Particle swarm optimization for time-optimal control design

In this paper,a particle swarm optimization(PSO)based method is proposed to obtain the time-optimal bang-bang control law for both linear and nonlinear systems.By introducing a penalty function,the method can be modified to deal with systems with constraints.Compared with existing computational methods,the proposed method can be implemented in a straightforward manner.The convergent solutions can be achieved by selecting suitable PSO parameters regardless of the initial guess of the switching times.A double integrator and a third-order nonlinear system are used to demonstrate the effectiveness and robustness of the proposed method.The method is applied to obtain the time-optimal control law for a high performance linear motion positioning system.The results show the practicality of the proposed algorithm.     

基于语义单元的足球视频事件检测

A semantic unit based event detection scheme in soccer videos is proposed in this paper. The scheme can be characterized as a three-layer framework. At the lowest layer, low-level features including color, texture, edge, shape, and motion are extracted. High-level semantic events are defined at the highest layer. In order to connect low-level features and high-level semantics, we design and define some semantic units at the intermediate layer. A semantic unit is composed of a sequence of consecutives frames with the same cue that is deduced from low-level features. Based on semantic units, a Bayesian network is used to reason the probabilities of events. The experiments for shoot and card event detection in soccer videos show that the proposed method has an encouraging performance.     

An assignment method for IPUs in distributed systems

In a distributed system,one of the most important thing is to establish an assignment method for distributing tasks.It is assumed that a distributed system does not have a central administrator,all independent processing units in this system want to cooperate for the best results,but they cannot know the conditions of one another,So in order to undertake the tasks in admirable proportions,they have to adjust their undertaking tasks only by selt-learning.In this paper,the performance of this system is analyzed by Markov chains,and a robust method of self-learning for independent processing units in this kind of systems is presented.This method can lead the tasks of the system to be distributed very well among all the independent processing units,and can also be used to solve the general assignment problem.     

Algorithms to Compute the Largest Invariant Set Contained in an Algebraic Set for Continuous-Time and Discrete-Time Nonlinear Systems

In this paper, some computational tools are proposed to determine the largest invariant set, with respect to either a continuous-time or a discrete-time system, that is contained in an algebraic set. In particular, it is shown that if the vector field governing the dynamics of the system is polynomial and the considered analytic set is a variety, then algorithms from algebraic geometry can be used to solve the considered problem. Examples of applications of the method(spanning from the characterization of the stability to the computation of the zero dynamics) are given all throughout the paper.     

Designing Motion Gesture Interfaces in Mobile Phones for Blind People

Despite the existence of advanced functions in smartphones, most blind people are still using old-fashioned phones with familiar layouts and dependence on tactile buttons. Smartphones support accessibility features including vibration, speech and sound feedback, and screen readers. However, these features are only intended to provide feedback to user commands or input. It is still a challenge for blind people to discover functions on the screen and to input the commands. Although voice commands are supported in smartphones, these commands are difficult for a system to recognize in noisy environments. At the same time, smartphones are integrated with sophisticated motion sensors, and motion gestures with device tilt have been gaining attention for eyes-free input. We believe that these motion gesture interactions offer more efficient access to smartphone functions for blind people. However, most blind people are not smartphone users and they are aware of neither the affordances available in smartphones nor the potential for interaction through motion gestures. To investigate the most usable gestures for blind people, we conducted a user-defined study with 13 blind participants. Using the gesture set and design heuristics from the user study, we implemented motion gesture based interfaces with speech and vibration feedback for browsing phone books and making a call. We then conducted a second study to investigate the usability of the motion gesture interface and user experiences using the system. The findings indicated that motion gesture interfaces are more efficient than traditional button interfaces. Through the study results, we provided implications for designing smartphone interfaces.     

Modeling in Multi-Resolution and Its Applications

It is becoming common for many designers to work together on very complex assemblies in a collaborative environment. To work efficiently in this environment, the capabilities to simplify the portions of an assembly and to reset it to the original resolution should be added to the current CAD systems. Thus operators realizing multi-resolution on B-rep were proposed in previous work. This paper illustrates a prototype multi-resolution system to integrate the proposed operators and its applications. The multi-resolution system can be used in similarity comparisons to reduce the calculation load and to get the full comparison result in the overall shape. Then the features recognized with this system can be used in more detailed comparisons. This system also can be used conceptually to solve security problems in collaborative design.     

Seam tracking and visual control for robotic arc welding based on structured light stereovision

A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type Ⅴgroove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.     

Smart objects identification system for robotic surveillance

Video surveillance is an active research topic in computer vision.In this paper,humans and cars identifcation technique suitable for real time video surveillance systems is presented.The technique we proposed includes background subtraction,foreground segmentation,shadow removal,feature extraction and classifcation.The feature extraction of the extracted foreground objects is done via a new set of afne moment invariants based on statistics method and these were used to identify human or car.When the partial occlusion occurs,although features of full body cannot be extracted,our proposed technique extracts the features of head shoulder.Our proposed technique can identify human by extracting the human head-shoulder up to 60%–70%occlusion.Thus,it has a better classifcation to solve the issue of the loss of property arising from human occluded easily in practical applications.The whole system works at approximately 16 29 fps and thus it is suitable for real-time applications.The accuracy for our proposed technique in identifying human is very good,which is 98.33%,while for cars identifcation,the accuracy is also good,which is 94.41%.The overall accuracy for our proposed technique in identifying human and car is at 98.04%.The experiment results show that this method is efective and has strong robustness.     

A distributed algorithm for multi-region problem in BEM

The situation of multi-region problem may oftem appear when boundary element method(BEM)is applied in practical problems especially in VLSI-CAD.It is difficult to deal with this problem if traditional methods are used.Particularly. when the problem to be solved contains a lot of materials,the advantages of using BEM such as simplicity,convenience and rapidity will be weakened due to the complexity of solving complex boundary element equation.In this paper a distributed algorithm for multi-region problem in BEM is presented.This algorithm has been implemented in a distributed system consisting of 3 workstations to extract VLSI layout parameters.The results show that the calculation time of this distributed algorithm is less than that of the traditional methods.The results also demonstrate that this algorithm can speed up the computation and has the features of parallelism and high efficiency.     

A novel visual interface for human-robot communication

The purpose of a robot is to execute tasks for people. People should be able to communicate with robots in a natural way. People naturally express themselves through body language using facial gestures and expressions. We have built a human-robot interface based on head gestures for use in robot applications. Our interface can track a person's facial features in real time (30 Hz video frame rate). No special illumination or facial makeup is needed to achieve robust tracking. We use dedicated vision hardware based on correlation image matching to implement the face tracking. Tracking using correlation matching suffers from the problems of changing shade and deformation or even disappearance of facial features. By using multiple Kalman filters we are able to overcome these problems. Our system can accurately predict and robustly track the positions of facial features despite disturbances and rapid movements of the head (including both translational and rotational motion). Since we can reliably track faces in real-time we are also able to recognize motion gestures of the face. Our system can recognize a large set of gestures (15) ranging from yes, no and may be to detecting winks, blinks and sleeping. We have used an approach that decomposes each gesture into a set of atomic actions, e.g. a nod for yes consists of an atomic up followed by a down motion. Our system can understand gestures by monitoring the transition between atomic actions.     

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.     

Dance performance evaluation using hidden Markov models

We present in this paper a hidden Markov model‐based system for real‐time gesture recognition and performance evaluation. The system decodes performed gestures and outputs at the end of a recognized gesture, a likelihood value that is transformed into a score. This score is used to evaluate a performance comparing to a reference one. For the learning procedure, a set of relational features has been extracted from high‐precision motion capture system and used to train hidden Markov models. At runtime, a low‐cost sensor (Microsoft Kinect) is used to capture a learner's movements. An intermediate step of model adaptation was hence requested to allow recognizing gestures captured by this low‐cost sensor. We present one application of this gesture evaluation system in the context of traditional dance basics learning. The estimation of the log‐likelihood allows giving a feedback to the learner as a score related to his performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Hand gesture recognition using two-level speed normalization,feature selection and classifier fusion

Hand gesture recognition provides an alternative way to many devices for human computer interaction. In this work, we have developed a classifier fusion based dynamic free-air hand gesture recognition system to identify the isolated gestures. Different users gesticulate at different speed for the same gesture. Hence, when comparing different samples of the same gesture, variations due to difference in gesturing speed should not contribute to the dissimilarity score. Thus, we have introduced a two-level speed normalization procedure using DTW and Euclidean distance-based techniques. Three features such as ‘orientation between consecutive points’, ‘speed’ and ‘orientation between first and every trajectory points’ were used for the speed normalization. Moreover, in feature extraction stage, 44 features were selected from the existing literatures. Use of total feature set could lead to overfitting, information redundancy and may increase the computational complexity due to higher dimension. Thus, we have tried to overcome this difficulty by selecting optimal set of features using analysis of variance and incremental feature selection techniques. The performance of the system was evaluated using this optimal set of features for different individual classifiers such as ANN, SVM, k-NN and Naïve Bayes. Finally, the decisions of the individual classifiers were combined using classifier fusion model. Based on the experimental results it may be concluded that classifier fusion provides satisfactory results compared to other individual classifiers. An accuracy of 94.78 % was achieved using the classifier fusion technique as compared to baseline CRF (85.07 %) and HCRF (89.91 %) models.     

A Real-Time Approach to the Spotting, Representation, and Recognition of Hand Gestures for Human–Computer Interaction

Aiming at the use of hand gestures for human–computer interaction, this paper presents a real-time approach to the spotting, representation, and recognition of hand gestures from a video stream. The approach exploits multiple cues including skin color, hand motion, and shape. Skin color analysis and coarse image motion detection are joined to perform reliable hand gesture spotting. At a higher level, a compact spatiotemporal representation is proposed for modeling appearance changes in image sequences containing hand gestures. The representation is extracted by combining robust parameterized image motion regression and shape features of a segmented hand. For efficient recognition of gestures made at varying rates, a linear resampling technique for eliminating the temporal variation (time normalization) while maintaining the essential information of the original gesture representations is developed. The gesture is then classified according to a training set of gestures. In experiments with a library of 12 gestures, the recognition rate was over 90%. Through the development of a prototype gesture-controlled panoramic map browser, we demonstrate that a vocabulary of predefined hand gestures can be used to interact successfully with applications running on an off-the-shelf personal computer equipped with a home video camera.     

Airwriting: a wearable handwriting recognition system

We present a wearable input system which enables interaction through 3D handwriting recognition. Users can write text in the air as if they were using an imaginary blackboard. The handwriting gestures are captured wirelessly by motion sensors applying accelerometers and gyroscopes which are attached to the back of the hand. We propose a two-stage approach for spotting and recognition of handwriting gestures. The spotting stage uses a support vector machine to identify those data segments which contain handwriting. The recognition stage uses hidden Markov models (HMMs) to generate a text representation from the motion sensor data. Individual characters are modeled by HMMs and concatenated to word models. Our system can continuously recognize arbitrary sentences, based on a freely definable vocabulary. A statistical language model is used to enhance recognition performance and to restrict the search space. We show that continuous gesture recognition with inertial sensors is feasible for gesture vocabularies that are several orders of magnitude larger than traditional vocabularies for known systems. In a first experiment, we evaluate the spotting algorithm on a realistic data set including everyday activities. In a second experiment, we report the results from a nine-user experiment on handwritten sentence recognition. Finally, we evaluate the end-to-end system on a small but realistic data set.     

一种用于异常行为检测的运动特征描述子

目前,用于描述视频中人群的运动信息大多是基于光流的速度描述子。事实上,加速度蕴含丰富的运动信息,能够提供速度描述子在描述复杂运动模式时缺失的信息,以更好地表征复杂的运动模式。文中研究了一种运动特征描述子,使用受限玻尔兹曼机模型进行异常行为检测。首先,提取视频中的光流场信息,计算帧间加速度光流;然后,对一个时空块中的加速度信息进行直方图统计,将若干帧的所有时空块直方图特征进行拼接,从而获得加速度描述子;最后,在仅包含正常行为的训练集上建立受限玻尔兹曼机模型,在测试阶段根据测试视频重建特征与原始特征的误差大小进行异常检测。实验表明,所提出的加速度描述子结合速度描述子,在UMN数据集与UCF-Web数据集上,ROC曲线下的面积分别达到了0.984与0.958,相较于其他算法,所提方法取得了更高的异常行为检测准确率。     

复杂背景下的手势分割与识别

目前在基于单目视觉的手势识别中,手势分割技术几乎都是基于简单的背景或者要求 手势者带有特殊颜色的手套,给人机交互增加了一定的限制.本文融合人手颜色信息和手势运 动信息,两次利用种子算法对复杂背景下的手势进行分割.根据分割出的手区域大大加速了运动 特征参数的提取,并结合手区域的形状特征,建立手势的时空表观模型.识别时,采用独立分布的 多状态高斯概率模型,进行时间规整.手势训练集和测试集的识别率分别为97.8%和95.6%.     

3D text segmentation and recognition using leap motion

In this paper, we present a method of Human-Computer-Interaction (HCI) through 3D air-writing. Our proposed method includes a natural way of interaction without pen and paper. The online texts are drawn on air by 3D gestures using fingertip within the field of view of a Leap motion sensor. The texts consist of single stroke only. Hence gaps between adjacent words are usually absent. This makes the system different as compared to the conventional 2D writing using pen and paper. We have collected a dataset that comprises with 320 Latin sentences. We have used a heuristic to segment 3D words from sentences. Subsequently, we present a methodology to segment continuous 3D strokes into lines of texts by finding large gaps between the end and start of the lines. This is followed by segmentation of the text lines into words. In the next phase, a Hidden Markov Model (HMM) based classifier is used to recognize 3D sequences of segmented words. We have used dynamic as well as simple features for classification. We have recorded an overall accuracy of 80.3 % in word segmentation. Recognition accuracies of 92.73 % and 90.24 % have been recorded when tested with dynamic and simple features, respectively. The results show that the Leap motion device can be a low-cost but useful solution for inputting text naturally as compared to conventional systems. In future, this may be extended such that the system can successfully work on cluttered gestures.