Abstract-The paper presents a proposal of sensor fusion combining data derived from a Kinect device and highprecision sensors. The main idea was to enhance the tracking of a human arm in order to obtain precise coordinates. The Kinect plays the role of a calibration device and sensors' data are used in kinematics equations for enhanced tracking of the arm. This way the resulting information has less uncertainty and can be directly transmitted to another system, e.g. robotics one. The system has been implemented using kinematics-based approach with transformation operators as well as quaternions. In terms of accuracy the main result is that the Kinect performs very similar with sensors.
The paper presents a solution to the problem of movement tracking in images acquired from video cameras monitoring outside terrain. The solution is resistant to such adverse factors as: leaves fluttering, grass waving, smoke or fog, movement of clouds etc. The presented solution is based on well known image processing methods, nevertheless the key was the use of an appropriate conduct procedure. In order to obtain a real-time system the CUDA technology was involved.
The paper presents a method of irregular patterns creation and matching in an example computer vision system designed for unknown scale objects recognition. The matching process is based on the Hough transform for irregular objects with a parameter space defined by translation, rotation and scaling operations. The high efficiency of the technique allows for poor quality or highly complex images to be analysed. The technique may be used in robot vision systems with unknown scale of the scene, identification systems or for image analysis, directly on grey-level images. In order to guarantee the performance the process of patterns matching is supported by massively parallel processing with the CUDA technology.
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