpation Probe and its control strategy to palpate and detect the location of stiff inclusions in soft tissues. The folder contains a ReadMe file and a binary Matlab file. For more details about the content of the binary file and the data structure, please read the ReadMe file.
Spiral - Imperial Co... arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2020License: CC BYData sources: Spiral - Imperial College Digital RepositoryORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 6visibility views 6 Powered bymore_vert Spiral - Imperial Co... arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2020License: CC BYData sources: Spiral - Imperial College Digital RepositoryORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018 United Kingdom Funded by:EC | PALEC| PALAuthors: Fischer, Tobias; Chang, Hyung Jin; Demiris, Yiannis;Fischer, Tobias; Chang, Hyung Jin; Demiris, Yiannis;Overview The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix _glasses), and 2) One with natural appearances (no eyetracking glasses are worn; suffix _noglasses). The _noglasses images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the _glasses images. There is code accompanying this dataset: https://github.com/Tobias-Fischer/rt_gene. Please use the issue tracker in the code respository if you have questions regarding the dataset. Subjects / 3-Fold evaluation 15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used). We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups): 's001', 's002', 's008', 's010' 's003', 's004', 's007', 's009' 's005', 's006', 's011', 's012', 's013' The validation set consists of sessions 's014', 's015' and 's016'. While the MATLAB script (prepare_dataset.m; see code repository) creates train and test images for each subject, all images were used for the evaluation (see evaluate_model.py). Labeled dataset (sXYZ_glasses) The file for each subject contains the following information: label_combined.txt This is the main file containing labels. The formatting is as follows: seq_number, [head pose: down(pos) / up (neg), left(pos) / right(neg)], [gaze: up(pos) / down(neg), right(pos) / left(neg)], timestamp label_headpose.txt This file contains more detail about the head pose of the subject. seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp kinect2_calibration.yaml The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "original" folder The face_before_inpainting folder contains the face with a large margin to the left and right. The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder. The overlay folder contains images where the mask was overlaid on the face_before_inpainting images. The face folder contains the face image extracted using MTCNN with a tighter margin. The left and right folders contain the left and right eye image areas. The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting). "inpainted" folder The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting. Then, the images contained in the face, left and right folders were extracted using MTCNN as above. Unlabeled dataset (sXYZ_noglasses) kinect2_calibration.yaml This file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "face" folder This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn). Overview The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix _glasses), and 2) One with natural appearances (no eyetracking glasses are worn; suffix _noglasses). The _noglasses images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the _glasses images. There is code accompanying this dataset: https://github.com/Tobias-Fischer/rt_gene. Please use the issue tracker in the code respository if you have questions regarding the dataset. Subjects / 3-Fold evaluation 15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used). We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups): 's001', 's002', 's008', 's010' 's003', 's004', 's007', 's009' 's005', 's006', 's011', 's012', 's013' The validation set consists of sessions 's014', 's015' and 's016'. While the MATLAB script (prepare_dataset.m; see code repository) creates train and test images for each subject, all images were used for the evaluation (see evaluate_model.py). Labeled dataset (sXYZ_glasses) The file for each subject contains the following information: label_combined.txt This is the main file containing labels. The formatting is as follows: seq_number, [head pose: down(pos) / up (neg), left(pos) / right(neg)], [gaze: up(pos) / down(neg), right(pos) / left(neg)], timestamp label_headpose.txt This file contains more detail about the head pose of the subject. seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp kinect2_calibration.yaml The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "original" folder The face_before_inpainting folder contains the face with a large margin to the left and right. The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder. The overlay folder contains images where the mask was overlaid on the face_before_inpainting images. The face folder contains the face image extracted using MTCNN with a tighter margin. The left and right folders contain the left and right eye image areas. The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting). "inpainted" folder The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting. Then, the images contained in the face, left and right folders were extracted using MTCNN as above. Unlabeled dataset (sXYZ_noglasses) kinect2_calibration.yaml This file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "face" folder This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn).
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert ZENODO arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2018License: CC BY NC SAData sources: Spiral - Imperial College Digital RepositoryAll Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=od______2659::8d976af93c74c3da308bdf856c3f9b67&type=result"></script>'); --> </script>
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Research data keyboard_double_arrow_right Dataset 2020 United KingdomPublisher:The University of Sheffield Funded by:UKRI | RoboPatient - Robot assis..., UKRI | Morphological computation...UKRI| RoboPatient - Robot assisted learning of constrained haptic information gain ,UKRI| Morphological computation of perception and actionHerzig, Nicolas; He, Liang; Maiolino, Perla; Guaman, Sara Abad; Nanayakkara, Thrishantha;These data are complementing the following publication: [1] N. Herzig, L. He, P. Maiolino, S-A Abad, and T. Nanayakkara, Conditioned Haptic Perception for 3D localization of Nodules in Soft Tissue Palpation with a Variable Stiffness Probe. PLoS One. DOI: 10.1371/journal.pone.0237379 These data support our research on a Variable Stiffness Palpation Probe and its control strategy to palpate and detect the location of stiff inclusions in soft tissues. The folder contains a ReadMe file and a binary Matlab file. For more details about the content of the binary file and the data structure, please read the ReadMe file. These data are complementing the following publication: [1] N. Herzig, L. He, P. Maiolino, S-A Abad, and T. Nanayakkara, Conditioned Haptic Perception for 3D localization of Nodules in Soft Tissue Palpation with a Variable Stiffness Probe. PLoS One. DOI: 10.1371/journal.pone.0237379 These data support our research on a Variable Stiffness Palpation Probe and its control strategy to palpate and detect the location of stiff inclusions in soft tissues. The folder contains a ReadMe file and a binary Matlab file. For more details about the content of the binary file and the data structure, please read the ReadMe file.
Spiral - Imperial Co... arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2020License: CC BYData sources: Spiral - Imperial College Digital RepositoryORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.15131/shef.data.12732824&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 6visibility views 6 Powered bymore_vert Spiral - Imperial Co... arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2020License: CC BYData sources: Spiral - Imperial College Digital RepositoryORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.15131/shef.data.12732824&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018 United Kingdom Funded by:EC | PALEC| PALAuthors: Fischer, Tobias; Chang, Hyung Jin; Demiris, Yiannis;Fischer, Tobias; Chang, Hyung Jin; Demiris, Yiannis;Overview The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix _glasses), and 2) One with natural appearances (no eyetracking glasses are worn; suffix _noglasses). The _noglasses images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the _glasses images. There is code accompanying this dataset: https://github.com/Tobias-Fischer/rt_gene. Please use the issue tracker in the code respository if you have questions regarding the dataset. Subjects / 3-Fold evaluation 15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used). We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups): 's001', 's002', 's008', 's010' 's003', 's004', 's007', 's009' 's005', 's006', 's011', 's012', 's013' The validation set consists of sessions 's014', 's015' and 's016'. While the MATLAB script (prepare_dataset.m; see code repository) creates train and test images for each subject, all images were used for the evaluation (see evaluate_model.py). Labeled dataset (sXYZ_glasses) The file for each subject contains the following information: label_combined.txt This is the main file containing labels. The formatting is as follows: seq_number, [head pose: down(pos) / up (neg), left(pos) / right(neg)], [gaze: up(pos) / down(neg), right(pos) / left(neg)], timestamp label_headpose.txt This file contains more detail about the head pose of the subject. seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp kinect2_calibration.yaml The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "original" folder The face_before_inpainting folder contains the face with a large margin to the left and right. The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder. The overlay folder contains images where the mask was overlaid on the face_before_inpainting images. The face folder contains the face image extracted using MTCNN with a tighter margin. The left and right folders contain the left and right eye image areas. The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting). "inpainted" folder The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting. Then, the images contained in the face, left and right folders were extracted using MTCNN as above. Unlabeled dataset (sXYZ_noglasses) kinect2_calibration.yaml This file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "face" folder This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn). Overview The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix _glasses), and 2) One with natural appearances (no eyetracking glasses are worn; suffix _noglasses). The _noglasses images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the _glasses images. There is code accompanying this dataset: https://github.com/Tobias-Fischer/rt_gene. Please use the issue tracker in the code respository if you have questions regarding the dataset. Subjects / 3-Fold evaluation 15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used). We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups): 's001', 's002', 's008', 's010' 's003', 's004', 's007', 's009' 's005', 's006', 's011', 's012', 's013' The validation set consists of sessions 's014', 's015' and 's016'. While the MATLAB script (prepare_dataset.m; see code repository) creates train and test images for each subject, all images were used for the evaluation (see evaluate_model.py). Labeled dataset (sXYZ_glasses) The file for each subject contains the following information: label_combined.txt This is the main file containing labels. The formatting is as follows: seq_number, [head pose: down(pos) / up (neg), left(pos) / right(neg)], [gaze: up(pos) / down(neg), right(pos) / left(neg)], timestamp label_headpose.txt This file contains more detail about the head pose of the subject. seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp kinect2_calibration.yaml The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "original" folder The face_before_inpainting folder contains the face with a large margin to the left and right. The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder. The overlay folder contains images where the mask was overlaid on the face_before_inpainting images. The face folder contains the face image extracted using MTCNN with a tighter margin. The left and right folders contain the left and right eye image areas. The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting). "inpainted" folder The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting. Then, the images contained in the face, left and right folders were extracted using MTCNN as above. Unlabeled dataset (sXYZ_noglasses) kinect2_calibration.yaml This file contains the camera projection matrix in ROS format (this file should not be required). kinect2_pose.txt This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required). "face" folder This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn).
ZENODO arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2018License: CC BY NC SAData sources: Spiral - Imperial College Digital RepositoryAll Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=od______2659::8d976af93c74c3da308bdf856c3f9b67&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert ZENODO arrow_drop_down Spiral - Imperial College Digital RepositoryDataset . 2018License: CC BY NC SAData sources: Spiral - Imperial College Digital RepositoryAll Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=od______2659::8d976af93c74c3da308bdf856c3f9b67&type=result"></script>'); --> </script>
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