Sequential boundary tracking via reinforcement learning for overlapping cell resolution in sickle cell imaging. [PDF]
Sci RepBatool S, Guo M, Asghar MN, Iqbal S.
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People Counting Using YOLO-Based Detection and Clustering for a Mobile Robot. [PDF]
Sensors (Basel)Gomulka K, Wozniak P, Krzeszowski T.
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RGB-D SLAM: A Review of Methods and Performance Trade-Offs for Different Requirements. [PDF]
Sensors (Basel)Yan Y, Li J, Liu Q, Lv S, Wu Q.
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DMFRNet: Dynamic Multi-Scale Feature Reweighting Network for Dairy Cow Detection. [PDF]
Animals (Basel)Wei Z, Zhao G, Yu Z, Li X, Li Q, Wei D.
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Integrative artificial intelligence and multi-omics modeling approach for characterizing microbial dynamics and health impacts in space microgravity and radiation conditions. [PDF]
Front MicrobiolLu Y, Chang Z, Peng K.
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Research on Fire Source Recognition and Fire Extinguishing Algorithms Based on Multimodal Fusion and Lightweight Model Deployment. [PDF]
Sensors (Basel)Zhai D, Zhai Q, Liu S, Liu X, Guo T.
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A Selective Tracking and Detection Framework with Target Enhanced Feature
2018 24th International Conference on Pattern Recognition (ICPR), 2018In the long time tracking, object representation and occlusion handling are two important challenges. We propose a novel selective tracking and detection framework in which a new probabilistic object-enhanced feature is integrated. Firstly, besides precise object appearance feature, we believe the neighboring foreground-background contrast is another ...
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Tracking Objects to Detect Feature Dependencies
15th IEEE International Conference on Program Comprehension (ICPC '07), 2007The domain-specific ontology of a software system includes a set of features and their relationships. While the problem of locating features in object-oriented programs has been widely studied, runtime dependencies between features are less well understood.
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Emblem Detections by Tracking Facial Features
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Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 2003We describe a real-time system for face and facial feature detection and tracking in continuous video. The core of this system consists of a set of novel facial feature detectors based on our previously proposed information-based maximum discrimination learning technique. These classifiers are very fast and allow us to implement a fully automatic, real-
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