Jianyi Kong

An Interactive Image Segmentation Method in Hand Gesture Recognition

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

Simultaneous Calibration: A Joint Optimization Approach for Multiple Kinect and External Cameras

Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are targeted at joint calibration of multi-sensors (more than four devices), which normally is a practical issue in the real-time systems. In this paper, we propose a novel method and a corresponding workflow framework to simultaneously calibrate relative poses of a Kinect and three external cameras. By optimizing the final cost function and adding corresponding weights to the external cameras in different locations, an effective joint calibration of multiple devices is constructed. Furthermore, the method is tested in a practical platform, and experiment results show that the proposed joint calibration method can achieve a satisfactory performance in a project real-time system and its accuracy is higher than the manufacturer’s calibration.

Gesture recognition based on an improved local sparse representation classification algorithm

The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized

Fusion hand gesture segmentation and extraction based on CMOS sensor and 3D sensor

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Hand gesture recognition based on convolution neural network

l1 norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the

D-S evidential theory on sEMG signal recognition

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