Min Dong

A Gesture Recognition Method Based on Binocular Vision System

This paper demonstrates a gesture recognition approach based on binocular camera. The binocular vision system can deal with stereo imaging problem using disparity map. After the cameras are calibrated, the approach uses skin color model and depth information to separate the hand from the environment in the image. And the features of the gestures are extracted by feature extraction algorithm. These gestures as well as their features constitute a set of training examples in machine learning. The Support Vector Machine (SVM), which is supervised learning models, are used to classify these gestures that are labeled with their meaning, such as digits gesture. In training and classification processes, we use the same feature extraction algorithm handling the gesture image and SVM can recognize the meaning of a gesture. The gesture recognition method mentioned in this paper represents a high accuracy in recognizing number gestures.

The elderly health monitoring platform based on spark

With the explosion of sensor data, it is hard for traditional health monitoring platforms to process big data concurrently or analyze data online. This paper proposes a novel elderly health monitoring platform which introduces memory-based computation framework Spark to carry out the analysis of the data clustering. On the basis of parallelization of SMV detection algorithm, the proposed platform implements online analysis of real-time data stream using Spark Streaming. Experimental results show that with a large of number of users accessing, fall detection and clustering analysis can be achieved efficiently.

Gesture recognition based on Kinect

With the rapid development of computer science, gesture recognition has been a highlight of research in the area of Human Computer Interaction (HCI). Generally speaking, gesture recognition can be divided into two types: static gesture recognition and dynamic gesture recognition. Under the background of the aging population, how to use the method of gesture recognition to help the elderly adapt to “Intelligent Age” is a meaningful issue, which deserves more attention. This paper describes a gesture recognition method based on Kinect, a 3D somatosensory camera sensor. This method involves skeleton tracking, where the skeleton data is produced from the depth images obtained via Kinect. Extensive experiments demonstrate the superior performance of the proposed methods over Kinect.

Multi-feature gesture recognition based on Kinect

Human Computer Interaction (HCI) has been a popular research area during the last few years. Compared with the tradition HCI methods such as using a keyboard or mouse, people prefer to have their tasks done in a more natural way. As an essential form of non-verbal communication in daily life, gesture is a good choice to turn the ideas into reality. Although various recognition methods are proposed to solve the problem, these methods are time-tensed, space-tensed or miscellaneous. This paper introduced a new method to recognize the hand gesture correctly and efficiently. The recognition is done through two phases: the skeleton phase concerning capturing and processing skeleton feature of the hand gesture, and the hand phase focusing on extracting hand contour feature of the hand gesture. Experimental results confirm an overall 94% accuracy in recognizing and matching the pre-defined templates and robustness to backgrounds.

Automatic feature extraction and optimal path planning for robotic drawing

Automatic robotic drawing is a fantastic demo to show the combination of intelligence and robot technique. It requires automatic feature extraction, complex robot path planning and optimization, which can make many contributions in industrial manufacturing. In this paper, we propose a hybrid method by combining local binarization with global binarization to extract features. The extracted features which will be drawn by a robot refer to some cells with complex shapes. The path of drawing should be optimized because the drawing process is time consuming. Therefore, these cells need to be partitioned into multiple cells due to its complexity. The path of drawing each cell can be generated separately, and the trajectories of drawing all cells need to be connected to form a complete trajectory. Here we propose a new solution to optimize the connection while considering the acceleration and deceleration of the robot tool. All these algorithms have been implemented and attractive result is achieved by experiment. The proposed method can be used in many industrial applications such as painting and grinding.

Real-time structure-light-based 3D terrain sensing for mobile robot using CUDA

3D surface inspection has been widely used in the industry to detect all kinds of surface defects and to measure the overall quality of a produced piece. It gives us the idea of using this technique on mobile robots for terrain sensing. 3D surface inspection provides reliable and accurate measurements, but offline. So the challenge of using surface inspection on mobile robots is how to implement it for real-time measurement, for mobile robots need to get information from the environment online and make responses immediately. For this purpose, a real-time terrain sensing system for mobile robots based on structure light is proposed. To satisfy the requirement of online measurement, a one-shot pattern using monochromatic light is used in this system, and the parallel implementation of the required algorithms is made using CUDA to accelerate the processing. The one-shot pattern is based on the pattern primitive which is similar to the checkerboard corner, and it provides a high accuracy performance. The use Graphics Processing Units (GPUs) for data processing tasks facilitates the construction of cost-effective and real-time systems. Such a system has been developed in our laboratory, and we start to use it on a 6 wheel robot. Last, the experimental results demonstrate the performance of this system.