The article proposes an approach to improve the visual quality of generated images based on the use of primary data processing methods. To reduce the effect of noise associated with the appearance of dust or water droplets, a multi-criteria filtering method is proposed for use. The proposed method allows two-pass processing. At the first stage, the method uses a criterion that forming strong noise suppression with blurring of the boundaries. Based on the generated data, we perform filtering while preserving and strengthening the boundaries of objects. Next, we perform the formation of a combined approach to fusion of image. In the case of low illumination, the noise component is strong and its suppression is an important problem. In the case of fog, the task is complicated, since the boundaries of objects are blurred. An adaptive non-linear alpha-contrasting algorithm is used for elimination and compensation. Pairs of test images obtained by sensors with a resolution of 6000x4000 (8 bit, color image, visible range) are used as test data used to evaluate the effectiveness, pairs of images with the same illumination but different fog density are used for the test. Images of simple shapes are used as analyzed objects.
The article discusses the implementation of a hardware neuroaccelerator based on FPGAs of the Cyclone IV series with 115,000 logical elements. An assessment of the requirements for the hardware resources of the computing platform is given. The features of the implementation of the neural network in the tasks of cognitive robotics and industrial production have been investigated in order to improve safety in the interaction of a robot and a person.
The article describes forming the device's software and hardware components for automated analysis of the shape of objects located on the cutting table in industrial robotic systems. A video camera with a resolution of 800x600 pixels is used as a data recording device. When performing an analysis object on the cutting table, a single image is formed. The image stitching process uses algorithms: preprocessing, simplification, and highlighting of key features. The converted data is combined into a single data field that is analyzed at multiple levels. The analysis is carried out based on the accumulated data and the basis of the full image with the subsequent transition to small (original) parts. The second step is to transform the image. On the resulting image highlights the border of the shape of the object, which is located on the cutting table. At the final stage, the shape is analyzed and its boundaries are clarified. Further, a cloud of data is formed, transmitted to the computing PC unit of the cutting table in the form of a complex contour of the object. The application of this approach makes it possible to automate the process of binding the zero point, minimize the number of unused residues, and form a field contour into which an object can be placed for its cutting. As test data, we used the results of applying the blocks of the proposed approach to analyze an object's shape located on the table's working table for waterjet cutting. The working field of the machine is 1.5x1.5 m. The camera resolution was 800x600 pixels in RGB format.
Automatic 3-D recovery from multimodal images can be extremely useful for information extraction for the robot navigation application. In most cases, such a scene contains missing holes on depth maps that appear during the synthesis from multi-views. This paper presents an automated pipeline for processing multimodal images to 3-D digital surface models. The proposed approach uses the modified exemplar-based technique in quaternion space. We also perform depth completion by fusing data from multiple recorded multimodal images affected by occlusions. We propose an algorithm using the concepts of a sparse representation of quaternions, which uses a new gradient to calculate the priority function by integrating the structure of quaternions with local polynomial approximation - the intersection of confidence intervals). Moreover, the color information incorporates into the optimization criteria to obtain sharp inpainting results. Compared with state-of-the-art techniques, the proposed algorithm provides plausible restoration of the depth map from multimodal images, making them a promising tool for an autonomous robot navigation application.
KEYWORDS: Binary data, Video surveillance, Video, Detection and tracking algorithms, 3D image processing, Stochastic processes, Robot vision, Medical research, Medical equipment, Machine vision
This paper describes an action recognition method based on the 3D local binary dense micro-block difference. The proposed algorithm is a three-stage procedure: (a) image preprocessing using a 3D Gabor filter, (b) a descriptor calculation using 3D local binary dense micro-block difference with skeleton points, and (c) SVM classification. The proposed algorithm is based on capturing 3D sub-volumes located inside a video sequence patch and calculating the difference in intensities between these sub-volumes. For intensifies motion used the convolution with a bank of 3D arbitrarily-oriented Gabor filters. We calculate the local features for pre-processed frames, such as 3D local binary dense micro-block difference (3D LBDMD). We evaluate the proposed approach on the UCF101 database. Experimental results demonstrate the effectiveness of the proposed approach on video with a stochastic textures background with comparisons of the state-of-the-art methods.
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