(a-topleft) The original image in RGN color space, (b-topright) the hard segmentation output of the GFkuts algorithm after 5 iterations, (c-bottomleft) the soft segmentation result... by Julian D. Colorado (9461385)

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the differen... by Antonio Quintero-Rincón (21087716)

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

“…<p>A) Two consecutive image frames are used to calculate the optical flow via the Farnebäck algorithm, which utilizes a multi-level resolution pyramid to estimate intensity displacements between the two frames. …”

Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

“…The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. …”

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

Data_Sheet_1_Information-theoretic analysis of Hierarchical Temporal Memory-Spatial Pooler algorithm with a new upper bound for the standard information bottleneck method.pdf by Shiva Sanati (16035875)

Data_Sheet_2_Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms.docx by Hao-Chun Lu (12965318)

“…</p>Methods<p>The CT was estimated from 3 points of each image. We used five machine-learning base algorithms to construct the classifiers. …”

Data_Sheet_1_Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms.docx by Hao-Chun Lu (12965318)

“…</p>Methods<p>The CT was estimated from 3 points of each image. We used five machine-learning base algorithms to construct the classifiers. …”

MARGO workflow, tracking algorithm, and sample behavioral box. by Zach Werkhoven (6473165)

“…Each frame consists of image processing (green) to segment foreground from the background, noise estimation (magenta) to assess the quality of foreground segmentation and determine if the current frame can be tracked, and tracking (cyan) of foreground binary blobs. …”

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

“…<p dir="ltr">Figures represented here illustrates the <b>metaheuristic-based band selection framework</b> for hyperspectral image classification using <b>Binary Jaya Algorithm enhanced with a mutation operator</b> to improve population diversity and avoid premature convergence. …”

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

“…Finally, for spatial relationship verification, a Spatial Coverage Consistency Checking Operator for MCLP Results (SCCCOMR) is designed. Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …”

DataSheet_1_Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT I... by Mengshi Dong (5181833)

“…Purpose<p>To establish and validate a radiomics model to estimate the malignancy of mediastinal lymph nodes (LNs) based on contrast-enhanced CT imaging.…”

Supplementary Material for: Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation by Higaki A. (11035161)

“…In this study, we developed an automatic artery/vein differentiation and a size measurement system utilizing machine learning algorithms. <b><i>Methods and Results:</i></b> We used 654 independent mouse mesenteric artery images for model training. …”

3D Microvascular Image Data and Labels for Machine Learning by Natalie Holroyd (7099391)

“…<p dir="ltr">These images and associated binary labels were collected from collaborators across multiple universities to serve as a diverse representation of biomedical images of vessel structures, for use in the training and validation of machine learning tools for vessel segmentation. …”

Imaging of the zebrafish tectum. by Giovanni Diana (7610630)

“…(E) Example of raw fluorescence and estimation of hidden variables of the HMM using a sequential maximum-likelihood algorithm (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1007481#sec014" target="_blank">Materials and methods</a>).…”

WEISS Catheter Segmentation in Fluoroscopy Dataset by Evangelos Mazomenos (6761597)

“…The output of the algorithm was manually checked and corrected to provide the final catheter segmentation.…”