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In recent years, the smartphones which are integrated with many high-accuracy sensors have become very popular. Utilizing the development of sensors, developers have done many useful researchers. Indoor positioning navigation is an interesting and hot field of them. Step detecting and counting are key technologies in indoor positioning. In this paper, we propose a novel method to count steps which is an improvement of the classical steps detection method, the peak detection method of the acceleration. Our proposed scheme consists of two parts: the scoring part and adaptive window length part. Two adaptive window length algorithms which can adapt to the varying velocity are also proposed in this paper. In our experiments, a smartphone-Nubia NX513J is handled, with screen facing upwards to record the accelerometer data. The scoring algorithm of acceleration in the process of step detection is tested in two kinds of path: a straight path and a U-shaped path in which it shows better results than conventional peak detection method.

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This paper describes a vision-based obstacle detection system for Unmanned Surface Vehicle (USV) towards the aim of real-time and high performance obstacle detection on the sea surface. By using both the monocular and stereo vision methods, the system offers the capacity of detecting and locating multiple obstacles in the range from 30 to 100 meters for high speed USV which runs at speeds up to 12 knots. Field tests in the real scenes have been taken and the obstacle detection system for USV is proven to provide stable and satisfactory performance.

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We consider parallel computation for Gaussian process calculations to overcome computational and memory constraints on the size of datasets that can be analyzed. Using a hybrid parallelization approach that uses both threading (shared memory) and message-passing (distributed memory), we implement the core linear algebra operations used in spatial statistics and Gaussian process regression in an R package called bigGP that relies on C and MPI. The approach divides the matrix into blocks such that the computational load is balanced across processes while communication between processes is limited. The package provides an API enabling R programmers to implement Gaussian process-based methods by using the distributed linear algebra operations without any C or MPI coding. We illustrate the approach and software by analyzing an astrophysics dataset with n = 67, 275 observations.

In this paper, we propose a novel splatting framework for clutter reduction and pattern revealing in parallel coordinates. Our framework consists of two major components: a polyline splatter for cluster detection and a segment splatter for clutter reduction. The cluster detection is performed by splatting the lines one by one into the parallel coordinates plots, and for each splatted line we enhance its neighboring lines and suppress irrelevant ones. To reduce visual clutter caused by line crossings and overlappings in the clustered results, we provide a segment splatter which represents each polyline by one segment and splats these segments with different speeds, colors, and lengths from the leftmost axis to the rightmost axis. Users can interactively control both the polyline splatting and the segment splatting processes to emphasize the features they are interested in. The experimental results demonstrate that our framework can effectively reveal some hidden patterns in parallel coordinates.

Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships. These different tasks are typically treated as separate ones. However, they bear strong connections: good regions should respect the semantic labels; support can only be defined for meaningful regions; support relationships strongly depend on semantics. In this paper, we therefore introduce an approach to jointly segment the instances and infer their semantic labels and support relationships from a single input image. By exploiting a hierarchical segmentation, we formulate our problem as that of jointly finding the regions in the hierarchy that correspond to instances and estimating their class labels and pairwise support relationships. We express this via a Markov Random Field, which allows us to further encode links between the different types of variables. Inference in this model can be done exactly via integer linear programming, and we learn its parameters in a structural SVM framework. Our experiments on NYUv2 demonstrate the benefits of reasoning jointly about all these subtasks of indoor scene parsing.