DSpace Komunita:http://hdl.handle.net/11025/4002021-01-20T01:45:01Z2021-01-20T01:45:01ZСompression for texture images in different basis functions using system criteria analysisBabikov, A.Yu.Voronin, V. V.Ryzhov, V. P.Ryzhov, Yu.V.http://hdl.handle.net/11025/346412020-07-02T08:37:02Z2018-01-01T00:00:00ZNázev: Сompression for texture images in different basis functions using system criteria analysis
Autoři: Babikov, A.Yu.; Voronin, V. V.; Ryzhov, V. P.; Ryzhov, Yu.V.
Editoři: Skala, Václav
Abstrakt: This paper considers image compression for texture images. For texture representation we consider the
orthogonal decomposition of two-dimensional signals (images) using spectral transform in the different basis
functions. This paper focuses on the analysis of the following basis DCT, FFT, Haar, Hartley, and Walsh using
system criteria analysis. The error of the orthogonal representation of images and the computational cost are
considered when choosing a basis system, which is based on the Bellman-Zadeh concept using fuzzy sets. It is
shown that the Haar transform can represent textural images more efficiently with smaller average risk than
other basis functions.2018-01-01T00:00:00ZBabikov, A.Yu.Voronin, V. V.Ryzhov, V. P.Ryzhov, Yu.V.The solution of the problem of simplifying the images for the subsequent minimization of the image bit depthSemenishchev, EvgeniiVoronin, ViacheslavShraifel, Igorhttp://hdl.handle.net/11025/346402020-07-02T08:37:02Z2018-01-01T00:00:00ZNázev: The solution of the problem of simplifying the images for the subsequent minimization of the image bit depth
Autoři: Semenishchev, Evgenii; Voronin, Viacheslav; Shraifel, Igor
Editoři: Skala, Václav
Abstrakt: In this paper, the approach of changing bit depth of images is considered. This type of operation is required when
performing primary processing operations, identifying parameters and stitching images. The process of changing
bits depth of images is performed in three stages. At each stage, the error minimization criterion is tested Result
of applying the approach allows obtaining numerical region characteristics including the number of clusters, the
number of minimum and maximum cluster sizes. To perform the process of minimizing some of the criteria, it is
necessary to divide the image into areas. The paper presents a mathematical description of the approach, as well
as flowcharts for performing operations of data processing steps. The article gives recommendations for choosing
coefficients to obtain optimal minimizing parameters. The test images give an example of performing bit
changes on image areas.2018-01-01T00:00:00ZSemenishchev, EvgeniiVoronin, ViacheslavShraifel, IgorUsing a spatiotemporal plane to recover a moving object’s shape using Spatiotemporal Boundary FormationCunningham, Douglas W.http://hdl.handle.net/11025/346392020-07-02T08:37:03Z2018-01-01T00:00:00ZNázev: Using a spatiotemporal plane to recover a moving object’s shape using Spatiotemporal Boundary Formation
Autoři: Cunningham, Douglas W.
Editoři: Skala, Václav
Abstrakt: When ever an object moves, it successively covers and uncovers surfaces that are farther away. This occlusion
and dis-occlusion always occurs precisely at the boundaries of the moving object and as such provide information
not only about the shape of the object but also about its velocity, transparency, and relative depth. Humans can
and do use this information, and the process has come to be called Spatiotemporal Boundary Formation (SBF).
Previous authors have used the wealth of experimental investigations into SBF to create a mathematical model
of the process. In this article we proposed a novel method to recover the orientation and velocity the local edge
segments of the moving objects which is more flexible, more robust, more compact, and allows the recovery of
edges that do not have a constant velocity. The method can be used in object segmentation algorithms or as a
pre-filter for machine-learning-based recognition algorithms in order to improve the overall result.2018-01-01T00:00:00ZCunningham, Douglas W.The fast semi-bounded kernel-diffeomorphism estimatorBen Othman, IbtissemTroudi, MolkaGhorbel, Faouzihttp://hdl.handle.net/11025/346382020-07-02T08:37:03Z2018-01-01T00:00:00ZNázev: The fast semi-bounded kernel-diffeomorphism estimator
Autoři: Ben Othman, Ibtissem; Troudi, Molka; Ghorbel, Faouzi
Editoři: Skala, Václav
Abstrakt: We introduce, by this work, a fast method to estimate probability density functions in the semi-bounded case. This
new technique is a simplified version of the kernel-diffeomorphism estimator which requires complexity in the
calculations. It is based on a logarithmic transformation of the data which will be estimated by the conventional
kernel estimator. Thus, the algorithm complexity is reduced from O(N2) to O(N).2018-01-01T00:00:00ZBen Othman, IbtissemTroudi, MolkaGhorbel, Faouzi