The ambiguity and complexity of medical cerebrovascular image makes the skeleton gained by conventional skeleton algorithm discontinuous, which is sensitive at the weak edges, with poor robustness and too many burrs. ...
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Terrain LOD algorithm is a dynamic and local dough sheet subduction algorithm. On the basis of the research on traditional quadtree algorithm, this paper proposed a new terrain LOD algorithm using quadtree. On deviati...
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Dataspace is a semi-structural data model for the management of large-scale heterogeneous data *** a dataspace,each data object consists of a set of attribute-value pairs to describe the internal properties of the obj...
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Dataspace is a semi-structural data model for the management of large-scale heterogeneous data *** a dataspace,each data object consists of a set of attribute-value pairs to describe the internal properties of the object and their relationships to other objects. The dataspace model provides a flexible query language that supports attribute-value pair-based predicate queries and semantic link queries. In this paper we introduce a Resource Space Model that extends the concepts of dataspace to make it more flexible by incorporating classification semantics. We model a dataspace as a set of Resource Spaces (RS) with each RS representing a kind of resources that share common attributes and can be put into the same category, either according to a user’s knowledge of the world or his query preferences. Our model is suitable for designing a user-tailored semantic view of a dataspace while at the same time without losing the schema-later data-centric nature. A practical query language is introduced to implement flexible query operations on the resource spaces of the dataspace. Examples show how the proposed model and language support both the schema query and the data query.
Co-occurrence histograms of oriented gradients (CoHOG) are powerful descriptors in object detection. In this paper, we propose to utilize a very large pool of CoHOG features with variable-location and variable-size bl...
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Co-occurrence histograms of oriented gradients (CoHOG) are powerful descriptors in object detection. In this paper, we propose to utilize a very large pool of CoHOG features with variable-location and variable-size blocks to capture salient characteristics of the object structure. We consider a CoHOG feature as a block with a special pattern described by the offset. A boosting algorithm is further introduced to select the appropriate locations and offsets to construct an efficient and accurate cascade classifier. Experimental results on public datasets show that our approach simultaneously achieves high accuracy and fast speed on both pedestrian detection and car detection tasks.
Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the s...
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Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In t...
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We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unl...
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This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in t...
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ISBN:
(纸本)9781424475421
This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method.
Identifying Full names/abbreviations for entities is a challenging problem in many applications, e.g. question answering and information retrieval. In this paper, we propose a general extraction method of extracting f...
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In this paper, we introduced an intelligent call center platform (ICCP) for communication enterprises. The platform is based on two broad suites of technologies: Knowledge management and natural language processing. F...
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ISBN:
(纸本)9781424463275
In this paper, we introduced an intelligent call center platform (ICCP) for communication enterprises. The platform is based on two broad suites of technologies: Knowledge management and natural language processing. First, we designed an enterprise knowledge modeling technology, and provided a knowledge management system for managing customer service knowledge. Then mobile customers are allowed to send Chinese queries, through either Internet tools (e.g. MSN, QQ, BBS and Fetion) or Wireless SM devices (e.g. mobile phones), to a multi-tier natural language understanding engine, and the engine retrieves the knowledge bases to find correct answers after understanding the queries. The ICCP has been successfully used in a domestic communication group since March 2009, and achieved a satisfactory performance.
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