An Image Similarity Invariant Feature Extraction Method Based on Radon Transform

With the advancements of computer technology, image recognition technology has been more and more widely applied and feature extraction is a core problem of image recognition. In study, image recognition classifies the processed image and identifies the category it belongs to. By selecting the feature to be extracted, it measures the necessary parameters and classifies according to the result. For better recognition, it needs to conduct structural analysis and image description of the entire image and enhance image understanding through multi-object structural relationship. The essence of Radon transform is to reconstruct the original N-dimensional image in N-dimensional space according to the N-1 dimensional projection data of N-dimensional image in different directions. The Radon transform of image is to extract the feature in the transform domain and map the image space to the parameter space. This paper study the inverse problem of Radon transform of the upper semicircular curve with compact support and continuous in the support. When the center and radius of a circular curve change in a certain range, the inversion problem is unique when the Radon transform along the upper semicircle curve is known. In order to further improve the robustness and discrimination of the features extracted, given the image translation or proportional scaling and the removal of impact caused by translation and proportion, this paper has proposed an image similarity invariant feature extraction method based on Radon transform, constructed Radon moment invariant and shown the description capacity of shape feature extraction method on shape feature by getting intra-class ratio. The experiment result has shown that the method of this paper has overcome the flaws of cracks, overlapping, fuzziness and fake edges which exist when extracting features alone, it can accurately extract the corners of the digital image and has good robustness to noise. It has effectively improved the accuracy and continuity of complex image feature extraction.

Bright - Proposed Family of Lightweight Block Ciphers for IoT-Enabled Smart Environment

Lightweight security algorithms are tailored for resource-constrained environment. To improve the efficiency of an algorithm, usually, a tradeoff is involved in lightweight cryptography in terms of its memory requirements and speed. This paper proposes a software-oriented new family of lightweight block ciphers, BRIGHT. Proposed family of ciphers support a range of block and key sizes for constraint environment. BRIGHT family has 6 variants and all variants fulfill Strict Avalanche Criteria and key sensitivity test. It is believed that BRIGHT family of ciphers provides better security and performance in IoT-enabled smart environment. Our aim, while designing BRIGHT is to enhance the cipher for IoT applications. For this, we have used the concept of key whitening that helps to resist against attacks like MITM and brute-force. Round permutation in BRIGHT results in stronger and faster diffusion and provides resistance against linear, differential, impossible differential, related-key rectangle, biclique, MITM, and statistical saturation attack which is likely to be applied to GFN based ciphers. BRIGHT using round constant thwarts attacks like rotational cryptanalysis, self-similarity, invariant attack, related-key attacks, and weak key attacks.

INVESTIGATION OF PHYSICAL PHENOMENA OCCURRED IN CONTACT AREA OF BULK PARTICLES AT THEIR MOTION IN SCREW DRUMS BY METHODS OF SIMILARITY THEORY, ENGINEERING AND COMPUTER GRAPHICS

In Trubilin State Agricultural University of Kuban there are created machinery working devices as screw drums allowing the assurance of motion of bulk particles at their horizontal location and also promoting the intensity of particles interaction between each other and with the walls of screw drums which widens technological potentialities and decreases dimensions of equipment and its weight. In the paper there are shown various sorts of screw drums and analytical methods of the study of physical phenomena taking place in the contact area of bulk particles. The search of a screw drum design was carried out by the methods of descriptive geometry and engineering graphics with the aid of the “Compass-3D” program complex. The apparatus of dimensionless kinematic functions (similarity invariant) and the analysis of dimensionalities allowing the investigation not one such a case but their infinite number united by the community of properties was used.

Automatic Registration of Optical Images with Airborne LiDAR Point Cloud in Urban Scenes Based on Line-Point Similarity Invariant and Extended Collinearity Equations

This paper proposes a novel method to achieve the automatic registration of optical images and Light Detection and Ranging (LiDAR) points in urban areas. The whole procedure, which adopts a coarse-to-precise registration strategy, can be summarized as follows: Coarse registration is performed through a conventional point-feature-based method. The points needed can be extracted from both datasets through a matured point extractor, such as the Forster operator, followed by the extraction of straight lines. Considering that lines are mainly from building roof edges in urban scenes, and being aware of their inaccuracy when extracted from an irregularly spaced point cloud, an "infinitesimal feature analysis method" fully utilizing LiDAR scanning characteristics is proposed to refine edge lines. Points which are matched between the image and LiDAR data are then applied as guidance to search for matched lines via the line-point similarity invariant. Finally, a transformation function based on extended collinearity equations is applied to achieve precise registration. The experimental results show that the proposed method outperforms the conventional ones in terms of the registration accuracy and automation level.

EFFICIENT ORIENTATION AND CALIBRATION OF LARGE AERIAL BLOCKS OF MULTI-CAMERA PLATFORMS

Aerial multi-camera platforms typically incorporate a nadir-looking camera accompanied by further cameras that provide oblique views, potentially resulting in utmost coverage, redundancy, and accuracy even on vertical surfaces. However, issues have remained unresolved with the orientation and calibration of the resulting imagery, to two of which we present feasible solutions. First, as standard feature point descriptors used for the automated matching of homologous points are only invariant to the geometric variations of translation, rotation, and scale, they are not invariant to general changes in perspective. While the deviations from local 2D-similarity transforms may be negligible for corresponding surface patches in vertical views of flat land, they become evident at vertical surfaces, and in oblique views in general. Usage of such similarity-invariant descriptors thus limits the amount of tie points that stabilize the orientation and calibration of oblique views and cameras. To alleviate this problem, we present the positive impact on image connectivity of using a quasi affine-invariant descriptor. Second, no matter which hard- and software are used, at some point, the number of unknowns of a bundle block may be too large to be handled. With multi-camera platforms, these limits are reached even sooner. Adjustment of sub-blocks is sub-optimal, as it complicates data management, and hinders self-calibration. Simply discarding unreliable tie points of low manifold is not an option either, because these points are needed at the block borders and in poorly textured areas. As a remedy, we present a straight-forward method how to considerably reduce the number of tie points and hence unknowns before bundle block adjustment, while preserving orientation and calibration quality.