SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), i.e.LR and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially correlated.However , for a multi-resolution scenario , there are no related works.Therefore , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces.Furthermore , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.     

Analyzing Qualitative Spatio-Temporal Calculi using Algebraic Geometry

Abstract

Qualitative spatial reasoning is based on calculi which comprise relations and operation tables that encode operations like relation composition. Designing a calculus involves determining these tables and analyzing reasoning properties—a demanding task that is susceptible to errors if performed manually. This paper is concerned with automating computation of operation tables and analysis of qualitative calculi over real-valued domains like the plane ². We present an approach to specify qualitative relations using polynomial equations that allows methods from algebraic geometry to be applied. This paper shows how reasoning with qualitative relations can be posed algebraically and demonstrates algebraic reasoning using Gröbner base analysis. We evaluate this approach and describe our implementation, which is freely available as part of the spatial reasoning toolbox SparQ.     

Reasoning about Depth and Motion from an Observer's Viewpoint

ABSTRACT

The goal of this paper is to present a logic-based formalism for representing knowledge about objects in space and their movements, and show how this knowledge could be built up from the viewpoint of an observer immersed in a dynamic world. In this paper space is represented using functions that extract attributes of depth, size and distance from snapshots of the world. These attributes compose a novel spatial reasoning system named Depth Profile Calculus (DPC). Transitions between qualitative relations involving these attributes are represented by an extension of this calculus called Dynamic Depth Profile Calculus (DDPC). We argue that knowledge about objects in the world could be built up via a process of abduction on DDPC relations.     

The Influence of Landmarks and Pre-exposure to a Structural Map During the Process of Spatial Knowledge Acquisition: A Study with Children and Adults in a Virtual Environment

ABSTRACT

This study investigated the effects of featural information (landmarks) and geometric information (pre-exposure to a structural map) and their possible interaction during the process of spatial knowledge acquisition of 8- and 11-year-old children and adults in a virtual environment. The study confirmed the well-known result of a developmental achievement in spatial cognition from childhood to adulthood. Although landmarks and the pre-exposure to a structural map did not affect the time to learn a specific route, they influenced the use of behavior in spatial learning and eased the acquisition of spatial knowledge measured by a route reversal and map-drawing tasks. Children and adults are able to integrate featural and geometric information in the spatial knowledge acquisition process in an environmental space, but their integration depends on the spatial processing stages that are investigated. Moreover, it was successfully demonstrated that the use of desktop virtual environments seems to be appropriate to investigate the development of spatial cognition.     

基于CBR的民用飞机维修决策系统研究

基于CBR的智能维修决策技术,可以为新型国产飞机维修大纲制订人员提供有用的决策信息,提高制定飞机维修大纲的智能化水平和效率。研究的核心问题就是基于CBR的维修任务和维修间隔制订方法,它们为维修决策支持系统提供理论指导与核心算法。最后开发了基于CBR、以SQL Server作为后台数据库管理系统的民用飞机维修决策系统,并以新舟600（MA600）飞机ATA32起落架系统组件为实例进行验证,效果良好。     

Mereological inheritance

In recent investigations of Spatial Reasoning, SpatialKnowledge Representation, and Geographic Information Systems, asignificant effort has been spent by many scholars of the areaabout the problem of representing properties of spatial objects bymeans of logical theories.An analogous effort has been the problem of analyzingthe qualitative relations which can be established between twospace regions. These investigations have led to a generalframework of the field known by the term ``mereo-topology'.Though both the above mentioned approaches have proved to besuccessful in the investigation of formal and practically relevantaspects of spatial objects, no attempt has been carried out in the direction of integrating the approaches and looking at the relationshipbetween a general logical theory of space and mereo-topology from an analytical point of view, in particular for exploiting thecombinatorial behaviour of such an integrated model.This paper intends to fill the gap and analyze the behaviour ofspatial formulae of a logical theory of space as objects which canbe classified based on the behaviour they exhibit with respect tothe parts and supertparts of the regions where they are true. Wename these categories of behaviourinheritance modalities.     

美育在思想政治教育中的地位与作用

从德育和美育的关系，美育和功能讨论美育在思想政治教育中的作用，并针对当代大学生的审美误区，论述了美育在思想政治教育中的作用及进行审美教育的必要性与迫切性。     

自适应空间机动目标跟踪算法

在空间目标跟踪问题中,目标机动导致的模型不匹配问题会导致滤波算法出现滞后现象。为了对空间机动目标进行快速跟踪,在平方根容积卡尔曼滤波〖（Square rootCubatureKalmanFilter,SCKF）的基础上,引入强跟踪滤波（StrongTrackingFilter,STF）的思想,推导得到了次优渐消因子在SCKF中的等价描述。并通过建立以矩匹配方法为基础的自适应机制,设计了兼顾滤波精确性和鲁棒性的自适应强跟踪平方根容积卡尔曼滤波（AdaptiveStrongTrackingSquare rootCubatureKalmanFilter,AST SCKF）算法。仿真结果表明,在目标机动前,AST-SCKF算法和SCKF算法的位置收敛精度相差不足1%;在目标机动后,AST-SCKF算法的位置和速度的收敛精度相对SCKF算法分别提高了95.19%和30.50%,同时,其收敛速度相对SCKF算法分别提高了57.20%和24.68%。