基于约束推理的车站咽喉图型识别及优化设计模型

通用型计算机辅助设计(CAD)软件中不包括铁路车站咽喉平面设计的工具,因此需要研究专业的设计平台。本文认为解决该问题的途径是,首先基于人机交互的图型环境,输入并建立车站咽喉的结构图型,然后通过有效的计算手段识别和判定这种咽喉结构,最后通过结构计算实现车站咽喉平面设计的目标。文章认为模块法比画线法输入咽喉结构图型效率更高,也便于识别。因此,将车站咽喉的基本图形分解为6种基本图元,并对这些图元进行了恰当的描述。应用矢量对接判定条件,识别咽喉结构图型中图元的连接关系。依这种关系,应用约束推理方法和最优化的数学模型,计算满足咽喉设计条件下最优岔心位置坐标和最大平行进路数,从而实现车站咽喉平面设计优化和自动化。

Online composite shape recognition based on relevance feedback

This paper describes a novel method of online composite shape recognition interms of the relevance feedback technology to capture a user's intentions incrementally, and adynamic user modeling method to adapt to various users' styles. First, the relevance feedback isadapted to refine the recognition results and reduce the ambiguity incrementally based on theestablishment of a feature-based vector model of a user's sketches. Secondly, a dynamic usermodeling is introduced to model the user's sketching habits based on recording and analyzinghistorical information incrementally. A model-based matching strategy is also employed in the methodto recognize sketches dynamically. Experiments prove that the proposed method is both effective andefficient.

Electromagnetic Model and Image Reconstruction Algorithms Based on EIT System

An intuitive 2D model of circular electrical impedance tomography （EIT） sensor with small size electrodes is established based on the theory of analytic functions. The validation of the model is proved using the result from the solution of Laplace equation. Suggestions on to electrode optimization and explanation to the ill-condition property of the sensitivity matrix are provided based on the model, which takes electrode distance into account and can be generalized to the sensor with any simple connected region through a conformal transformation. Image reconstruction algorithms based on the model are implemented to show feasibility of the model using experimental data collected from the EIT system developed in Tianjin University. In the simulation with a human chestlike configuration, electrical conductivity distributions are reconstructed using equi-potential backprojection （EBP） and Tikhonov regularization （TR） based on a conformal transformation of the model. The algorithms based on the model are suitable for online image reconstruction and the reconstructed results are aood both in size and position.

Bag-of-visual-words model for artificial pornographic images recognition

It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.

Tracking Target Identification Model Based on Multiple Algorithms

In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.

Probabilistic Model-Based Silhouette Refinement for Gait Recognition

An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed,and a relatively complete and high quality silhouette is obtained.The silhouettes are sequentially refined in two levels according to two different probabilistic models.The first level is within-sequence refinement.Each silhouette in a particular sequence is refined by an individual model trained by the gait images from current sequence.The second level is between-sequence refinement.All the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of pedestrians.The intention is to preserve the within-class similarity and to decrease the interaction between one class and others.Comparative experimental results indicate that the proposed algorithm is simple and quite effective,and it helps the existing recognition methods achieve a higher recognition performance.