A New Ground Truth Data Set For Seismic Studies

2009 ◽  
Vol 80 (3) ◽  
pp. 465-472 ◽  
Author(s):  
I. Bondar ◽  
K. L. McLaughlin
Keyword(s):  
Ground Truth ◽  
Data Set ◽  
Ground Truth Data

Chemical Proportionality within Molecular Networks

2021 ◽  
Author(s):  
Daniel Petras ◽  
Andrés Mauricio Caraballo-Rodríguez ◽  
Alan K. Jarmusch ◽  
Carlos Molina-Santiago ◽  
Julia M. Gauglitz ◽  
...  
Keyword(s):  
Natural Products ◽  
Ground Truth ◽  
Mass Spectrometry Data ◽  
Molecular Networks ◽  
Data Series ◽  
Sequential Data ◽  
Chemical Transformations ◽  
Data Set ◽  
Molecular Networking ◽  
Ground Truth Data

Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here we report the Chemical Proportionality contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of related compounds. We tested the ChemProp workflow on a ground truth data set of defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking environment.<br><b> </b>

scholarly journals Terrestrial Laser Scanning and Ground Truth Data for Characterizing Downed Dead Wood

2020 ◽  
Author(s):  
Tuomas Yrttimaa ◽  
Ninni Saarinen ◽  
Ville Luoma ◽  
Topi Tanhuanpää ◽  
Ville Kankare ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Dead Wood ◽  
Terrestrial Laser Scanning ◽  
Ground Truth ◽  
Point Clouds ◽  
Data Set ◽  
Ground Truth Data ◽  
Standing Trees ◽  
Downed Dead Wood

The feasibility of terrestrial laser scanning (TLS) in characterizing standing trees has been frequently investigated, while less effort has been put in quantifying downed dead wood using TLS. To advance dead wood characterization using TLS, we collected TLS point clouds and downed dead wood information from 20 sample plots (32 m x 32 m in size) located in southern Finland. This data set can be used in developing new algorithms for downed dead wood detection and characterization as well as for understanding spatial patterns of downed dead wood in boreal forests.

scholarly journals Assignment Flow for Order-Constrained OCT Segmentation

2021 ◽  
Author(s):  
Dmitrij Sitenko ◽  
Bastian Boll ◽  
Christoph Schnörr
Keyword(s):  
Geometric Approach ◽  
Ground Truth ◽  
Retinal Cell ◽  
Dice Similarity Coefficient ◽  
Retinal Layer ◽  
Healthy Human ◽  
Data Set ◽  
Ground Truth Data ◽  
Global Shape ◽  
Layer Segmentation

AbstractAt the present time optical coherence tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.

scholarly journals Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set

2020 ◽  
Vol 890 (2) ◽  
pp. 103 ◽  
Author(s):  
Shin Toriumi ◽  
Shinsuke Takasao ◽  
Mark C. M. Cheung ◽  
Chaowei Jiang ◽  
Yang Guo ◽  
...  
Keyword(s):  
Comparative Study ◽  
Ground Truth ◽  
Data Driven ◽  
Flux Emergence ◽  
Data Set ◽  
Ground Truth Data ◽  
Coronal Field ◽  
Solar Coronal

A Visual Tracking Scheme for Accurate Object Retrieval in Low Frame Rate Videos

2016 ◽  
Vol 25 (05) ◽  
pp. 1640003 ◽  
Author(s):  
Yoav Liberman ◽  
Adi Perry
Keyword(s):  
Visual Tracking ◽  
Template Matching ◽  
Large Data ◽  
Ground Truth ◽  
Frame Rate ◽  
Data Set ◽  
Ground Truth Data ◽  
Multiple Metrics ◽  
Low Frame Rate ◽  
Tracking Objects

Visual tracking in low frame rate (LFR) videos has many inherent difficulties for achieving accurate target recovery, such as occlusions, abrupt motions and rapid pose changes. Thus, conventional tracking methods cannot be applied reliably. In this paper, we offer a new scheme for tracking objects in low frame rate videos. We present a method of integrating multiple metrics for template matching, as an extension for the particle filter. By inspecting a large data set of videos for tracking, we show that our method not only outperforms other related benchmarks in the field, but it also achieves better results both visually and quantitatively, once compared to actual ground truth data.

Mixture Modeling with Pairwise, Instance-Level Class Constraints

2005 ◽  
Vol 17 (11) ◽  
pp. 2482-2507 ◽  
Author(s):  
Qi Zhao ◽  
David J. Miller
Keyword(s):  
Synthetic Data ◽  
Ground Truth ◽  
Mixture Modeling ◽  
Data Set ◽  
Ground Truth Data ◽  
New Class ◽  
Recent Approach ◽  
Semisupervised Clustering ◽  
The One ◽  
Number Of Classes

The goal of semisupervised clustering/mixture modeling is to learn the underlying groups comprising a given data set when there is also some form of instance-level supervision available, usually in the form of labels or pairwise sample constraints. Most prior work with constraints assumes the number of classes is known, with each learned cluster assumed to be a class and, hence, subject to the given class constraints. When the number of classes is unknown or when the one-cluster-per-class assumption is not valid, the use of constraints may actually be deleterious to learning the ground-truth data groups. We address this by (1) allowing allocation of multiple mixture components to individual classes and (2) estimating both the number of components and the number of classes. We also address new class discovery, with components void of constraints treated as putative unknown classes. For both real-world and synthetic data, our method is shown to accurately estimate the number of classes and to give favorable comparison with the recent approach of Shental, Bar-Hillel, Hertz, and Weinshall (2003).

scholarly journals Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Ioannis Agtzidis ◽  
Mikhail Startsev ◽  
Michael Dorr
Keyword(s):  
Eye Movement ◽  
Large Scale ◽  
Ground Truth ◽  
Training Set ◽  
Event Type ◽  
Short Article ◽  
Data Set ◽  
Ground Truth Data ◽  
The Gaze ◽  
Movie Clip

In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or physically implausible signals). In order to achieve more consistent annotations, the gaze samples were labelled by a novice rater based on rudimentary algorithmic suggestions, and subsequently corrected by an expert rater. Overall, we annotated eye movement events in the recordings corresponding to 50 randomly selected test set clips and 6 training set clips from Hollywood2, which were viewed by 16 observers and amount to a total of approximately 130 minutes of gaze data. In these labels, 62.4% of the samples were attributed to fixations, 9.1% – to saccades, and, notably, 24.2% – to pursuit (the remainder marked as noise). After evaluation of 15 published eye movement classification algorithms on our newly collected annotated data set, we found that the most recent algorithms perform very well on average, and even reach human-level labelling quality for fixations and saccades, but all have a much larger room for improvement when it comes to smooth pursuit classification. The data set is made available at https://gin.g- node.org/ioannis.agtzidis/hollywood2_em.

scholarly journals Optimizing sparse RFI prediction using deep learning

2019 ◽  
Vol 488 (2) ◽  
pp. 2605-2615 ◽  
Author(s):  
Joshua Kerrigan ◽  
Paul La Plante ◽  
Saul Kohn ◽  
Jonathan C Pober ◽  
James Aguirre ◽  
...  
Keyword(s):  
Ground Truth ◽  
Maximum Amount ◽  
Limiting Factor ◽  
Radio Frequency Interference ◽  
Identification Algorithm ◽  
Radio Telescopes ◽  
Data Set ◽  
Ground Truth Data ◽  
Time Frequency ◽  
Data Throughput

ABSTRACT Radio frequency interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array (HERA) grow larger in number of receivers. To address this, we present a deep fully convolutional neural network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known ‘ground truth’ data set for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6 × 105 HERA time-ordered 1024 channelled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time–frequency context which increases discrimination between RFI and non-RFI. The inclusion of phase when predicting achieves a recall of 0.81, precision of 0.58, and F2 score of 0.75 as applied to our HERA-67 observations.

scholarly journals Population receptive field shapes in early visual cortex are nearly circular

2020 ◽  
Author(s):  
Garikoitz Lerma-Usabiaga ◽  
Jonathan Winawer ◽  
Brian A. Wandell
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Receptive Field ◽  
Receptive Fields ◽  
Ground Truth ◽  
Central Position ◽  
Data Set ◽  
Ground Truth Data ◽  
Aspect Ratios ◽  
Early Visual Cortex

AbstractThe visual field region where a stimulus evokes a neural response is called the receptive field (RF). Analytical tools combined with functional MRI can estimate the receptive field of the population of neurons within a voxel. Circular population RF (pRF) methods accurately specify the central position of the pRF and provide some information about the spatial extent (diameter) of the receptive field. A number of investigators developed methods to further estimate the shape of the pRF, for example whether the shape is more circular or elliptical. There is a report that there are many pRFs with highly elliptical pRFs in early visual cortex (V1-V3; Silson et al., 2018). Large aspect ratios (>2) are difficult to reconcile with the spatial scale of orientation columns or visual field map properties in early visual cortex. We started to replicate the experiments and found that the software used in the publication does not accurately estimate RF shape: it produces elliptical fits to circular ground-truth data. We analyzed an independent data set with a different software package that was validated over a specific range of measurement conditions, to show that in early visual cortex the aspect ratios are less than 2. Furthermore, current empirical and theoretical methods do not have enough precision to discriminate ellipses with aspect ratios of 1.5 from circles. Through simulation we identify methods for improving sensitivity that may estimate ellipses with smaller aspect ratios. The results we present are quantitatively consistent with prior assessments using other methodologies.Significance StatementWe evaluated whether the shape of many population receptive fields in early visual cortex is elliptical and differs substantially from circular. We evaluated two tools for estimating elliptical models of the pRF; one tool was valid over the measured compliance range. Using the validated tool, we found no evidence that confidently rejects circular fits to the pRF in visual field maps V1, V2 and V3. The new measurements and analyses are consistent with prior theoretical and experimental assessments in the literature.

scholarly journals QUASI-ORTHORECTIFIED PROJECTION FOR THE MEASUREMENT OF RED GORGONIAN COLONIES

2018 ◽  
Vol XLII-2 ◽  
pp. 853-860 ◽  
Author(s):  
G. Pavoni ◽  
M. Palma ◽  
M. Callieri ◽  
M. Dellepiane ◽  
C. Cerrano ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Stereo Matching ◽  
Standard Procedure ◽  
Ground Truth ◽  
Practical Method ◽  
Textured Surface ◽  
Data Set ◽  
Ground Truth Data ◽  
Area Of Interest ◽  
Paramuricea Clavata

This study presents a practical method to estimate dimensions of <i>Paramuricea clavata</i> colonies using generic photographic datasets collected across wide areas. <i>Paramuricea clavata</i> is a non-rigid, tree-like octocoral; this morphology greatly affects the quality of the sea fans multi-view stereo matching reconstruction, resulting in hazy and incoherent clouds, full of “false” points with random orientation. Therefore, the standard procedure to take measurements over a reconstructed textured surface in 3D space is impractical. Our method overcomes this problem by using quasi-orthorectified images, produced by projecting registered photos on the plane that best fits the point cloud of the colony. The assessments of the measures collected have been performed comparing ground truth data set and time series images of the same set of colonies. The measurement errors fall below the requirements for this type of ecological observations.<br> Compared to previous works, the presented method does not require a detailed reconstruction of individual colonies, but relies on a global multi-view stereo reconstruction performed through a comprehensive photographic coverage of the area of interest, using a lowcost pre-calibrated camera. This approach drastically reduces the time spent working on the field, helping practitioners and scientists in improving efficiency and accuracy in their monitoring plans.

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