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1 Chapter 1Figure 1.1.1 “Grey on orange”, “Flower”, “Drawing drawing” [Pepperell, 2019].Figure 1.1.2 Example of three groups of stimuli tested in study (Original, colum...Figure 1.1.3 Examples of interactive realization of basic test models: (a) Wundt...Figure 1.1.4 Structure of the compound eye of a fly.Figure 1.1.5 Vertical section of the human eye.Figure 1.1.6 “Cage without bird”, the scheme for the detection of “blind spots”.Figure 1.1.7 Projection zones of the left hemisphere of the brain.Figure 1.2.1 Human oculomotor muscles.Figure 1.2.2 Determination of the sharpness of the resolution of the eye.Figure 1.2.3 Snellen table and Landolt rings.Figure 1.2.4 The stylized Landolt rings without the noise and with different noi...Figure 1.2.5 Comparison of methods for determination of regions of interest and ...Figure 1.2.6 Examples of oculograms of different test subjects who perceive the ...Figure 1.2.7 Transition row of face images from which the goal stimulus pairs ar...Figure 1.2.8 Schemes of eye movement when viewing various images [Yarbus, 1967].Figure 1.2.9 A reproduction of the painting “Didn’t Wait” by Russian artist I. R...Figure 1.2.10 Text without spaces between words.Figure 1.2.11 F-Pattern navigation.Figure 1.2.12 Comparison of certainty value of web page and saccadic estimation,...Figure 1.2.13 Tilt action.Figure 1.2.14 Curvature after-effect.Figure 1.2.15 Brightness assimilation [Ghosh, Bhaumik, 2010].Figure 1.2.18 Mach bands.Figure 1.2.17 Spatial frequency.Figure 1.2.18 Hermann’s grid.Figure 1.2.19 “Complementary” grid of Hermann.Figure 1.2.20 Light contrast.Figure 1.2.21 Texture pattern of various letters.Figure 1.2.22 Test images for determining color blindness.Figure 1.2.23 Achromatic pattern that causes subjective color sensations.Figure 1.2.24 Illustration based on dark shades of red and green and color illus...Figure 1.3.1 The combination of “figure-background”.Figure 1.3.2 Dual perception of background – figure combination.Figure 1.3.3 Chaotic pattern.Figure 1.3.4 Gestalt perception of the figure.Figure 1.3.5 Pattern based on clear patterns.Figure 1.3.6 Proximity factor grouping.Figure 1.3.7 Similarity factor grouping.Figure 1.3.8 Grouping on the principle of the same type of communication.Figure 1.3.9 Grouping factor “good continuation”.Figure 1.3.10 “Wedge” migratory birds, the factor of “common destiny”.Figure 1.3.11 Grouping by symmetry factor.Figure 1.3.12 Grouping factor closure.Figure 1.3.13 “Cats and dogs”, the factors of “good continuation” and closure.Figure 1.3.14 Triangle and circle configuration.Figure 1.3.15 Subjective contours.Figure 1.3.16 Necker’s subjective cube.Figure 1.3.17 Strengthening the subjective contour overlapping elements.Figure 1.3.18 The weakening of the subjective central rectangular contour.Figure 1.3.19 Three-dimensional subjective contours.Figure 1.3.20 Perception of forms depending on orientation.Figure 1.3.21 Perception of ambiguous forms.Figure 1.3.22 Recognition of the profile on the background and as a contour.Figure 1.3.23 The task for the Stroop effect.Figure 1.4.1 Flat and three-dimensional interposition of one scene.Figure 1.4.2 Linear perspective.Figure 1.5.1 Sequenced frames of illusion of ‘Hollow mask’ [Website youtube.com,...Figure 1.5.2 Constancy perception of size.Figure 1.5.3 Illusion of the Moon.Figure 1.5.4 The illusion of Muller-Lyer.Figure 1.5.5 Illusion Ponzo.Figure 1.5.6 Illusion of Poggendorf and Zolner.Figure 1.5.7 Horizontal Vertical Wundt Illusion.Figure 1.5.8 Illusion of Ebbingauz and Yastrov’s.Figure 1.5.9 Influence of context on apparent length, Baldwin’s illusion.Figure 1.5.10 Illusions based on contrast tilt.Figure 1.5.11 Fraser’s Illusion.Figure 1.5.12 Illusion of Münsterberg.Figure 1.5.13 Zander parallelogram, AB and AC are equal.

2 Chapter 2Figure 2.1 Ancient canons: (a) Acropolis. (b) Propylaea of the Acropolis of Athe...Figure 2.2 The proportions of the “Golden section” are the ultimate expression o...Figure 2.3 Benozzo Gozzoli “Procession of the Magi”. A fragment of the painting ...Figure 2.4 Dance movements are recorded in the scheme (the beginning of the twen...Figure 2.5 Surreal modern painting Ettore Aldo Del Vigo, compositional analysis.Figure 2.6 V. I. Barvenko “Alexander and Bucephalus”, hardboard, mixed technique...Figure 2.7 Fragments of the work by V. I. Barvenko “Alexander and Bucephalus”, c...Figure 2.8 Fragments of the work by V. I. Barvenko “Alexander and Bucephalus”, o...Figure 2.9 Analysis and determination of probable forms.Figure 2.10 Fragments of the work by V. I. Barvenko “Alexander and Bucephalus”, ...Figure 2.11 Michelangelo Buanarotti. Architectural design of the tombstone of Po...Figure 2.12 Analysis of architectural public structures. The Colosseum and the F...Figure 2.13 Analysis of landscape scenes of the painting in landscape painting w...Figure 2.14 Compositional schemes-landscape models [landscape website, 2020].Figure 2.15 Piazza del Poppolo. Rome. Italy. Reprint from an antique French orig...Figure 2.16 Analysis of landscape scenes from a reprint of an old French engravi...Figure 2.17 Gem from Giza, II-III centuries.Figure 2.18 (a) a drawing from the London Oriental Manuscript, circa 600, (b) th...Figure 2.19 E. Greco’s “Holy Family”. Compositional analysis.Figure 2.20 “Do Not Weep for Me, Mother” (Mary at the tomb of the Christ. Symbol...Figure 2.21 Yellow-Red-Blue - Wassily Kandinsky. 1925. Oil on canvas 127x200 cm ...Figure 2.22 Associative works of M. Ciurlionis 1903 [ciurlionis website, 2020].

3 Chapter 3Figure 3.1 Scheme of imaginative thinking.Figure 3.2 Types of imaginative thinking.Figure 3.3 The hierarchy of sensory images.Figure 3.4 Design image based on the symbols.Figure 3.5 Real images are converted to decorative symbols.Figure 3.6 The agglutination method in creation and imagination.Figure 3.7 The image elements of the makeup for Beijing Opera characters.Figure 3.8 The image in the project activities structure.Figure 3.9 An electric tower is a creative source for the image of brand-new cos...Figure 3.10 Stages of creating a project image.Figure 3.11 Stages of creating for costume design.Figure 3.12 The world-famous images of Charlie Chaplin, Marilyn Monroe and Salva...

4 Chapter 4Figure 4.1.1 Option 1. Welcome hugs.Figure 4.1.2 Option 2. Multi-level spiral.Figure 4.1.3 Option 3. Unification.Figure 4.1.4 Option 4. Continents.Figure 4.1.5 Option 5. Combinatorics.Figure 4.1.6 Option 6. Length.Figure 4.2.1 The Concept of “Rebirth”.Figure 4.2.2 The Concept of “Variability”.Figure 4.2.3 The Concept of “Riddle”.Figure 4.2.4 The concept of “Infinity of development”.

5 Chapter 5Figure 5.1 Illustration of representations in feature space learned by (a) exist...Figure 5.2 Illustration of the expected separations of the observation x, which ...Figure 5.3 Framework of our facial expression recognition model used for trainin...Figure 5.4 Failed case of (a) triplet loss, (b) (N+1)-tuplet loss, and (c) Coupl...Figure 5.5 Failed case of (a) triplet loss, (b) (N+1)-tuplet loss, and (c) Coupl...Figure 5.6 The training loss of different methods on SFEW validation set. The va...Figure 5.7 t-SNE visualization of images in Extended YaleB. The original images ...Figure 5.8 Using MI-FLF to swap some face attributes, while keeping their ID and...

6 Chapter 6Figure 6.1 Selection of necessary information on images.Figure 6.2 Creation of objects with information on images.Figure 6.3 Display of images in the form of tiles (ImageStrip).Figure 6.4 Display form of user interface.Figure 6.5 Database diagram.Figure 6.6 Main steps of text determination algorithm.Figure 6.7 Main steps of the Russian text recognition algorithm.Figure 6.8 Work of algorithm recognizing the English text.Figure 6.9 Cloud of Russian words specifying the objects of non-political class.Figure 6.10 Cloud of Russian words specifying the objects of political class.Figure 6.11 Cloud of English words specifying the objects of non-political class...Figure 6.12 Cloud of English words specifying the objects of political class.Figure 6.13 Frequency diagram for cities with the political activity.

7 Chapter 7Figure 7.1 Well-structured scene images, designed for the image understanding le...Figure 7.2 Simplest weakly-structured (camouflaged) scene images.Figure 7.3 Examples of raw scene images.Figure 7.4 The different kinds of low-resolution digital image displaying [Buten...Figure 7.5 The different types of interior/exterior topology objects: (a) – 8/8 ...Figure 7.6 Vague regions and their regularization example.Figure 7.7 The example of histogram analysis: original image (a) and the primary...Figure 7.8 The example of digital image encapsulation (gray-level image object d...Figure 7.9 3-D view of encapsulated image granules.Figure 7.10 Example of a spatial encapsulation by the ⁺G₂ Cartesian granule.Figure 7.11 Subsets of encapsulated Cartesian granules on the plane.Figure 7.12 The common positions of two granules diagram.Figure 7.13 (a) the result of the Algorithm 1 execution over the solid points cl...Figure 7.14 Three-level structure of GDMS on the basis of relational DBMS.Figure 7.15 Methodology of granulation of the image by L. Zadeh.Figure 7.16 Optimal structure of the granulated GDMS.Figure 7.17 The successive stages (1)-(6) of Automated target Detection for the ...Figure 7.18 The source character images for the classification.Figure 7.19 The examples of granulated character images.Figure 7.20 The fuzzy relation matrix graphic representations for the granulated...Figure 7.21 Corrupted character images examples.Figure 7.22 Different color space models in Cylindrical and Conical coordinates:...Figure 7.23 Google Earth image of seashore near Barcelona (a) and its RGB color ...Figure 7.24 Different projections of the granulated seashore image color histogr...

8 Chapter 8Figure 8.2.1 Geometry of 3-D ISAR scenario.Figure 8.2.2 Two 2-D coordinate systems built in the ISAR signal plane depicted ...Figure 8.2.3 The coordinate systems O’XY and built in stationary 2-D ISAR imag...Figure 8.2.4 2-D ISAR geometry.Figure 8.3.1 Traveling of the object trough range bins in case rectilinear movem...Figure 8.3.2 2-D geometrical model of aircraft MiG-35 in 64x64 pixels grid.Figure 8.3.3 LFM ISAR signal: real part (a), and imaginary part (b).Figure 8.3.4 LFM ISAR signal after correlation range compression: real (a), and ...Figure 8.3.5 3-D isometric projection of the object image after FFT azimuth comp...Figure 8.3.6 LFM SAR signal: real part (a), and imaginary part (b).Figure 8.3.7 LFM ISAR signal after correlation range compression: real part (a),...Figure 8.3.8 Aircraft Mig-35 final image in 3-D isometric projection (a), and in...Figure 8.3.9 Unwrapped phase history Ф₀(p) and extrapolated curve Ф(p) (a), (b –...Figure 8.3.10 Phase difference ΔФ(p).Figure 8.3.11 First cost function C₁ = M(A) (a), and second cost function C2(A) ...Figure 8.3.12 Aircraft Mig-35 – final image, obtained after autofocusing procedu...Figure 8.3.13 ISAR signal with Barker’s phase code modulation in 3-D isometric p...Figure 8.3.14 Barker’s PCM ISAR signal (isometric projection) after correlation ...Figure 8.3.15 ISAR image (isometric projection) after FFT azimuth compression (a...Figure 8.3.16 Family of phase correction functions (a) Ф(p), and three cases of ...Figure 8.3.17 3-D view of the surface, described by the image cost function C₁(p...Figure 8.3.18 Aircraft F-18 image: p₀ = 250, p = 0.01 (a), p = 0.02 (b).Figure 8.3.19 Aircraft F-18 image: p₀ = 250, p = 0.05- unfocused (a), p = 0.06-f...Figure 8.4.1 3-D geometry of the aircraft F18..Figure 8.4.2 Real and imaginary parts of the ISAR signal.Figure 8.4.3 Real and imaginary parts of the ISAR signal after demodulation.Figure 8.4.4 Isometric 3-D ISAR image.Figure 8.4.5 Final image (a) and power normalized image (b).Figure 8.4.6 Autofocusing by p = q = 58 and r = 30.Figure 8.4.7 Autofocusing by p = q = 58 and r = 46.Figure 8.4.8 Autofocusing by p =q = 58 and r = 60.Figure 8.4.9 A surface of the entropy function using as a cost function in the a...Figure 8.4.10 ISAR signal formation with complementary phase code modulation (fi...Figure 8.4.11 Real (a) and imaginary (b) component of the ISAR signal modulated ...Figure 8.4.12 Real (a) and imaginary (b) component of the ISAR signal, modulated...Figure 8.4.13 Real (a) and imaginary (b) part of the ISAR signal, modulated by f...Figure 8.4.14 Real (a) and imaginary (b) part of the ISAR signal, modulated by s...Figure 8.4.15 The image of the aircraft MiG-35, obtained from ISAR signal, modul...Figure 8.4.16 Aircraft MiG-35 image (256 levels of grey color scale) from CPCM I...Figure 8.4.17 Discrete structure of the object depicted in the space of the regu...Figure 8.4.18 Reconstructed ISAR image: by step p = 1 (a), by step p = 2 (b), by...

9 Chapter 9Figure 9.1 Multidimensional ESF of multispectral image.Figure 9.2 Probability spread function of multispectral image.Figure 9.3 Probability density distribution of a multispectral image.Figure 9.4 The equivalent spatial resolution determining by equivalent PSF of th...Figure 9.5 Determining the spectral reflectance value of x band using the identi...Figure 9.6 Multispectral imagery resolution enhancement dataflow diagram.Figure 9.7 ASTER multispectral satellite image (Odessa, Ukraine, 24.09.2007, 9 s...Figure 9.8 The results of the satellite imagery band downsampling by a factor of...Figure 9.9 Schematic representation of the analyzed subpixels on pixel grids of ...Figure 9.10 Basic types of subpixels relative location.Figure 9.11 The series-parallel system examples.Figure 9.12 Selection of subpixel reallocating rule.Figure 9.13 Sentinel 2 MSI fragment with false color combination of band 5 (705 ...Figure 9.14 Thermal infrared atmospheric windows (3-5 μm – mid-wave infrared ran...Figure 9.15 Raw satellite images of central part of Kyiv city (Ukraine): 10 m Se...Figure 9.16 NDVI-emissivity relationship approximation [Zaitseva et al., 2019].Figure 9.17 Visual comparison between the resolution of raw longwave-infrared da...Figure 9.18 Spatial resolution enhancement flowchart.Figure 9.19 LST image in the spatial domain (a) and its Fourier amplitude spectr...Figure 9.20 Extraction of low- and high-frequency components of LST data.Figure 9.21 General flowchart of frequency component merging for LST spatial res...Figure 9.22 The visual difference between low-resolution LST images (a), and enh...

10 Chapter 10Figure 10.1 Examples of images acquired by means of aerospace methods. Sources: ...Figure 10.2 Example of image obtained by means of push broom method before and a...Figure 10.3 Image before and after orthotransformation.Figure 10.4 Example of incorrectly exposed image. Source image – on the left, an...Figure 10.5 Example of the specific distortions in case of channels misalignment...Figure 10.6 Distortion peculiar to the moving object. Synthesized image.Figure 10.7 Specific bar diagram of image obtained by means of aerospace methods...Figure 10.8 Example of aerospace imagery of MKAD multilevel junction – Leningrad...Figure 10.9 Example of complex structure buildings. Khodynka Field, Moscow.Figure 10.10 Histogram of image before (from the left) and after (from the right...Figure 10.11 Example of fragments placing by means of simple algorithm (checker ...Figure 10.12 Example of fragments placing with the use of offered algorithm.Figure 10.13 Functional diagram of generation of training and validation dataset...Figure 10.14 Functional diagram of significance map generation subprocedure.Figure 10.15 Process of extraction and augmentation of the separate sample from ...Figure 10.16 Reference designations used by the authors on ANN topology diagrams...Figure 10.17 Basic ANN topology offered by the authors: generalization of U-Net,...Figure 10.18 Inception-G base block, i.e., modification of base block of Incepti...Figure 10.19 Functional diagram of Squeeze-and-Excitation adding to any base blo...Figure 10.20 Functional diagram of topology built according to the stacked hourg...Figure 10.21 Functional diagram of supplemented encoder context method.Figure 10.22 Examples of geometric structures (above) and “energy” fields for th...Figure 10.23 Dynamics of training process carried out according to forking learn...