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Due to the prevalence of GPS-enabled devices and wireless communication technology, spatial trajectories that describe the movement history of moving objects are being generated and accumulated at an unprecedented pace. However, a raw trajectory in the form of sequence of timestamped locations does not make much sense for humans without semantic representation. In this work we aim to facilitate human's...
This paper proposes a new approach for a mobile robot to avoid obstacles based self-defined landmarks when it moves in the indoor environment. In this work, a 2D laser rangefinder is employed to detect the interior environment. The environmental maps, including global map and local map, are built by a series of feature extraction algorithms. Then, depending on self-defining landmarks, the mobile robot...
Complex system is a very common objective reality, the study about complex problem is the frontiers and focuses of science. Complex system which always is hierarchical and has multi-layers, it certainly contains qualitative and quantitative information, and the data of it always are multi-dimensional. This paper proposed that in a complex hierarchical system background, through the method of expressing...
Visual saliency plays an important role in biological vision. This paper proposes a novel feature-based approach of visual saliency detection for natural color images. The saliency for each pixel is determined by two features: the structure information and the color information, which represent local information of pixels. The structure information is represented by local steering kernel and the color...
Locality Preserving Projections (LPP) is a linear projective map that optimally preserves the neighborhood structure of the data set. Though LPP has been applied in many fields, it has limits to solve recognition problem. Thus, a new palmprint recognition method is proposed based on Kernel Locality Preserving Projections (KLPP). Different from LPP method, KLPP not only describes the nonlinear correlations...
In this paper, a kernel uncorrelated adjacent-class discriminant analysis (KUADA) approach is proposed for image recognition. The optimal nonlinear discriminant vector obtained by this approach can differentiate one class and its adjacent classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices in kernel space using the Fisher criterion...
Due to the increasing number of documents in digital form, the automated text categorization (TC) has become more and more promising in the last ten years. A TC system can automatically assign a document with the most suitable category, but the reason for such an assignment is usually unknown by users. To make the TC system be interpretable, it is necessary to select a group of keywords, or termed...
With the Internet developing, there are many documents on the same topic which contains redundance. Multi-Document summarization is a technology of natural languages processing, which extract important information from multiple texts about the same topic according to the compression ratio. Sentence selection is an important part of Multi-document summarization .In this paper, we design a calculate...
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