In this paper, an image analysis method for future compression based on a weight model was proposed, which allows one to estimate the significance of the detailing coefficients of the orthogonal multiple-scale wavelet transform in terms of their contribution to the total image energy. The method presupposes the decomposition of the original image into a given number of levels, construction of significance maps for the detailing coefficients of each level before coding of the significant coefficients.
In this paper, an image compression method based on a weight model was proposed, which allows one to estimate the significance of the detailing coefficients of the orthogonal multiple-scale wavelet transform in terms of their contribution to the total image energy. The method presupposes the decomposition of the original image into a given number of levels, construction of significance maps for the detailing coefficients of each level, and coding of the significant coefficients. The software implementation of the proposed method in a high-level language is described, which made it possible to reduce the volume of standard test halftone images by at least six times.
The paper considers a new approach to image edges detecting. It is based on the weight image model application. In this model, image pixels are evaluated in terms of their importance for perception. For this we use energy signs, the calculation of which is based on the wavelet transform. The proposed approach allows you to create an effective edge detector for use in various computer vision systems, for example, in aviation systems, traffic control systems, road surface monitoring systems, biometric identification systems, etc.
The paper considers the model of representation of images using the energy characteristics of the wavelet transform. It describes an approach to the detection of edges on images based on the energy features of the wavelet transform. This approach is a modification of the Canny edge detector. It can be applied to the construction of effective information processing and control systems based on computer vision technologies.
In this article the new approach of contours detection in the images using energy characteristics of wavelet transform is proposed. This approach of contours detection with added procedure of contours intensification in the binary image, their connection and description can be considered as the base of different information systems connected with image processing and analysis. Also this approach based on image representation using energy models has its own independent meaning and can be used not only for contours detection, but for salient points detection and texture features extraction.
In this article the problem of image analysis in unmanned aerial vehicle on-board system for objects detection and recognition with the help of energy characteristics based on wavelet transform is described. The approach of salient points extraction based on wavelet transform is proposed. The salience of the points is substantiated with the energy estimates of their weights. On the basis of wavelet transform salient points extraction the method of image contour segmentation is proposed. For further image recognition the salient points descriptors constructed with the help of wavelet transform are used. The objects detection and recognition system for unmanned aerial vehicles based on proposed methods is simulated using the simulation platform.
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