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27 results for “feature tracking”
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
FIGURE 10. 1 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 10. 1, Trackway Gl6; 2, Close up of the Gl6-Pd interference; 3, Interpretative drawing of Gl6-Pd interference; 4, Orthophoto of trackway Gl6; 5, Shaded grey photogrammetric model of the interference. White arrows and letters a, b and c indicate interruption (a), displacement (b) of the tail trace, and trackway Gl6 last set (c). Scale bar is 5 cm.
FIGURE 9. 1 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 9. 1, Drawing of different parts making up the slab; 2, 3D photogrammetric false coloured model; 3, 3D photogrammetric shaded grey model. Scale bar is 0,5 m.
FIGURE 8 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 8. Slab MPUR NS 34/28. 1, Interference between trackways Gl4 and Pd indicating the relative antecedence of Gl4 trackmaker; 2, Trackway Pd, sets 2 and 3 interfering with Gl2, Gl4, Gl5 and Jb trackways. Scale bar is 5 cm.
FIGURE 7 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 7. Slab MPUR NS 34/28. 1, Swimming traces (indicated by black and white arrows) belonging to trackway Gl1; 2-3, Close-up of the best Gl1 tracks. Scale bar is 5 cm (1) and 1 cm (2-3).
FIGURE 4. 1 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 4. 1, Ganasauripus ladinus; 2, Pachypes dolomiticus manus; 3-4, Pachypes dolomiticus pedes. Scale bar is 1 cm (1) and 5 cm (2-4). Roman numerals refer to digits.
FIGURE 5. Event timeline. 1 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 5. Event timeline. 1, Ripple marks and Gl1 tracks formation; 2, Gl2 and Gl3 tracks formation; 3; Gl4 trackway formation; 4, Jb trackway formation; 5, Gl5 and Ct1 trackways formation; 6, Ct2 trackway formation; 7, Gl6 and Gl7 trackways formation; 8, Pd trackway formation; 9, Final interpretative drawing of the slab MPUR NS 34/28; 10, Trackmakers advancement directions. rip, ripple marks; Pd, Pachypes dolomiticus trackway; Ct1-Ct2, Chelichnus tazelwürmi trackways; Gl1-Gl7, Ganasauripus ladinus trackways; Jb, Janusichnus bifrons trackway; dcr,? desiccation cracks. Scale bar is 50 cm.
FIGURE 6 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 6. Slab MPUR NS 34/28. 1, Ripple marks counterprints; 2, Interference between ripple marks counterprints and Gl7 trackway. Scale bar is 5 cm.
FIGURE 3. 1-2 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 3. 1-2, Chelichnus tazelwürmi; 3, Janusichnus bifrons; 4, Ganasauripus ladinus. Scale bar is 1 cm. Roman numerals refer to digits.
FIGURE 1. 1 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 1. 1, Location map of the Butterloch-Bletterbach area – Redagno; 2, Schematic drawing of the ButterlochBletterbach section with the distribution of ichnotaxa; red tracks indicates the stratigraphic level from which the studied footprints came (from Ceoloni et al., 1988, redrawn and slightly modified).
GRID/CHiME-2 Track 1 - Video Features (25ms, 10ms)
<p>This archive contains the video features in Kaldi's [1] ark format that correspond to the CHiME-2 Track 1 [2] utterances for the isolated data (train, devel, test).</p> <p>The video files have been taken from the GRID corpus [3,4]. The features contain the 63-dimensional DCT coefficients of the landmark points extracted using the Viola-Jones algorithm. The features have been end-pointed and interpolated using a differential digital analyser in order to match the length of the utterances when using a frame length of 25ms and a frame shift of 10ms, which is the default configuration of Kaldi's feature extraction scripts.</p> <p>[1] http://kaldi-asr.org</p> <p>[2] http://spandh.dcs.shef.ac.uk/chime_challenge/chime2013/chime2_task1.html</p> <p>[3] http://spandh.dcs.shef.ac.uk/gridcorpus</p> <p>[4] Martin Cooke, Jon Barker, and Stuart Cunningham and Xu Shao, "An audio-visual corpus for speech perception and automatic speech recognition", The Journal of the Acoustical Society of America 120, 2421 (2006); http://doi.org/10.1121/1.2229005</p>
Multilingual Bottle-Neck Feature Learning from Untranscribed data for track 1 in zerospeech2017 (system 1 -- without VTLN)
<p>We investigate the extraction of bottle-neck features (BNFs) for multiple languages without access to manual transcription. Multilingual BNFs are derived from a multi-task learning deep neural network which is trained with unsupervised phoneme-like labels. The unsupervised phoneme-like labels are obtained from language-dependent Dirichlet process Gaussian mixture models separately trained on untranscribed speech of multiple languages.</p>
FIGURE 2 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 2. Orthophoto of the slab MPUR NS 34/28. Scale bar is 50 cm.
Workflow Automation of SEM Acquisitions and Feature Tracking
<p>Data related to the paper <em>Workflow Automation of SEM Acquisitions and Feature Tracking. </em>Includes images used for nanoparticle analysis and for tracking.</p>
Probing the Role of Feature Dimension Maps in Visual Cognition: Impact of Salience Level (Eye-tracking Follow-up Study)
ClinicalTrials.gov study NCT06852534. IPD Sharing: YES. Countries: 1. Publications: 14.
Set of extracted feature tracks for the year 2016.
<p>Dataframe composed of a set of trajectories identified and tracked from one yearlong of weather radar composites of the German Weather Service (DWD).</p>
Multilingual bottle-neck feature learning from untranscribed speech for track 1 in zerospeech2017 (system 2 -- with VTLN)
<p>We investigate the extraction of bottle-neck features (BNFs) for multiple languages without access to manual transcription. Multilingual BNFs are derived from a multi-task learning deep neural network which is trained with unsupervised phoneme-like labels. The unsupervised phoneme-like labels are obtained from language-dependent Dirichlet process Gaussian mixture models separately trained on untranscribed speech of multiple languages.</p> <blockquote> <p>In this version, the input MFCC for DPGMM is processed with VTLN.</p> </blockquote> <p> </p>
Prevalence, Clinical Features and Risk Factors of Cirrhotic Cardiomyopathy Assessed by Two-dimensional Speckle Tracking Imaging
ClinicalTrials.gov study NCT04969055. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Materials for Publication: Software Feature Request Detection in Issue Tracking Systems
<p>Additional figures, tables, experimant data, code, and results.</p> <p>See README.md for more information.</p>
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