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EN
Complex traffic control systems are equipped with a range of cameras for traffic surveillance, road traffic measurements. On many sites the different cameras cover the same observation areas but provide different quality streams to the system, usually compressed for surveillance and raw for vehicle detection. Elimination of duplicate cameras especially high quality devices is desired for enhancing the performance of systems. Vehicle detectors based on image processing are sensitive to the quality of input video streams. The paper presents results from tests of using lossy data compression for delivering video streams to vehicle detectors for traffic control. The limit of data loss is determined for assuring correct vehicle detection. The recommendations can be used for optimising traffic vision systems.
EN
Acquisition of image series using the digital camera gives a possibility to obtain high resolution/quality animation, much better than while using the digital camcorder. However, there are several problems to deal with when producing animation using such approach. Especially, if motion involves changes in observer position and spatial orientation, the resulting animation may turn out to look choppy and unsmooth. If there is no possibility to provide some hardware based stabilization of the camera during the motion, it is necessary to develop some image processing methods to obtain smooth animation. In this work we deal with the image sequence acquired without stabilization around an object. We propose a method that enables creation of smooth animation using the registration paradigm.
EN
Medical objective of this work is to compensate tumor's displacements due to free breathing, so as to reduce the irradiated zone and thus preserve healthy tissues. For this purpose, we are studying the feasibility of tracking the tumor's motion in portal images, i.e. in images generated by the treatment beam (high-energy X-rays). Two reference algorithms were tested on sequences of portal images of a phantom and of patients. Target tracking algorithm (block-matching), was able to correctly track the target only when the tumor was perceptible in the images. Otherwise it would require implanting of radioopaque markers in tumor's vicinity. Optical flow estimation was expected to deduce "invisible" tumor's displacements from the motion of neighboring tissues. However, the standard Horn & Schunck's algorithm gave poor results because of low contrast and absence of texture in the images. We are suggesting a modified version of this approach, based on intersections of motion constraint lines for a limited number of pixels carrying reliable and complementary motion information.
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