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This paper proposes a novel framework to segment hand gestures in RGB-depth (RGB-D) images captured by Kinect using humanlike approaches for human–robot interaction. The goal is to reduce the error of Kinect sensing and, consequently, to improve the precision of hand gesture segmentation for robot NAO. The proposed framework consists of two main novel approaches. First, the depth map and RGB image...
In this paper, we present an effective and accurate gaze estimation method based on two-eye model of a subject with the tolerance of free head movement from a Kinect sensor. To accurately and efficiently determine the point of gaze, i) we employ two-eye model to improve the estimation accuracy; ii) we propose an improved convolution-based means of gradients method to localize the iris center in 3D...
This paper proposes a novel method with a modified Expectation-Maximisation (EM) Algorithm to segment hand gestures in the RGB-D data captured by Kinect. With the depth map and RGB image aligned by the genetic algorithm to estimate the key points from both depth and RGB images, a novel approach is proposed to refine the edge of the tracked hand gesture, which is used to segment the RGB image of the...
This paper introduces a novel method for the depth map and RGB image alignment for Kinect. Genetic algorithm is used to estimate the key points from both depth and RGB images, and this method is robust to the uncertainties of the extracted point numbers. The experimental results demonstrated that the proposed method is capable of precisely aligning the depth and RGB images, contributes a great improvement...
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