In this paper, we describe a novel technique for vision based UAV (unmanned aerial vehicle) navigation. In this technique, the navigation (position estimation) problem is formulated as a tracking problem and solved by a particle filter. The state and observation models of the particle filter are established based on a stereo analysis of the image sequence generated by the UAV's video camera in connection with a DEM (digital elevation map) of the area of the flight, which helps to control estimation error accumulation. The efficacy of this technique is demonstrated by simulation experimental results.
KEYWORDS: Tumors, Motion models, Data modeling, Medical imaging, Imaging informatics, Current controlled current source, Radiotherapy, Cancer, Oncology, Abdomen
Radiation therapy (RT) is an important procedure in the treatment of cancer in the thorax and abdomen. However, its efficacy can be severely limited by breathing induced tumor motion. Tumor motion causes uncertainty in the tumor's location and consequently limits the radiation dosage (for fear of damaging normal tissue). This paper describes a novel signal model for tumor motion tracking/prediction that can potentially improve RT results. Using CT and breathing sensor data, it provides a more accurate characterization of the breathing and tumor motion than previous work and is non-invasive. The efficacy of our model is demonstrated on patient data.
KEYWORDS: Cameras, Error analysis, 3D image processing, Motion estimation, Unmanned aerial vehicles, 3D image reconstruction, Geographic information systems, Video, Global Positioning System, 3D vision
In this paper, we describe a novel approach to vision based navigation. In this approach, an airplane's position at each sampling time is estimated through a two-step process. In the first step, the plane's 3D motion is estimated from the current and previous image frames to produce an initial estimate of the plane's position. In the second step, the error in the initial position estimate is corrected by using a test image generated from a digital elevation map of the flight area and the previous frame. Experimental results demonstrated the efficacy of this approach--the correction step reduces position estimation error and with it, the error does not increase with time.
KEYWORDS: Image segmentation, Image processing algorithms and systems, Surveillance, Digital filtering, Video compression, Video surveillance, Motion estimation, Cameras, Video, Light sources and illumination
In this paper, we describe a novel approach to image sequence segmentation. In this approach, the presence of moving objects is first detected through background subtraction, i.e., the difference between the current frame and a dynamically updated background. Then, moving objects are extracted from the background subtraction image. Experimental results on surveillance image sequences demonstrated the efficacy of the proposed approach and its improvements over previous background subtraction techniques.
In this paper, we describe a novel approach to image sequence segmentation and its real-time implementation. This approach uses the 3D structure tensor to produce a more robust frame difference signal and uses curve evolution to extract whole objects. Our algorithm is implemented on a standard PC running the Windows operating system with video capture from a USB camera that is a standard Windows video capture device. Using the Windows standard video I/O functionalities, our segmentation software is highly portable and easy to maintain and upgrade. In its current implementation on a Pentium 400, the system can perform segmentation at 5 frames/sec with a frame resolution of 160 by 120.
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