Robotic catching of thrown objects is one of the common robotic tasks, which is explored in a number of papers. This task includes subtask of tracking and forecasting the trajectory of the thrown object. Here we propose an algorithm for estimating future trajectory based on video signal from two cameras. Most of existing implementations use deterministic trajectory prediction and several are based on machine learning. We propose a combined forecasting algorithm where the deterministic motion model for each trajectory is generated via the genetic programming algorithm. Object trajectory is extracted from video sequence by the image processing algorithm, which include Canny edge detection, Random Sample Consensus circle recognition and stereo triangulation. After that rajectory is forecasted using proposed method. Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm is able to forecast the trajectory accurately.
Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.
The paper discusses the issue of creating an intelligent diagnostic system for welded joints based on the radiographic method. This will speed up the process of decoding radiographic images and reduce the number of errors associated with human factors, since at this time most of the work on decoding images is done manually. The goal of the work is to develop an intelligent system for finding defects in a welded joint in a radiographic image using neural networks. The obtained results are the algorithm of operation of the intelligent diagnostic system for welded joints based on the radiographic method, a trained neural network for detecting defects of welded joints. Data Science R R Akhmedyanov, K F Tagirova, A M Vulfin, V V Berkholts and R Ch Gayanov V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 464 2. Development of the structure of the system for detecting defects of welded joints based on image analysis Radiographic control (Radio-graphic method of NDT) is a method of radiation non-destructive testing (NTD), based on the transformation of the image of the monitored object into a radiographic image or recording of this image on a memory device and subsequent conversion to a light image [1, 18]. Radiographic control is carried out in order to identify surfacing and welded joints (seam and heataffected zone): cracks; lack of fusion; pores; metal and non-metallic inclusions, the density of which differs from the density of the welded joint metal (tungsten, slag, oxide, etc.); inaccessible for external inspection of undercuts, burn-throughs, etc. [2]. The process of radiographic inspection of a welded joint is shown in Figure 1. analysis of signals characterized by a high degree of uncertainty, e.g., "non-stochastic" type, which includes most biomedical signals, including EСS; increasing the level of intelligent assistance of medical specialists; revealing hidden regularities and extracting new knowledge from the accumulated data, which will allow to build production systems of explaining the diagnostic solutions. The process of radiographic inspection of a welded joint is shown in Figure 1.Data Science R R Akhmedyanov, K F Tagirova, A M Vulfin, V V Berkholts and R Ch Gayanov V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 465
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