While playing a fundamental role in shape understanding, the medial axis is known to be sensitive to small boundary perturbations. Methods for pruning the medial axis are usually guided by some measure of significance. The majority of significance measures over the medial axes of 3D shapes are locally defined and hence unable to capture the scale of features. We introduce a global significance measure that generalizes in 3D the classical Erosion Thickness (ET) measure over the medial axes of 2D shapes. We give precise definition of ET in 3D, analyze its properties, and present an efficient approximation algorithm with bounded error on a piece-wise linear medial axis. Experiments showed that ET outperforms local measures in differentiating small boundary noise from prominent shape features, and it is significantly faster to compute than existing global measures. We demonstrate the utility of ET in extracting clean, shape-revealing and topology-preserving skeletons of 3D shapes.

The safety situation of China's hazmat tanker transportation is grim. Such accidents not only have high spill percentages and consistently large spills but they can also cause serious consequences, such as fires and explosions. Improving the training of drivers and the quality of vehicles, deploying roll stability aids, enhancing vehicle inspection and maintenance, and developing good delivery schedules may all be considered effective measures for mitigating hazmat tanker accidents, especially severe crashes.

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Mapping a source mesh into a target domain while preserving local injectivity is an important but highly non-trivial task. Existing methods either require an already-injective starting configuration, which is often not available, or rely on sophisticated solving schemes. We propose a novel energy form, called Total Lifted Content (TLC), that is equipped with theoretical properties desirable for injectivity optimization. By lifting the simplices of the mesh into a higher dimension and measuring their contents (2D area or 3D volume) there, TLC is smooth over the entire embedding space and its global minima are always injective. The energy is simple to minimize using standard gradient-based solvers. Our method achieved 100% success rate on an extensive benchmark of embedding problems for triangular and tetrahedral meshes, on which existing methods only have varied success.

How drivers' visual characteristics change as they pass tunnels was studied. Firstly, nine drivers' test data at tunnel entrance and inside sections using eye movement tracking devices were recorded. Then the transfer function of BP artificial neural network was employed to simulate and analyze the variation of the drivers' eye movement parameters. The relation models between eye movement parameters and the distance of the tunnels were established. In the analysis of the fixation point distributions, the analytic coordinates of fixations in visual field were clustered to obtain different visual area of fixations by utilizing dynamic cluster theory. The results indicated that, at 100 meters before the entrance, the average fixation duration increased, but the fixations number decreased substantially. After 100 meters into the tunnel, the fixation duration started to decrease first and then increased. The variations of drivers' fixation points demonstrated such a pattern of change as scatter, focus, and scatter again. While driving through the tunnels, drivers presented a long time fixation. Nearly 61.5% subjects' average fixation duration increased significantly. In the tunnel, these drivers pay attention to seven fixation points areas from the car dashboard area to the road area in front of the car.

Coastal surveillance video helps officials to obtain on-site visual information on maritime traffic situations, which benefits building up the maritime transportation detection infrastructure. The previous ship detection methods focused on detecting distant small ships in maritime videos, with less attention paid to the task of ship detection from coastal surveillance video. To address this challenge, a novel framework is proposed to detect ships from coastal maritime images in three typical traffic situations in three consecutive steps. First the Canny detector is introduced to determine the potential ship edges in each maritime frame. Then, the self-adaptive Gaussian descriptor is employed to accurately rule out noisy edges. Finally, the morphology operator is developed to link the detected separated edges to connected ship contours. The model's performance is tested under three typical maritime traffic situations. The experimental results show that the proposed ship detector achieved satisfactory performance (in terms of precision, accuracy and time cost) compared with other state-of-the-art algorithms. The findings of the study offer the potential of providing real-time visual traffic information to maritime regulators, which is crucial for the development of intelligent maritime transportation.

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It can be concluded that the special light zones can help relieve drivers' vision fatigue to some extent and further develop certain visual stimulus that can enhance drivers' attention. The study would provide a scientific basis for safety measurement implementation in extra-long tunnels.