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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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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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