自拍相机运动手势交互界面的设计与实现

随着智能移动终端的发展及摄像镜头的小型化,自拍变得越来越流行。如何设计新型自拍交互方法使得用户在自拍过程中能够自由、实时地控制相机是自拍相机交互界面的关键问题。提出利用基于视觉的运动手势交互界面的新方法,使自拍过程中用户只要挥一挥手臂就可以实现与自拍相机的交互功能。使用手势交互的方法,用户可以把相机放在任意的平台上,自由地摆出各种自拍姿态,增加了自拍的丰富性,提高了用户体验。主要提出挥手及画圈两种交互手势,通过组合应用可以实现丰富高效的自拍交互控制功能,如快门控制、白平衡,曝光度等。手势的识别利用相机摄像的实时图像进行处理,采用稀疏光流算法来识别运动手势。用户评估实验表明,所提出运动手势自拍交互界面具有较好的交互效率以及良好的用户满意度,两种手势的识别效率约为85%。     

Efficient generic face model fitting to images and videos

In this paper we present a robust and lightweight method for the automatic fitting of deformable 3D face models on facial images. Popular fitting techniques such as those based on statistical models of shape and appearance require a training stage based on a set of facial images and their corresponding facial landmarks, which have to be manually labeled. Therefore, new images in which to fit the model cannot differ too much in shape and appearance (including illumination variation, facial hair, wrinkles, etc.) from those used for training. By contrast, our approach can fit a generic face model in two steps: (1) the detection of facial features based on local image gradient analysis and (2) the backprojection of a deformable 3D face model through the optimization of its deformation parameters. The proposed approach can retain the advantages of both learning-free and learning-based approaches. Thus, we can estimate the position, orientation, shape and actions of faces, and initialize user-specific face tracking approaches, such as Online Appearance Models (OAMs), which have shown to be more robust than generic user tracking approaches. Experimental results show that our method outperforms other fitting alternatives under challenging illumination conditions and with a computational cost that allows its implementation in devices with low hardware specifications, such as smartphones and tablets. Our proposed approach lends itself nicely to many frameworks addressing semantic inference in face images and videos.     

基于计算机视觉及深度学习的无人机手势控制系统

传统的无人机人机交互需要专门的设备和专业的训练,便捷新颖的交互方式往往更令人青睐。利用普通相机,对基于计算机视觉以及深度学习的无人机手势控制系统进行了研究。该系统首先利用快速跟踪算法在视频序列中提取出操作者所在区域,大大减少后续视频处理压力的同时去除了复杂背景以及相机漂移的影响。其次,根据动作的时间信息,用不同颜色编码光流特征,叠加在一张图片上,将视频转换为同时包含时间特征以及空间特征的彩色纹理图。最后,利用卷积神经网络对彩色纹理图进行学习及分类,根据分类结果生成控制无人机的指令。该系统每0.4 s对1.6 s内的动作进行一次判定,利用卷积神经网络对图片的分类实现实时性的人机交互,系统在60 m范围内的识别准确率在93%以上,在室内和室外环境下,操作者可以通过模仿指令动作方便地控制无人机。     

Learning gaze biases with head motion for head pose-free gaze estimation

When estimating human gaze directions from captured eye appearances, most existing methods assume a fixed head pose because head motion changes eye appearance greatly and makes the estimation inaccurate. To handle this difficult problem, in this paper, we propose a novel method that performs accurate gaze estimation without restricting the user's head motion. The key idea is to decompose the original free-head motion problem into subproblems, including an initial fixed head pose problem and subsequent compensations to correct the initial estimation biases. For the initial estimation, automatic image rectification and joint alignment with gaze estimation are introduced. Then compensations are done by either learning-based regression or geometric-based calculation. The merit of using such a compensation strategy is that the training requirement to allow head motion is not significantly increased; only capturing a 5-s video clip is required. Experiments are conducted, and the results show that our method achieves an average accuracy of around 3° by using only a single camera.     

A versatile interaction framework for robot programming based on hand gestures and poses

This paper proposes a framework for industrial and collaborative robot programming based on the integration of hand gestures and poses. The framework allows operators to control the robot via both End-Effector (EE) and joint movements and to transfer compound shapes accurately to the robot. Seventeen hand gestures, which cover the position and orientation controls of the robotic EE and other auxiliary operations, are designed according to cognitive psychology. Gestures are classified by a deep neural network, which is pre-trained for two-hand pose estimation and fine-tuned on a custom dataset, achieving a test accuracy of 99%. The index finger’s pointing direction and the hand’s orientation are extracted via 3D hand pose estimation to indicate the robotic EE’s moving direction and orientation, respectively. The number of stretched fingers is detected via two-hand pose estimation to represent decimal digits for selecting robot joints and inputting numbers. Finally, we integrate these three manners seamlessly to form a programming framework.We conducted two interaction experiments. The reaction time of the proposed hand gestures in indicating randomly given instructions is significantly less than that of other gesture sets, such as American Sign Language (ASL). The accuracy of our method in compound shape reconstruction is much better than that of hand movement trajectory-based methods, and the operating time is comparable with that of teach pendants.     

一种基于改进的LEA头部姿态估计方法

局部嵌入分析(LEA)是图嵌入化的局部线性嵌入(LLE)方法。在头姿态估计问题上,选择局部邻域时只考虑属于同一类的姿态,但失去了相邻姿态的几何拓扑信息。为此,提出一种改进的邻域选择方法,充分利用先验姿态信息,使降维后的流形更加平滑,同类姿态互相靠近,不同类姿态之间的距离随着姿态差值变大而增大,且能够使训练及测试样本的低维流形更加靠近,降低了估计误差。在Facepix人脸数据库上的实验证明了该方法的有效性。     

State of charge estimation for lithium-ion batteries: An adaptive approach

State of charge (SoC) estimation is of key importance in the design of battery management systems. An adaptive SoC estimator, which is named AdaptSoC, is developed in this paper. It is able to estimate the SoC in real time when the model parameters are unknown, via joint state (SoC) and parameter estimation. The AdaptSoC algorithm is designed on the basis of three procedures. First, a reduced-complexity battery model in state-space form is developed from the well-known single particle model (SPM). Then a joint local observability/identifiability analysis of the SoC and the unknown model parameters is performed. Finally, the SoC is estimated simultaneously with the parameters using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.     

Facial neuromuscular signal classification by means of least square support vector machine for MuCI

Facial neuromuscular signal has recently drawn the researchers’ attention to its outstanding potential as an efficient medium for Muscle Computer Interface (MuCI) applications. The proper analysis of such electromyogram (EMG) signals is essential in designing the interfaces. In this article, a multiclass least-square support vector machine (LS-SVM) is proposed for classification of different facial gestures EMG signals. EMG signals were captured through three bi-polar electrodes from ten participants while gesturing ten different facial states. EMGs were filtered and segmented into non-overlapped windows from which root mean square (RMS) features were extracted and then fed to the classifier. For the purpose of classification, different models of LS-SVM were constructed while tuning the kernel parameters automatically and manually. In the automatic mode, 48 models were formed while parameters of linear and radial basis function (RBF) kernels were tuned using different optimization techniques, cost functions and encoding schemes. In the manual mode, 8 models were shaped by means of the considered kernel functions and encoding schemes. In order to find the best model with a reliable performance, constructed models were evaluated and compared in terms of classification accuracy and computational cost. Results reported that the model including RBF kernel which was tuned manually and encoded by one-versus-all scheme provided the highest classification accuracy (93.10%) and consumed 0.98 s for training. It was indicated that automatic models were outperformed since they required too much time for tuning the parameters without any meaningful improvement in the final classification accuracy. The robustness of the selected LS-SVM model was evaluated through comparison with Support Vector Machine, fuzzy C-Means and fuzzy Gath-Geva clustering techniques.     

An evidence‐based approach to object pose estimation from LiDAR measurements in challenging environments

This paper addresses the problem of estimating object pose from high‐density LiDAR measurements in unpredictable field robotic environments. Point‐cloud measurements collected in such environments do not lend themselves to providing an initial estimate or systematic segmentation of the point‐cloud. A novel approach is presented that evaluates measurements individually for the evidence they provide to a collection of pose hypotheses. A maximum evidence strategy is constructed that is based in the idea that the most likely pose must be that which is most consistent with the observed LiDAR range measurements. This evidence‐based approach is shown to handle the diversity of range measurements without an initial estimate or segmentation. The method is robust to dust. The approach is demonstrated by two pose estimation problems associated with the automation of a large mining excavator.     

An improved discriminative common vectors and support vector machine based face recognition approach

An improved discriminative common vectors and support vector machine based face recognition approach is proposed in this paper. The discriminative common vectors (DCV) algorithm is a recently addressed discriminant method, which shows better face recognition effects than some commonly used linear discriminant algorithms. The DCV is based on a variation of Fisher’s Linear Discriminant Analysis for the small sample size case. However, for multiclass problem, the Fisher criterion is clearly suboptimal. We design an improved discriminative common vector by adjustment for the Fisher criterion that can estimate the within-class and between-class scatter matrices more accurately for classification purposes. Then we employ support vector machine as the classifier due to its higher classification and higher generalization. Testing on two public large face database: ORL and AR database, the experimental results demonstrate that the proposed method is an effective face recognition approach, which outperforms several representative recognition methods.     

An efficient approach for face recognition based on common eigenvalues

In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination.     

An ICA based approach for solving profit based unit commitment problem market

With the advent of paralleling and implementation of restructuring in the power market, some routine rules and patterns of traditional market should be accomplished in a way different from the past. To this end, the unit commitment (UC) scheduling that has once been aimed at minimizing operating costs in an integrated power market, is metamorphosed to profit based unit commitment (PBUC) by adopting a new schema, in which generation companies (GENCOs) have a common tendency to maximize their own profit. In this paper, a novel optimization technique called imperialist competitive algorithm (ICA) as well as an improved version of this evolutionary algorithm are employed for solving the PBUC problem. Moreover, traditional binary approach of coding of initial solutions is replaced with an improved integer based coding method in order to reduce computational complexity and subsequently ameliorate convergence procedure of the proposed method. Then, a sub-ICA algorithm is proposed to obtain optimal generation power of thermal units. Simulation results validate effectiveness and applicability of the proposed method on two scenarios: (a) a set of unimodal and multimodal standard benchmark functions, (b) two GENCOs consist of 10 and 100 generating units.     

Feature extraction and feature based design approaches in the development of design interface for process planning

The quest for completely automated process planning systems has exposed the lack of techniques capable of automatically understanding the stored CAD models in a manner suitable for process planning. Most current generations of process planning systems have used the ability of humans to translate the part drawing requirements into a form suitable for computer aided process planing. Recently, research advances have been made to improve the understanding of computer stored 3-D part models. The two approaches used are feature recognition and feature based design. This paper presents a state of the art review of feature recognition techniques developed and presents feature based design as an alternative. Process planning systems developed using both approaches are presented.     

Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method

With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classification techniques, genetic programming, support vector model etc. Computational time for running a model has a great importance in the 21st century. The algorithms or models with less computational time are more efficient and thus gives more profit to the banks or firms. In this study, we proposed a new strategy to reduce the computational time for credit scoring. In this approach we have used SVM incorporated with the concept of reduction of features using F score and taking a sample instead of taking the whole dataset to create the credit scoring model. We run our method two real dataset to see the performance of the new method. We have compared the result of the new method with the result obtained from other well known method. It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.     

An integrated approach for scene understanding based on Markov Random Field model

In this paper, we propose a Markov Random Field model-based approach as a unified and systematic way for modeling, encoding and applying scene knowledge to the image understanding problem. In our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm. More specifically, the image is first segmented into a set of disjoint regions by a conventional region-based segmentation technique which operates on image pixels, and a Region Adjacency Graph (RAG) is then constructed from the resulting segmented regions based on the spatial adjacencies between regions. Our scheme then proceeds on the RAG by defining the region merging and labeling problem based on the MRF models. In the MRF model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into the energy function to be minimized by a general optimization technique. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process such that the erroneous segmentation and misinterpretation due to incomplete knowledge about each problem domain can be compensately recovered by continuous estimation of the single unified energy function. We exploit the proposed scheme to segment and interpret natural outdoor scene images.     

Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance

Scheduling of single machine in manufacturing systems is especially complex when the order arrivals are dynamic. The complexity of the problem increases by considering the sequence-dependent setup times and machine maintenance in dynamic manufacturing environment. Computational experiments in literature showed that even solving the static single machine scheduling problem without considering regular maintenance activities is NP-hard. Multi-agent systems, a branch of artificial intelligence provide a new alternative way for solving dynamic and complex problems. In this paper a collaborative multi-agent based optimization method is proposed for single machine scheduling problem with sequence-dependent setup times and maintenance constraints. The problem is solved under the condition of both regular and irregular maintenance activities. The solutions of multi-agent based approach are compared with some static single machine scheduling problem sets which are available in the literature. The method is also tested under real-time manufacturing environment where computational time plays a critical role during decision making process.     

Industry structure modeling for competitive analysis: An integrated AI and systems approach

This paper describes a new approach to model the industry structure that will determine the market share equilibrium in a competitive environment. This approach takes advantage of certain developments in the Expert Systems area in the field of Artificial Intelligence to organize qualitative knowledge of industry structure, and combines such a knowledge base with mathematical systems modeling techniques to develop a market share equilibrium model. The rich structural information embedded in the model makes it potentially a very powerful tool for use in industry assessment and competitive strategy development. One version of the model is implemented in SRI International. The system is fully interactive and is currently used to assess the competitive environment for the polyolefins industry.     

脑-机接口中基于相似关系的MRPs双滤波特征提取算法

针对基于EEG的脑-机接口(BCI)实验数据分布不明朗的特点,双滤波模式(DFP)算法利用样本模式相似性来优化BCI的分类特征——运动相关电位(MPPs) 特征的空间(即电极位置)和时间投影方向,使得映射后异类样本模式差异性与同类相似性的比值最大化。该算法考虑MRPs特征对时间、空间的敏感性,并以自适应的方式挖掘它们适合分类的信息;优化时不需要进行样本数据分布假设,符合BCI数据特点。最后,DFP算法对BCI competition I、II两组数据进行实验,识别效果均高于相关比赛的最好成绩,这表明DFP算法能有效提取MRPs特征。     

An integrated state space partition and optimal control method of multi-model for nonlinear systems based on hybrid systems

Multilinear model approach turns out to be an ideal candidate for dealing with nonlinear systems control problem. However, how to identify the optimal active state subspace of each linear subsystem is an open problem due to that the closed-loop performance of nonlinear systems interacts with these subspaces ranges. In this paper, a new systematic method of integrated state space partition and optimal control of multi-model for nonlinear systems based on hybrid systems is initially proposed, which can deal with the state space partition and associated optimal control simultaneously and guarantee an overall performance of nonlinear systems consequently. The proposed method is based on the framework of hybrid systems which synthesizes the multilinear model, produced by nonlinear systems, in a unified criterion and poses a two-level structure. At the upper level, the active state subspace of each linear subsystem is determined under the optimal control index of a hybrid system over infinite horizon, which is executed off-line. At the low level, the optimal control is implemented online via solving the optimal control of hybrid system over finite horizon. The finite horizon optimal control problem is numerically computed by simultaneous method for speeding up computation. Meanwhile, the model mismatch produced by simultaneous method is avoided by using the strategy of receding-horizon. Simulations on CSTR (Continuous Stirred Tank Reactor) confirm that a superior performance can be obtained by using the presented method.