Kernel-based object tracking

A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.     

Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.     

Reliable Detection of Overtaking Vehicles Using Robust Information Fusion

Early detection of overtaking vehicles is an important task for vision-based driver assistance systems. Techniques utilizing image motion are likely to suffer from spurious image structures caused by shadows and illumination changes, let alone the aperture problem. To achieve reliable detection of overtaking vehicles, the authors have developed a robust detection method, which integrates dynamic scene modeling, hypothesis testing, and robust information fusion. A robust fusion algorithm, based on variable bandwidth density fusion and multiscale mean shift, is introduced to obtain reliable motion estimation against various image noise. To further reduce detection error, the authors model the dynamics of road scenes and exploit useful constraints induced by the temporal coherence in vehicle overtaking. The proposed solution is integrated into a monocular vision system onboard for obstacle detection. Test results have shown superior performance achieved by the new method     

Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.

A probabilistic model for automatic segmentation of the esophagus in 3-D CT scans

Being able to segment the esophagus without user interaction from 3-D CT data is of high value to radiologists during oncological examinations of the mediastinum. The segmentation can serve as a guideline and prevent confusion with pathological tissue. However, limited contrast to surrounding structures and versatile shape and appearance make segmentation a challenging problem. This paper presents a multistep method. First, a detector that is trained to learn a discriminative model of the appearance is combined with an explicit model of the distribution of respiratory and esophageal air. In the next step, prior shape knowledge is incorporated using a Markov chain model. We follow a "detect and connect" approach to obtain the maximum a posteriori estimate of the approximate esophagus shape from hypothesis about the esophagus contour in axial image slices. Finally, the surface of this approximation is nonrigidly deformed to better fit the boundary of the organ. The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic. Cross-validation on 144 CT scans showed that the Markov chain based approach clearly outperforms the particle filter. It segments the esophagus with a mean error of 1.80 mm in less than 16 s on a standard PC. This is only 1 mm above the interobserver variability and can compete with the results of previously published semiautomatic methods.     

Interference aware cooperative routing for wireless ad hoc networks

In this paper, we propose a cooperative approach for routing in wireless ad hoc networks. Our solution improves the interference distribution in the network, with an immediate positive impact on the throughput performance and energy efficiency. In determining new routes, we consider not only the cost associated with the current route, but also the potential interference impact of the route on the neighboring nodes.We use this cooperative approach to determine routes for CDMA ad hoc networks, which are known to be severely limited in performance by the near–far effect. Our simulation results using cooperative routing show an improvement in throughput of up to 60% compared to the classic minimum energy routing approach. This improvement is achieved at the expense of only a slight increase in the average energy per bit transmission for an end-to-end path.