The device-free (DF) object tracking in wireless networks requires a tracked object neither be equipped with a transceiver nor actively participate in the tracking process. One of methods to implement a DF tracking system is to leverage the variation of radio signals introduced by object intrusion. However, many factors may result in the variation of radio signals and it is hard to distinguish which variation is caused by object intrusion. In this paper, we investigate how some factors impact signal variations, and in further impact the effectiveness of DF tracking. Based on analysis, we conduct experiments with TelosB motes, which are carried out in three representative environments with different system parameters. Different wireless channel quality indicators are employed to monitor signal variations. The experiment results provide a significant guidance on how to choose environments, system parameters, channel quality indicators, and metrics to implement an effective DF tracking system.

A guidewire is a medical device inserted into vessels during image guided interventions for balloon inflation. During interventions, the guidewire undergoes non-rigid deformation due to patients' breathing and cardiac motions, and such 3D motions are complicated when being projected onto the 2D fluoroscopy. Furthermore, in fluoroscopy there exist severe image artifacts and other wire-like structures. All these make robust guidewire tracking challenging. To address these challenges, this paper presents a probabilistic framework for robust guidewire tracking. We first introduce a semantic guidewire model that contains three parts, including a catheter tip, a guidewire tip and a guidewire body. Measurements of different parts are integrated into a Bayesian framework as measurements of a whole guidewire for robust guidewire tracking. Moreover, for each part, two types of measurements, one from learning-based detectors and the other from online appearance models, are applied and combined. A hierarchical and multi-resolution tracking scheme is then developed based on kernel-based measurement smoothing to track guidewires effectively and efficiently in a coarse-to-fine manner. The presented framework has been validated on a test set of 47 sequences, and achieves a mean tracking error of less than 2 pixels. This demonstrates the great potential of our method for clinical applications.

In this paper, we present a novel probabilistic framework for automatic follicle quantification in 3D ultrasound data. The proposed framework robustly estimates size and location of each individual ovarian follicle by fusing the information from both global and local context. Follicle candidates at detected locations are then segmented by a novel database guided segmentation method. To efficiently search hypothesis in a high dimensional space for multiple object detection, a clustered marginal space learning approach is introduced. Extensive evaluations conducted on 501 volumes containing 8108 follicles showed that our method is able to detect and segment ovarian follicles with high robustness and accuracy. It is also much faster than the current ultrasound manual workflow. The proposed method is able to streamline the clinical workflow and improve the accuracy of existing follicular measurements.

Man-made environments possess a lot of regularities which simplify otherwise difficult pose estimation and visual reconstruction tasks. The constraints arising front parallel and orthogonal lines and planes can be efficiently exploited at various stages of vision processing pipeline. In this paper we propose an approach for estimation of vanishing points by exploiting the constraints of structured man-made environments, where the majority of lines is aligned with the principal orthogonal directions of the world coordinate frame. We combine efficient image processing techniques used in the line detection and initialization stage with simultaneous grouping and estimation of vanishing directions using expectation maximization (EM) algorithm. Since we assume an uncalibrated camera the estimated vanishing points can be used towards partial camera calibration and estimation of the relative orientation of the camera with respect to the scene. The presented approach is computationally efficient and has been verified extensively by experiments.