ToF-camera amplitude image with (rounded to full pixels) corresponding 3D points. by Bartlomiej Pycinski (3151272)

Subjects:

Results of the second experimental set-up on femoral phantom (Pos.#2). by Bartlomiej Pycinski (3151272)

Subjects:

Results of the first experimental set-up on femoral phantom with manually removed outliers (Pos.#1, Corrected). by Bartlomiej Pycinski (3151272)

Subjects:

Mean projection errors obtained using two camera positions for calibration and validation (Fig 2). by Bartlomiej Pycinski (3151272)

Subjects:

Results of the first experimental set-up on femoral phantom (Pos.#1). by Bartlomiej Pycinski (3151272)

Subjects:

ICP registration results in various ToF camera positions. by Bartlomiej Pycinski (3151272)

Subjects:

CT surfaces extracted from the 3D segmentation results. by Bartlomiej Pycinski (3151272)

Subjects:

ToF depth images segmentation results in a 3D view. by Bartlomiej Pycinski (3151272)

Subjects:

Average number of DE peaks and average PO^DE (nPO^DE) score at various sample sizes for prostate cancer. by Jinfeng Zou (201031)

“…<p>The default SNR was used for each pre-processing algorithm. At each sample size, the average number of DE peaks detected at a 10% FDR control was calculated based on 100 randomly sampled subsets. …”

Understanding Spatiotemporal Forms, Triggers and Consequences of Urban Dy- namics in Taipei: 1990-2005 by Polina Lemenkova (5855411)

“…Consequences of urban sprawl for the global cities, such as Taipei. 2) Detailed technical description of the GIS part: remote sensing data capture, pre-processing, algorithm processing, image classification and spatial analysis…”

Comparative enrichment analysis of biomarker-based patient clusters: A quantitative analysis. by Michal Markovich Gordon (179371)

“…An artificial neural network, trained with the cluster assignment of each individual in the training dataset, was used to classify individuals from the validation dataset using the same clinical biomarkers subjected to the same pre-processing algorithm as was the test dataset. The resulting clustering of the validation set was also subjected to enrichment analysis with the same terms as was the training set. …”

Impact of saturation-artifact removal on the stromal coherent transmittance. by Maëlle Vilbert (17101954)

“…<p>The “central cut” corresponds to the part of the image that has been cropped after detection of a saturated area (step B in the pre-processing algorithm, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0291613#pone.0291613.g002" target="_blank">Fig 2</a>). …”

Enhancing Computational Tools for LC-IM-MS-Based Small Molecule Workflows (ASMS 2017) by PNNL Omics (4106851)

“…Here, we investigate MS pre-processing algorithms to extend feature detection and quantification performance, as well as integration of IMS collisional cross section (CCS) libraries into data analysis tools for molecular characterization. …”

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets-3 by Michael Gormley (84453)

“…</p><p></p>on values obtained using different pre-processing algorithms. Row 2: Different univariate feature selection methods. …”

A diagnostic plot to assess, as a function of signal intensity, whether or not the underlying distributions for the expression values from each chip/sample share the same center by Daniel P Gaile (29727)

“…Rather, these plots support the hypothesis that the Stage I pre-processing algorithms could not adequately adjust for differences in the underlying population distributions of the expression values for the empty probesets.…”

Image_1_Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data.jpeg by Maggie D. Clarke (12277139)

“…In the assessment of the outcome of the pre-processing algorithms, we focus on comparing signal representation before and after pre-processing and discuss the importance of the different components of the main processing steps. …”