Images Of Different Nature Research Articles

Stable descriptors in image recognition tasks

Objective. The objective of the study is to determine various stable characteristics of images (semi-invariants and invariants) as descriptors necessary for the formation of a feature space of standards intended for recognizing images of different nature belonging to different classes of objects. Methods. The authors propose metrics for evaluating the proximity of the recognized image to a given standard in the space of covariance matrices, based on the obtained descriptors as a methodological basis for constructing image recognition methods. Results. The content of the main stages of selecting descriptors for a given class of objects is developed, taking into account the different illumination of the recognized images. The effectiveness of the results obtained is confirmed by experimental studies related to the solution of the problem of recognition of special images - facies. Conclusions. The definition of stable image descriptors as invariants or semi-invariants to zoom and brightness transformations allows solving the problems of facies classification in conditions of the unstable shooting of recognized images. The images can be rotated and shifted in any way. In general, the proposed approach allows developing an effective image recognition system in the presence of various types of interference on the recognized images.

FORMATION OF WAVELET - TRANSFORM SIGNALS TAKING INTO ACCOUNT THRESHOLD FUNCTIONS IN THE DIGITAL STREAM COMPRESSION PROBLEM

In order to reduce the digital stream, the problem of improving the quality of digital filtering based on Discrete Wavelet Transformation (DWT) was analyzed using different options for setting the threshold function and the choice of wavelet basis. Given the considerable interest in this problem and the numerous studies concerning the search for ways to optimize the suppression of interference present in signals and images, the topic continues to be relevant and important. For such tasks as image compression, operations in processing and synthesis of different signals, in the analysis of images of different nature, in reducing (compressing) large amounts of information, and to protect information is often used wavelet transform, implemented on the basis of mirror filters. For the formation of wavelet-transform signals taking into account the threshold functions in the problem of digital stream compression, a standard approach to solving the problem of signal purification from interference and random distortions using Daubechies wavelet and adjusting the signal decomposition coefficients based on wavelet functions using soft and rigid threshold task options. It is shown that the use of complex bases provides an advantage both in terms of threshold filtering error and in terms of reducing the risk of accidental distortion in the reconstruction of the useful signal by wavelet coefficients. Appropriate steps were taken for the test signal and experimental data. When using the threshold function, the large modulus (most significant) wavelet coefficients remain unchanged, and the small ones are reset to zero. The change in the amplitude of the recovered signal leads in the latter case to a decrease in the absolute values of all wavelet coefficients, including large modulo. For those applications where it is important to keep the amplitude characteristics constant, this approach is not suitable, but there are problems where it is more important to maintain the regularity of the signal than to accurately reproduce its amplitude. Rhis is the filtering of images from various obstacles, where the method of "soft" task of the threshold function is a widely used approach. When analyzing signals, the constant amplitude is also not always a mandatory requirement. An audio signal can be amplified after filtering, and pre-cleaning it from interference is more important than changing the amplitude characteristics. The use of complex wavelet transform allowed to obtain error reduction and lower threshold level when adjusting wavelet coefficients, and can be recommended for analysis of complex wavelet transform methods as an effective tool for cleaning signals and images of different nature for further research.

ДИЗАЙНЕРСКИЕ КОМПОЗИЦИИ ДЛЯ КОВРОВ И ГОБЕЛЕНОВ НА ОСНОВЕ ПРЕОБРАЗОВАНИЯ МНОГОГРАННИКОВ

The article considers the development of the author’s original compositions for carpets, tapestries, decorative panels, etc. based on synthesized multifaceted geometric structures. Combination of planar and dimensional figures represented by an orthographic diagram leads to the appearance of unexpected abstract (sometimes figurative) images of different nature, which can be used in the sphere of design and fine arts. While choosing images, preference is given to compositions possessing harmonious artistic and aesthetic features.

Generalized Fractional Filter-Based Algorithm for Image Denoising

This paper presents a new algorithm for image denoising using a fractional integral mask of the K-operator. K-operator is the generalized fractional operator, and it reduces to Riemann–Liouville and Caputo fractional derivatives in a special case. The proposed algorithm is applied to digital images of different nature to demonstrate the performance of image denoising. Experimental results are compared with other existing filters together with block matching and 3-D filtering, and weighted nuclear norm minimization-based approaches. The obtained experimental results show that the proposed algorithm is computationally efficient and its average performance is comparatively better than other discussed methods.

Image Super-Resolution via Block Extraction and Sparse Representation

Super-Resolution (SR) has many applications in several issues of the image processing by obtaining High-Resolution (HR) images from Low-Resolution (LR) images. In this paper, a SR technique that can increase the resolution in images of different nature is proposed. Our approach in obtaining SR image, first uses Lanczos interpolation of initial LR image, then edge features are extracted via convolution of an image with two different filters; following, the most informative features are performed employing principal component analysis (PCA). In next step, preprocessed image presented in blocks is used, where for an each block its sparse representation is performed using LR dictionary and another HR dictionary. In final step, the SR blocks are reconstructed resulting in improved SR image. Experimental results demonstrate the effectiveness of our method in comparison to state-of-the art techniques in terms of objective criteria PSNR, MAE and SSIM values as well as in subjective visual performance. Additionally, the proposed technique significantly reduces computational time in SR reconstruction.

Assessing the contribution of color in visual attention

Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene, on which higher level vision tasks, such as object recognition, can focus. The saliency-based model of visual attention represents one of the main attempts to simulate this visual mechanism on computers. Though biologically inspired, this model has only been partially assessed in comparison with human behavior. Our methodology consists in comparing the computational saliency map with human eye movement patterns. This paper presents an in-depth analysis of the model by assessing the contribution of different cues to visual attention. It reports the results of a quantitative comparison of human visual attention derived from fixation patterns with visual attention as modeled by different versions of the computer model. More specifically, a one-cue gray-level model is compared to a two-cues color model. The experiments conducted with over 40 images of different nature and involving 20 human subjects assess the quantitative contribution of chromatic features in visual attention.

Extension of phase correlation to subpixel registration

In this paper, we have derived analytic expressions for the phase correlation of downsampled images. We have shown that for downsampled images the signal power in the phase correlation is not concentrated in a single peak, but rather in several coherent peaks mostly adjacent to each other. These coherent peaks correspond to the polyphase transform of a filtered unit impulse centered at the point of registration. The analytic results provide a closed-form solution to subpixel translation estimation, and are used for detailed error analysis. Excellent results have been obtained for subpixel translation estimation of images of different nature and across different spectral bands.

Segment-based registration technique for visual-infrared images

A new general registration method for images of different na- ture is presented. As gray levels or textures cannot be used for the registration of images from separate spectral bands, an edge-based method has been developed. Edge images are processed to extract straight linear segments, which are then grouped to form triangles. A set of candidate transformations is determined by matching triangles from the source and destination images. The transformations are then evalu- ated by matching the transformed set of source segments to the set of destination segments. As the coincidence of vertices or edge overlap- ping cannot be assumed in the registration of images of different nature, a new function for evaluating the matching quality between source and destination segments that does not rely on overlapping measures is pro- posed. Results and subjective evaluation of the registration of visual and thermal infrared images are presented. © 2000 Society of Photo-Optical Instru- mentation Engineers. (S0091-3286(00)03401-2)