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- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging337
This work extends the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improves its efficiency, making it scalable for high content microscopy imaging.
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- Cell Segmentation Using Coupled Level Sets and Graph-Vertex Coloring137
The proposed four-color level set algorithm is used to efficiently and accurately segment hundreds of individual epithelial cells within a moving monolayer sheet from time-lapse images of in vitro wound healing without any false merging of cells.
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- Facial expression recognition for monitoring neurological disorders based on convolutional neural network108
This study presents a quantitative, objective and non-invasive facial expression recognition system to help in the monitoring and diagnosis of neurological disorders influencing facial expressions.
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- Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking90
A new efficient motion detection algorithm referred to as the flux tensor is used to detect moving objects in infrared video without requiring background modeling or contour extraction, and is insensitive to shadows as well as illumination changes in the visible channel.
- VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results81
A large-scale video object detection and tracking dataset, which consists of 79 video clips with about 1.5 million annotated bounding boxes in 33, 366 frames, and the evaluation protocol of the VisDrone-VDT2018 challenge and the results of the algorithms on the benchmark dataset are presented.
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- Deep learning-based face analysis system for monitoring customer interest73
This paper presents a deep learning-based system for monitoring customer behavior specifically for detection of interest, which fuses local part-based features with holistic facial information for robust facial expression recognition.
- Directional reversals enable Myxococcus xanthus cells to produce collective one-dimensional streams during fruiting-body formation70
It is found that cells undergo a phase transition from exploratory flocking, in which unstable cell groups move rapidly and coherently over long distances, to a reversal-mediated localization into one-dimensional growing streams that are inherently stable in space.
- Fuzzy Clustering and Active Contours for Histopathology Image Segmentation and Nuclei Detection69
This paper presents a new method for gland structure segmentation and nuclei detection using fuzzy c-means with spatial constraint algorithm and shows high performances for nuclei Detection compared to the human annotation.
- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
- Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images65
Experimental results on Stanford Tissue Microarray Database show that integration of CNN classification with explicit image segmentation leads to better adherence of identified class boundaries to actual tissue boundaries and improves the classification accuracy.
- Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network64
An ensemble convolutional neural network (MU-net) based on a U-net structure and the conditional generative adversarial network (cGAN) is adopted to generate label-preserving minority class data specifically to implement the data augmentation to alleviate the imbalance data problem.
- SHREC 2020: Classification in cryo-electron tomograms60
Seven different methods of finding and classifying proteins are evaluated in cryo-ET, finding that neural networks can achieve significantly better localization and classification performance, in particular convolutional networks with focus on high-resolution details such as those based on U-Net architecture.
- Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning59
These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy and paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.
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- Vasoactive agonists exert dynamic and coordinated effects on vascular smooth muscle cell elasticity, cytoskeletal remodelling and adhesion57
It is concluded that angiotensin II and adenosine may regulate extracellular matrix adhesion and elasticity in vascular smooth muscle cells as a form of adaptation to more efficiently support contractile behaviour.
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- Cell segmentation using Hessian-based detection and contour evolution with directional derivatives56
A new approach to obtain precise boundaries of cells with complex shapes using ridge measures for initial detection and a modified geodesic active contour for curve evolution that exploits the halo effect present in phase-contrast microscopy is presented.
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- Histopathology Tissue Segmentation by Combining Fuzzy Clustering with Multiphase Vector Level Sets50
This chapter presents an automatic segmentation system for histopathology imaging that combines multispectral edge and region informations through a vector multiphase level set framework and Beltrami color metric tensors refines the segmentation.
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- Development of a novel in vivo corneal fibrosis model in the dog40
The tested topical corneal alkali burn dose generated significant opacity and fibrosis in dog corneas without damaging the limbus as evidenced by histopathology, IHC, TEM, and OCT findings, and represents a viable large animal corneal fibrosis in vivo model.
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- Robust Tracking of Migrating Cells Using Four-Color Level Set Segmentation36
An automated cell segmentation and tracking system designed to study migration of cells imaged with a phase contrast microscope using active contour level set methods with a novel extension that efficiently prevents false-merge problem is presented.
- SHREC 2021: Classification in Cryo-electron Tomograms34
It is shown that learning-based approaches can achieve notably better localization and classification performance than baseline template matching (TM), a traditional method widely used in cryo-ET research.
- DRPnet: automated particle picking in cryo-electron micrographs using deep regression33
A deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs, which outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure.
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- Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation31
A novel approach for tissue segmentation using fuzzy spatial clustering, vector-based multiphase level set active contours and nuclei detection using an iterative kernel voting scheme that is robust even in the case of clumped touching nuclei.
- Persistent Feeding and Swallowing Deficits in a Mouse Model of 22q11.2 Deletion Syndrome28
Videofluoroscopic swallow study revealed that LgDel mice have altered feeding and swallowing behaviors, including slower lick rates, longer inter-lick intervals, and longer pharyngeal transit times with liquid consistency, which may provide opportunities for improved therapeutic intervention for adolescents and adults with 22q11DS, as well as others with a history of perinatal feeding and swallow disorders.
- Flexible Hinges in Bacterial Chemoreceptors28
It is found that intact, lipid-bilayer-inserted, and thus functional homodimers of the Escherichia coli chemoreceptor Tar exhibited bends at two flexible hinges along their ∼200-Å, rod-like, cytoplasmic domains, and Variable hinge bending explains indistinct densities for receptor rods outside the base plate region in subvolume averages of chemotaxis arrays.
- VisDrone-MOT2020: The Vision Meets Drone Multiple Object Tracking Challenge Results27
The VisDrone-MOT2020 consists of 79 challenging video sequences, including 56 videos for training, 7 videos for validation and 17 videos for evaluation, which feature 12 participating MOT algorithms.
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- DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking25
A dual-stream marker-guided network (DMNet) for segmentation of touching cells in microscopy videos of many cell types using an explicit cell marker-detection stream, with a separate mask-prediction stream using a distance map penalty function, which enables supervised training to focus attention on touching and nearby cells.
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- Predicting Mechanical Properties of Carbon Nanotube (CNT) Images Using Multi-Layer Synthetic Finite Element Model Simulations23
This work presents a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery and proposes the proposed deep learning architecture, CNTNeXt, which builds upon the previous CNTNet neural network.
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- Cell Spreading Analysis with Directed Edge Profile-Guided Level Set Active Contours23
A new approach to estimate accurate cell boundaries with complex shapes by applying a modified geodesic active contour level set method that directly utilizes the halo effect typically seen in phase contrast microscopy is presented.
- Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning22
Identifying the relevant process-structure interactions for the CNT forests using physics-based simulations and machine learning could rapidly advance the design, development, and adoption of CNT forest applications with varied morphologies and properties.
- Superresolution microscope image reconstruction by spatiotemporal object decomposition and association: application in resolving t-tubule structure in skeletal muscle21
A new image reconstruction algorithm based on tracklets, short trajectories of the same objects, which is able to resolve low to medium density of overlapping emitters with improved localization accuracy and resolves the transverse tubule structure of the mammalian skeletal muscle for the first time.
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- Automated Quantification of Vocal Fold Motion in a Recurrent Laryngeal Nerve Injury Mouse Model20
VF motion dynamics after iatrogenic recurrent laryngeal nerve injury in a mouse surgical model is examined to identify a method of inducing injury with a consistent recovery pattern from which to evaluate spontaneous recovery and test therapeutic interventions.
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- Efficient GPU Implementation of the Integral Histogram16
A proposed Graphics Processing Unit (GPU) implementation uses parallel prefix sums on row and column histograms in a cross-weave scan with high GPU utilization and communication-aware data transfer between CPU and GPU memories.
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- Efficient segmentation using feature-based graph partitioning active contours16
A new algorithm is described that implements the exact GPAC algorithm using a constant memory requirement of a few kilobytes, independent of image size.
- DeepFTSG: Multi-stream Asymmetric USE-Net Trellis Encoders with Shared Decoder Feature Fusion Architecture for Video Motion Segmentation15
A novel deep architecture for robust moving object detection that incorporates single and multi-stream multi-channel USE-Net trellis asymmetric encoders extending U-Net with squeeze and excitation (SE) blocks and a single shared decoder network for fusing multiple motion and appearance cues is proposed.
- CorNet: Unsupervised Deep Homography Estimation for Agricultural Aerial Imagery15
An unsupervised deep learning network is developed that estimates the sequence of planar homography matrices of the authors' corn corn kernels from imagery, without using any metadata to correct estimation errors.
- Recurrent laryngeal nerve transection in mice results in translational upper airway dysfunction15
A mouse model of RLN injury provides several novel functional outcome measures to increase the translational potential of findings in preclinical animal studies.
- Feature selection for appearance-based vehicle tracking in geospatial video15
Overall SFFS and SFS perform very well, close to the optimum determined by FOCUS, but RELIEF does not work as well for feature selection in the context of appearance-based object tracking.
- Time-dependent in situ structural and cellular aberrations in rabbit cornea in vivo after mustard gas exposure14
It is concluded that structural and cellular changes in primary corneal layers are early pathological events contributing to MGK in vivo, and efficient targeting of them with suitable agents has the potential to mitigate SM ocular injury.
- Robust mosaicking of maize fields from aerial imagery14
The results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management and the speeded up robust features (SURF) descriptor, VMZ produces the highest‐quality mosaics.
- A Strength Endurance Exercise Paradigm Mitigates Deficits in Hypoglossal-Tongue Axis Function, Strength, and Structure in a Rodent Model of Hypoglossal Motor Neuron Degeneration13
Results showed that sham-exercised CTB-SAP rats had significant deficits in lick rate, swallow timing, and lick force, which suggest that high-repetition/low-resistance tongue exercise in this model is a safe and effective treatment to prevent/diminish signs of hypoglossal-tongue axis degeneration.
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- Fast modular RLE-based inspection scheme for PCBs13
This inspection system uses run-length encoding (RLE) for storage and operations and an inspection scheme which exploits the availability of an artwork for comparison purposes to meet the demands for speed and accuracy.
- grasviq : an image analysis framework for automatically quantifying vein number and morphology in grass leaves12
The Grass Vein Image Quantification (GrasVIQ) framework, designed specifically for parallel venation, automatically segments and quantifies vein patterns from images of cleared leaf pieces using classical computer vision techniques and can perform high throughput vein quantification, with precision on par with manual quantification.
- Complex Non-sinus-associated Pachymeningeal Lymphatic Structures: Interrelationship With Blood Microvasculature12
The novel findings reveal the presence of lymphatic-like structures that exist on their own and/or in close proximity to microvessels that could facilitate fluid exchange between the two compartments and constitute an alternative route for CSF drainage.
- Geospatial content summarization of UAV aerial imagery using mosaicking12
A feature based registration method is proposed which brings the complexity and homogeneity nature of maize data under consideration and formulates a two step registration method which reduces error accumulation acquired through traditional frame-to-frame registration.
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- DeepDDK: A Deep Learning based Oral-Diadochokinesis Analysis Software11
A deep learning based software to extract quantitative data from the oral DDK signal, thereby transforming it into an objective diagnostic and treatment monitoring tool is presented.
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- Deep learning approaches in electron microscopy imaging for mitochondria segmentation11
It is shown that a convolutional neural network provides accurate mitochondria segmentation in CA1 hippocampus area of brain that is imaged by a focused ion beam scanning electron microscope (FIBSEM).
- Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation11
This work proposes an approach for vessel segmentation that combines multi-focus image fusion with robust adaptive filtering, which considerably improves segmentation quality by deblurring out-of-focus regions through incorporating 3D structure information from multiple focus steps.
- Vehicle detection and orientation estimation using the radon transform11
A robust and accurate method for detecting vehicles using a template-based directional chamfer matching, combined with vehicle orientation estimation based on a refined segmentation, followed by a Radon transform based profile variance peak analysis approach is developed.
- Feature fusion using ranking for object tracking in aerial imagery11
A framework for dynamic online ranking-based feature evaluation and fusion in aerial wide-area tracking that increases localization accuracy by reducing multimodal features or background clutter and increases the robustness of the tracker in dynamically changing environments.
- Cell segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns11
This work extends GPAC for fluorescence-based cell segmentation using regional density functions and dramatically improves its efficiency for segmentation from O(N4) to O( N2), for an image with N2 pixels, making it practical and scalable for high throughput microscopy imaging studies.
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- Accurate Spatial Neighborhood Relationships for Arbitrarily-Shaped Objects Using Hamilton-Jacobi GVD10
A new algorithm for efficiently, and accurately extracting accurate neighborhood graphs in linear time by computing the Hamilton-Jacobi generalized Voronoi diagram (GVD) using the exact Euclidean-distance transform with Laplacian-of-Gaussian, and morphological operators is proposed.
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- Impact of Limb Phenotype on Tongue Denervation Atrophy, Dysphagia Penetrance, and Survival Time in a Mouse Model of ALS9
Both LCN-SOD1 phenotypes had significant tongue denervation atrophy, even hindlimb phenotype mice without dysphagia, providing robust rationale for using this preclinical model to explore targeted treatments for tongue den reservation atrophy and ensuing dysphagía.
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- Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map9
This work proposes a new deep learning-based approach to fuse a set of multi-channel single-focus microscopy images within each volumetric z-stack into a single fused image that accurately captures as much of the vascular structures as possible.
- iFER: facial expression recognition using automatically selected geometric eye and eyebrow features9
The proposed system, iFER (eye–eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production.
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- Influence of membrane cholesterol and substrate elasticity on endothelial cell spreading behavior9
The initial response of endothelial cells to changes in substrate elasticity was determined by membrane cholesterol levels, and the extent of endothelial cell spreading increases with membrane cholesterol content, indicating that membrane-cholesterol plays an important role in cell spreading on soft materials constructed with appropriate elasticity.
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- LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos8
The experimental results show promising performance for automated, objective, and quantitative analysis of LAR events from laryngeal endoscopy videos with over 90% and 99% F1 scores for LAR and non-LAR frames respectively.
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- Image Analysis for Ophthalmology: Segmentation and Quantification of Retinal Vascular Systems8
This chapter describes a set of widely used retinal imaging modalities, including fundoscopy, fluorescein angiography (FA), and optical coherence tomography (OCT), along with emerging modalities to measure retinal blood flow dynamics, to describe processing techniques for measuring geometrical vascular structures.
- Context-sensitive filtering in RLE for PCB inspection8
A fast printed circuit board (PCB) inspection methodology based on the segmentation of the PCB image into basic patterns and context sensitive filtering of the difference image for functional defect detection is presented.
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- Nucleus Classification in Colon Cancer H&E Images using Deep Learning7
An automated deep learning system for tissue classification in histopathology images and the ability of CNN in learning high-dimensional and complex classes of objects makes it a very useful tool for a variety of classification tasks in biomedical imaging.
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- Meningeal lymphatic architecture and drainage dynamics surrounding the human middle meningeal artery6
In vivo and ex vivo evidence of a distinct CSF-ISF drainage compartment surrounding the middle meningeal artery (MMA) in humans is presented and this integrated human framework motivates future mechanistic studies into the role of ventral lymphatics in brain fluid clearance and neuroimmune regulation.
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- Realtime motion detection based on the spatio-temporal median filter using GPU integral histograms6
This work describes a parallel 3D spatiotemporal median filter algorithm implemented in CUDA for many core Graphics Processing Unit (GPU) architectures using the integral histogram as a building block to support adaptive window sizes and characterize performance of the parallel 3d median filter for different image sizes and varying number of histogram bins.
- Feature prominence-based weighting scheme for video tracking6
Experimental results show that feature prominence outperforms these other feature fusion methods and is embedded within a test-bed tracking system.
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- Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images5
A deep learning system for robust segmentation of cranial vasculature of mice in confocal microscopy images using an ensemble of two deep-learning cascades consisting of two coarse-to-fine subnetworks with skip connections in between to improve sensitivity and precision.
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- Mosaicing of Dynamic Mesentery Video with Gradient Blending5
A novel algorithm, Deformable Normalized Cross Correlation (DNCC) image matching with RANSAC to establish robust registration and to produce seamless panorama from motion-blurred frames is proposed, and a gradient blending method based on image edge information is presented.
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- Axon and Myelin Sheath Segmentation in Electron Microscopy Images using Meta Learning4
A robust, meta-learning based pipeline for segmentation of axons and surrounding myelin sheaths in electron microscopy images using a meta learning-based training strategy and a U-net like encoder decoder deep neural network is proposed.
- Advancing Laryngeal Adductor Reflex Testing Beyond Sensory Threshold Detection4
There are variant and invariant subcomponents of the LAR motor response, and only a fraction of suprathreshold stimuli evoked complete glottic closure during the LAR, which has demonstrated untapped potential in the current FEESST protocol.
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- DeepMaizeCounter: Smarter Stand Counts for Seedling Maize from Mosaic Imagery with YOLOv43
Knowing how many plants have germinated in a farmer's or researcher's field is central to crop management and research.
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- Comparison of two cleaning and sterilization protocols of diamond burr tips used in debridement for canine superficial chronic corneal epithelial defects3
No significant damage to Group 1 DBTs was noted after 100 cycles, however, particulate matter is not adequately removed using this sterilization technique, and Ultrasonic cleaning is warranted between DBDs to achieve adequate particulate removal before sterilization.
- Development of semi-automatic procedure for detection and tracking of fiducial markers for orofacial kinematics during natural feeding3
Computer vision tools are developed to extract kinematics and behavioral features from XROMM videos to overcome barriers of current analysis methods and gain basic scientific knowledge and translational insights for feeding disorders caused by neurological conditions such as ALS, Parkinson's disease, and stroke.
- Moving object detection and tracking for event-based video analysis3
This dissertation surveys different taxonomies of motion understanding problems, identifies the major components in an automated visual event recognition system, and presents the challenges and the significant studies in moving object detection, shadow elimination, and object tracking.
- Maintaining Trajectories of Salient Objects for Robust Visual Tracking3
This paper presents a robust approach to track multiple objects for low resolution, far-field visual surveillance applications by utilizing an adaptive background model and resolving the correspondence between their trajectory segments using proximity and appearance similarity measures.
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- Corneal injury is associated with stromal and vascular alterations within cranial dura mater2
It is demonstrated for the first time that two weeks after the initial insult, alkaline injury to the cornea leads to remote pathological changes within the coronal suture area of the dura mater, which provides a much-needed link explaining the association between ophthalmic conditions and the development of neurodegenerative diseases.
- Mobile Image Analysis for Microscopic Images of Seeds2
This work presents an image processing application named MUSeed that automatically computes seed morphometry and identifies clusters consisting of multiple touching seeds based on size and shape criteria for further processing by the cluster decomposition module.
- Aerial visual localization through novel applications of Weisfeiler-Lehman graph embeddings1
This paper introduces a novel graph-based framework for UAV visual localization that uses Weisfeiler-Lehman (WL) graph embeddings and introduces a novel subgraph extraction technique inspired by bucket sort that accelerates embedding generation within a sliding-window search space.
- Bag-of-graph-attributes and sparse graph structures for efficient UAV localization in complex environments1
A novel graph-based building neighborhood pattern search pipeline for robust aerial visual localization in environments lacking unique landmarks and introduces two novel methods, Bag-of-Graph-Attributes (BoGA) and Sparse Graph Integral Histograms (S-GIH), to enable computationally efficient approximate subgraph search while maintaining robustness against errors in building detection or segmentation.
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- Level Set-Based Fast Multi-phase Graph Partitioning Active Contours Using Constant Memory1
This work presents multi-phase FastGPAC that extends the dramatic improvement of memory requirements and computational complexity on two-class GPAC, into multi-class image segmentation and implements an exact GPAC segmentation using constant memory requirement of few kilobytes and enables use of GPAC on high throughput and high resolution images.
- Impact of tongue exercise on hypoglossal axis survival, structure, and output in a rodent model of hypoglossal motor neuron degeneration–
Findings provide further evidence that tongue exercise-induced neuroplasticity may be a viable treatment to improve functional outcomes in patients with MNDs and/or OSA.
- Graph attribute encoding and sparse graph integral histograms for efficient aerial localization–
Comprehensive evaluations across four diverse datasets demonstrate that the end-to-end approach achieves over a 2000× speedup for reference graphs exceeding 150,000 nodes, maintains high retrieval accuracy under severe structural noise, and maximizes localization reliability in the presence of real-world detection errors.
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- Genioglossus sarcomere and mitochondria morphology in a rodent model of hypoglossal motor neuron death–
A novel rodent model is developed using intralingual injections of cholera toxin B conjugated to saporin to induce targeted loss of XII LMNs and XII motor output to hypothesize that XII LMN loss leads to degenerative changes in genioglossus sarcomere and mitochondria morphology in CTB-SAP rats.
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- SAM-SPJunc: Self-Prompting for Junction Detection in Retinal Images via Radius-Based Representations–
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- Remote Instrumentation Science Environment for Intelligent Image Analytics–
A Remote Instrumentation Science Environment (RISE) for intelligent image analytics that provides the infrastructure to securely capture images, determine process parameters via machine learning, and provide experimental control actions via automation, under the premise of “human-on-the-loop” is proposed.
- Microvascular Pachymeningeal Lymphatic Structures: Association with Blood Vessels and Cranial Bones–
The novel findings reveal the presence of lymphatic‐like structures that exist on their own and/or in close proximity to microvessels and the interrelationship and possible connections between lymphatic vessels and dural blood vessels.
- A hybrid local and global multi-object tracking with semantic spatial and appearance modules–
A hybrid tracking-by-detection system that combines local and global data association scheme to ensure efficiency and reduce complexity and can handle the appearance of new objects, temporal disappearance, object terminations, and object occlusions.
- Robust target tracking using adaptive color feature and likelihood fusion–
An improved STAPLE (iSTAPLE) tracker is proposed by adding the Color Names (CN)2 to the correlation part of the tracker by showing through comprehensive experiments on the VOT Challenge 2016 dataset that iSTAPle obtains a gain of 25% in tracking robustness.
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- Development of an Efficient Methodology for the Mapping and Digital Analysis of 3D Surfaces via SEM–
This work presents an efficient, reproducible measurements of a 3-dimensional surface that is made significantly more difficult when considering a sphere, a configuration devoid of any easily identifiable reference points.
- The effect of substrate elasticity on the adhesion and elasticity of vascular smooth muscle cells (1077.8)–
Initial results showed a significant increase in cell elasticity/stiffness, adhesion force, and adhesion probability of the VSMCs cultured on the stiffer substrates compared with the cells cultured on softer substrates, supporting the hypothesis that elasticity of substrate exerts an effect on the mechanical and adhesive properties of VS MCs.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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