![]() ![]() The technology shows promising results in terms of detected features and localization accuracy. This study demonstrates that skin feature localization for patient tracking in a surgical setting is feasible. An average accuracy of 0.627 and 0.622 mm was achieved for MSER and SURF, respectively. The localization accuracy was compared to a system with conventional markers, serving as a ground truth. The detected features for the entire patient datasets were found to have an overall triangulation error of 0.207 mm for MSER and 0.204 mm for SURF. The framework was tested on a cadaver dataset and in eight clinical cases. The triangulation error is used for assessing the localization quality in 3D. Features can then be accurately detected using MSER and SURF and afterward localized by triangulation. The proposed tracking framework is based on a multi-camera setup for obtaining multi-view acquisitions of the surgical area. Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Feature (SURF) algorithms are applied for skin feature detection. To improve reliability and facilitate clinical workflow, this study proposes a new marker-free tracking framework based on skin feature recognition. Computer-assisted navigation is increasingly used in minimally invasive surgery (MIS), but current solutions require the use of reference markers in the surgical field for both patient and instruments tracking. Minimally invasive spine surgery is dependent on accurate navigation. We conclude that combination of complementary models can better track objects in terms of accuracy and robustness. Experimental results on OTB2013 and OTB2015 datasets show that the proposed method performs favorably compared with some state-of-the-art methods. Moreover, we introduce the attention mechanism to highlight discriminative features in each CNN. The prediction scores of all CNNs are adaptively fused to obtain robust tracking performance. The importances of different CNNs are identified by a joint inference of candidate location, predicted location and confidence score. Therefore, we propose to leverage the complementary properties of different CNNs for visual tracking in this paper. We observe that different CNNs usually have complementary characteristics in representing target objects. Despite demonstrated successes for visual tracking, utilizing features from the same network might suffer from the suboptimal performance due to limitations of CNN architecture itself. Some methods extract different levels of features based on pre-trained CNNs to deal with various challenging scenarios. Overall, the proposed advanced SHM technologies show great promise for fatigue crack damage detection of steel bridges in laboratory configurations, hence form the basis for long-term fatigue sensing solutions in field applications.Ĭonvolutional neural networks (CNNs) have shown favorable performance in recent tracking benchmark datasets. Laboratory test results verified that the proposed approaches can robustly identify the true fatigue crack from many non-crack edges. In vision-based fatigue crack detection approaches, two novel sensing methodologies are established through feature tracking and image overlapping, respectively. In SEC-based fatigue crack sensing, the research focuses are placed on numerical prediction of the SEC's response under fatigue cracking and experimental validations of sensing algorithms for monitoring fatigue cracks over long-term. These technologies are categorized as: 1) a large-area strain sensing technology based on the soft elastomeric capacitor (SEC) sensor and 2) non-contact vision-based fatigue crack detection approaches. In this dissertation, advanced SHM technologies are proposed for detecting and monitoring fatigue cracks in steel bridges. Despite various levels of success of crack monitoring methods over the past decades in the fields of structural health monitoring (SHM) and non-destructive evaluation (NDE), monitoring fatigue cracks in steel bridges is still challenging due to the complex structural joint layout and unpredictable crack propagation paths. If bridges are not appropriately inspected and maintained, fatigue cracks can eventually lead to catastrophic failures, in particular for fracture-critical bridges. Fatigue cracks that develop in steel highway bridges under repetitive traffic loads are one of the major mechanisms that degrade structural integrity. ![]()
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