A Survey of Recognizing Action from Single Still Images

人体行为识别是计算机视觉的研究难点与热点,目前大部分研究者主要针对视频中的行为展开研究.然而,人类的视觉往往根据单张图片就可判断图片中发生的行为.基于单张静态图像的人体行为识别,挑战性更大,是近年来人体行为识别研究的一个趋势,更是探索人类视觉奥秘的一个很好切入点.本文对单张静态图像的人体行为识别方法进行梳理,将其分为三类,最后对其未来研究方向进行展望.Human action recognition is a difficult and active research area in computer vision.At present,most of researchers in this field focus on recognizing action from video.However,human can understand human action based on a single picture.Recognize action from single still images has more challenge and is a trend in action recognition in recent years,but also a good entry point to explore the mysteries of human vision.In this paper,we sort out the methods of recognizing action from single still images and classify these methods into three categories.At last,the future research directions are discussed.国家自然科学基金项目(60873179);高等学校博士学科点专项科研基金项目(20090121110032);深圳市科技计划项目-基础研究(JC200903180630A);深圳市科技研发基金项目-深港创新圈计划(ZYB200907110169A);湖南省科技厅科研项目(2010TC2006);教育厅科研项目资助(09A046

A级数据中心综合能源系统多目标优化设计和调度

采用分布式综合能源系统取代数据中心的传统供能系统,是降低数据中心运行费用的有效措施。考虑A级数据中心的供能可用度,设计了一种分布式综合能源系统,并基于混合整数非线性规划方法,建立了综合经济和环境效益的多目标优化设计和调度模型,选取ε-约束法对相互制约的多目标问题进行处理,在通用代数建模系统(GAMS)中建模并调用LINDOGLOBAL求解器进行优化求解。采用多维偏好分析线性规划决策法从非劣解集中选出最终解,并通过逼近于理想解的排序方法进行验证。算例结果表明,优化后的分布式综合能源系统能够保证A级数据中心的供能可用度,同时在降低年总费用和减排二氧化碳方面均具优势

Hyperspectral image classification based on spectral-spatial combination features and graph cut

高光谱图像中存在着特征维度高而训练集小的问题。为解决该问题,提出了一种2步走的分类方法:1)通过支持向量机对图像进行初步分类,根据分类结果计算出每个类别的均值特征;2)使用1)计算出来的均值特征作为能量函数的数据项,然后利用图割原理对图像做二次分类。实验中发现:空间上相近的像素点往往具有相似的特征,且属于同一个类别。针对这种现象,提取一个将谱域特征和空域特征相结合的新特征。该特征既包含了光谱信息也包含了空间信息,具有较好的分类性能和鲁棒性。在IndIAn PInE数据集和PAVIA unIVErSITy数据集进行实验,实验结果表明了本文提出方法的有效性。The high-dimension of the feature vs.small-size of training set is an unsolved problem in the hyperspectral image classification task.To solve this problem a two-step classification method is proposed.Firstly,a preliminary classification is performed by the support vector machine( SVM) and the classification results are used to calculate the mean feature( MF) of each class.Secondly,a classification based on the graph cut theory is applied with the MFs as an input of the energy function.The experimental results showed that spatially nearby pixels have large possibilities of having the same label and similar features.Therefore,a new feature called spectral-spatial combination( SSC) is extracted that combines the spectral-based feature and spatial-based feature.The SSC feature contains the related spectral and spatial information of each pixel and provides better classification performance and robustness.Experiment results on the Indian Pine dataset and the Pavia University dataset demonstrated the effectiveness of the proposed method.国家自然科学基金资助项目(61202143); 福建省自然科学基金资助项目(2013J05100;2010J01345;2011J01367); 湖南省自然科学基金资助项目(12JJ2040

RBFD: a Robust Image Local Binary Feature Descriptor

针对传统浮点型特征描述子占用空间大、匹配速度慢的问题,提出一种基于梯度统计信息比较的局部二值特征描述子.通过对比特征点邻域梯度统计信息生成二值特征描述子,再利用多邻域和多分块策略提高描述子判别力,最后通过近似简化的AdA bOOST算法实现描述子降维.实验结果表明,与已有描述子相比,文中提出的描述子在实现快速生成的同时其鲁棒性更强.The traditional floating feature descriptors are in high memory load and slow in matching.To best address these problems, this paper proposed a novel binary feature descriptor based on gradient statistic information comparison.Firstly, the image patch around the keypoint is divided into sub-regions, and our binary descriptor is constructed by comparing the gradient statistic information of these sub-regions.Then, a multi-gridding and multi-support region strategy is applied to boost the discrimination of our descriptor.Finally, a simplified Ada Boost algorithm is applied to realize the descriptor dimension reduction.The experimental results show that our descriptor is both efficient in construction and robust to compare with the state-of-the-art methods.国家自然科学基金(61373076;61202143); 厦门大学中央高校项目(2013121026;2011121052); 厦门大学985平台建设费项目; 高等学校博士学科点专项科研基金(201101211120024); 福建省自然科学基金(2013J05100;2010J01345;2011J01367); 湖南省自然科学基金(12JJ2040); 湖南省教育厅科研项目(09A046); 厦门市科技重点项目(3502Z20123017); 深圳市战略性新兴产业发展专项基金(JCY

Action recognition based on the angle histogram of key parts

当前的姿态表示的行为识别方法通常对姿态的准确性做了很强的假设,而当姿态分析不精确时,这些现有方法的识别效果不佳。提出了一种低维的、鲁棒的基于关键肢体角度直方图的人体姿态特征描述子,用于将整个动作视频映射成一个特征向量。同时,还在特征向量中引入共生模型,用以表示肢体间的关联性。最后,设计了分层的SVM分类器,第1层主要用于选择高判别力的肢体作为关键肢体,第2层则利用关键肢体的角度直方图并作为特征向量,进行行为识别。实验结果表明,基于关键肢体角度直方图的动作特征具有较好的判别能力,能更好地区分相似动作,并最终取得了更好的识别效果。The current pose-based methods usually make a strong assumption for the accuracy of pose,but when the pose analysis is not precise,these methods cannot achieve satisfying results of recognition.Therefore,this paper proposed a low-dimensional and robust descriptor on the gesture feature of the human body based on the angle histogram of key limbs,which is used to map the entire action video into an feature vector.A co-occurrence model is introduced into the feature vector for expressing the relationship among limbs.Finally,a two-layer support vector machine( SVM) classifier is designed.The first layer is used to select highly discriminative limbs as key limbs and the second layer takes angle histogram of key limbs as the feature vector for action recognition.Experiment results demonstrated that the action feature based on angle histogram of key limbs has excellent judgment ability,may properly distinguish similar actions and achieve better recognition effect.国家自然科学基金资助项目(61202143); 福建省自然科学基金资助项目(2013J05100;2010J01345;2011J01367); 厦门市科技重点项目资助项目(3502Z20123017

关于湿地价值评估的种种法律争端

一、引言对不动产的价值评估是基于对其最佳使用价值的评估。当然,这种分析只限于对该资产的合法使用而言。关于限制湿地变动其用途的重要法律法规,一定会影响其对于许多预期买家及对于其业主而言的价值评估。本文将列举和探讨一些主要的联邦法律法规条款,并对在联邦和州有关湿地的征用和监管过程中出现的一些法律和价值评估的争端展开调查研究

Valuation of Wetlands: Legal Issues

对不动产的价值评估是基于对其最佳使用价值的评估。当然，这种分析只限于对该资产的合法使用而言。关于限制湿地变动其用途的重要法律法规，一定会影响其对于许多预期买家及对于其业主而言的价值评估。本文将列举和探讨一些主要的联邦法律法规条款，并对在联邦和州有关湿地的征用和监管过程中出现的一些法律和价值评估的争端展开调查研究。在其历史上，联邦政府（与各州和地方政府一道）已经不是第一次转变做法了，这次的转变则发生在湿地方面。然而，就在不久前，农民、当地居民和其他利益相关者由于受到鼓励——其实是受到奖励，去把湿地排干并改变为其用途，旨在使其更具价值。现行的法律和政策已经证明了这样的认识，即：湿地本身是有价值的。实际上，正如下文所描述那样，湿地是生态系统的重要组成部分，发挥着许多重要的功能，它们本身是有价值的，是值得受到保护的。译者单位：厦门大学评估研究中心（361005

声门下喉气管狭窄分度的数值模拟分析

根据真实人体喉气管CT影像数据构建了声门下喉气管狭窄的气道模型，并对这些模型进行了吸气条件下的计算流体力学仿真。分析比较了不同狭窄分度模型内的流场特征、不同喉气管部段的阻力特性以及喉气管壁面的压力与切应力分布，并对这些特性与临床病患表象的关联性进行了讨论。分析表明声门下喉气管狭窄分度与狭窄导致的呼吸道阻力变化具有良好的关联性，此外，喉气管狭窄处压力及剪应力效应对病程的影响也需要关注。</p

Improvement of Adaptive of Mean Shift Algorithm

MEAn SHIfT算法是一种广泛应用于计算机视觉、模式识别等领域的统计迭代算法,它使每一个点“漂移“到密度函数的局部极大值点,均值漂移的方向就是梯度方向,因此,漂移序列总是向着函数值增加最快的方向移动,并且每次移动的步长大小具有自适应性.本文研究了MEAn SHIfT算法移动步长的自适应性,对其进行改进,使其能够通过参数的适当调整得到优于原MEAn SHIfT算法的收敛速度,并从理论上证明了改进的MEAn SHIfT算法能够收敛.本文的实验也进一步验证了改进的MEAn SHIfT算法的收敛性,并对比了改进前后的MEAn SHIfT算法的收敛速度.Mean Shift algorithm is a statistics iterative algorithm which is widely used in computer vision and pattern recognition,it makes each point "shift" to the local maximum of density function.Shifting direction is gradient direction of density function at each point,accordingly,shift point is always moving towards the direction which makes density function increase fastest,furthermore,the move length is adaptive.This paper investigates the adaptive of move length of each point in Mean Shift algorithm and improves it,enhances its convergence rate,moreover,proves the convergence of the improved Mean Shift algorithm.Experiment in this paper validates the convergence of the improved Mean Shift algorithm,contrast convergence rate of improved Mean Shift algorithm with convergence rate of traditional Mean Shift algorithm.国家自然科学基金(60873179)资