We propose a new method for contour tracking in video. The inverted distance transform of the edge map is used as an edge indicator function for contour detection. Using the concept of topographical distance, the watershed segmentation can be formulated as a minimization. This new viewpoint gives a way to combine the results of the watershed algorithm on different surfaces. In particular, our algorithm determines the contour as a combination of the current edge map and the contour, predicted from the tracking result in the previous frame. We also show that the problem of background clutter can be relaxed by taking the object motion into account. The compensation with object motion allows to detect and remove spurious edges in background. The experimental results confirm the expected advantages of the proposed method over the existing approaches.
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http://dx.doi.org/10.1109/TIP.2002.802522 | DOI Listing |
Sensors (Basel)
January 2025
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430000, China.
Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance.
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December 2024
Institute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 812 43 Bratislava, Slovakia.
The core of this publication is the design of a system for evaluating the condition of production equipment and machines by monitoring selected parameters of the production process with an additional sensor subsystem. The main positive of the design is the processing of data from the sensor layer using artificial intelligence (AI) and expert systems (ESs) with the use of edge computing (EC). Sensor information is processed directly at the sensor level on the monitored equipment, and the results of the individual subsystems are stored in the form of triggers in a database for use in the predictive maintenance process.
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January 2025
School of Mathematics and Computer, Wuhan Polytechnic University, Wuhan, 430048, China.
The rapid changes in the global environment have led to an unprecedented decline in biodiversity, with over 28% of species facing extinction. This includes snakes, which are key to ecological balance. Detecting snakes is challenging due to their camouflage and elusive nature, causing data loss and feature extraction difficulties in ecological monitoring.
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December 2024
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI).
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December 2024
Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan.
Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust () diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is considered the most effective solution for mitigating coffee leaf rust.
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