Segmentation and tracking are essential preliminary steps in the analysis of almost all live cell imaging applications. Although the number of open-source software systems that facilitate automated segmentation and tracking continue to evolve, many researchers continue to opt for manual alternatives for samples that are not easily auto-segmented, tracing cell boundaries by hand and reidentifying cells on consecutive frames by eye. Such methods are subject to inter-user variability, introducing idiosyncrasies into the results of downstream analysis that are a result of subjectivity and individual expertise. The methods are also susceptible to intra-user variability, meaning findings are challenging to reproduce. In this pilot study, we demonstrate and quantify the degree of intra- and inter-user variability in manual cell segmentation and tracking by comparing the phenotypic metrics extracted from cells segmented and tracked by different members of our research team. Furthermore, we compare the segmentation results for a ptychographic cell image obtained using different automated software and demonstrate the high dependence of performance on the imaging modality they were developed to handle. Our results show that choice of segmentation and tracking methods should be considered carefully in order to enhance the quality and reproducibility of results.
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http://dx.doi.org/10.1002/jemt.24715 | DOI Listing |
Sensors (Basel)
January 2025
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.
Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments.
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January 2025
Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany.
This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features.
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January 2025
Electrical and Computer Engineering Department, The University of Alabama, Tuscaloosa, AL 35487, USA.
Discretely monitoring traffic systems and tracking payloads on vehicle targets can be challenging when traversal occurs off main roads where overhead traffic cameras are not present. This work proposes a portable roadside vehicle detection system as part of a solution for tracking traffic along any path. Training semantic segmentation networks to automatically detect specific types of vehicles while ignoring others will allow the user to track payloads present only on certain vehicles of interest, such as train cars or semi-trucks.
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January 2025
School of Civil Engineering Architecture and the Environment, Hubei University of Technology, Wuhan 430068, China.
The green vision rate of rural highway greening landscape is a key factor affecting the driver's visual load. Based on this, this paper uses the eye tracking method to study the visual characteristics of drivers in different green vision environments on rural highways in Xianning County. Based on the HSV color space model, this paper obtains four sections of rural highway with a green vision rate of 10~20%, green vision rate of 20~30%, green vision rate of 30~40%, and green vision rate of 40~50%.
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January 2025
Institute of Sport Science, University of Applied Sciences Wiener Neustadt, 2700 Wiener Neustadt, Austria.
Striking velocity is a key performance indicator in striking-based combat sports, such as boxing, Karate, and Taekwondo. This study aims to develop a low-cost, accelerometer-based system to measure kick and punch velocities in combat athletes. Utilizing a low-cost mobile phone in conjunction with the PhyPhox app, acceleration data was collected and analyzed using a custom algorithm.
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