The very fast technological progress provokes the creation of a big volume of the information that can be fed in different forms. There are various science directions that use high dimensional data sets for the analysis. In this paper we presented the few aspects of the "knowledge discovery in databases" (KDD) process related to the Data Mining stage in astronomy, analyzed and reviewed Data Mining approaches. We presented the examples of astronomical sources of Big Data, instruments, information types, processing algorithms that can be used for the Data Mining process in astronomy. The paper deals with applying the CoLiTec (Collection Light Technology) software for the online processing of the different types of astronomical information using the Data Mining approach. This is achieved by using of the developed OnLine Data Analysis System (OLDAS), which helps with solving of the Data Mining tasks, like clustering, classification, and identification.

Data Mining of the Astronomical Images by the CoLiTec Software

Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corners and unified metrics to evaluate their corner detection performance. In this paper, we build three benchmark datasets for corner detection. The first two consist of those binary and gray-value images that have been commonly used in previous corner detection studies. The third one contains a set of urban images, called the Urban-Corner dataset. For each test image in these three datasets, the ground-truth corners are manually labeled as objectively as possible with the assistance of a line segment detector. Then, a set of benchmark evaluation metrics is suggested, including five conventional ones: the precision, the recall, the arithmetic mean of precision and recall (APR), the F score, the localization error (Le), and a new one proposed in this work called the repeatability referenced to ground truth (RGT). Finally, a comprehensive evaluation of current state-of-the-art corner detectors is conducted.

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A Benchmark for the Evaluation of Corner Detectors

This paper presents a novel image-based path planning algorithm that was developed using computer vision techniques, as well as its comparative analysis with well-known deterministic and probabilistic algorithms, namely A* and Probabilistic Road Map algorithm (PRM). The terrain depth has a significant impact on the calculated path safety. The craters and hills on the surface cannot be distinguished in a two-dimensional image. The proposed method uses a disparity map of the terrain that is generated by using a UAV. Several computer vision techniques, including edge, line and corner detection methods, as well as the stereo depth reconstruction technique, are applied to the captured images and the found disparity map is used to define candidate way-points of the trajectory. The initial and desired points are detected automatically using ArUco marker pose estimation and circle detection techniques. After presenting the mathematical model and vision techniques, the developed algorithm is compared with well-known algorithms on different virtual scenes created in the V-REP simulation program and a physical setup created in a laboratory environment. Results are promising and demonstrate effectiveness of the proposed algorithm. 

An Image-Based Path Planning Algorithm Using a UAV Equipped with Stereo Vision

This paper presents a novel image-based path planning algorithm that was developed using computer vision techniques, as well as its comparative analysis with well-known deterministic and probabilistic algorithms, namely A* and Probabilistic Road Map algorithm (PRM). The terrain depth has a significant impact on the calculated path safety. The craters and hills on the surface cannot be distinguished in a two-dimensional image. The proposed method uses a disparity map of the terrain that is generated by using a UAV. Several computer vision techniques, including edge, line and corner detection methods, as well as the stereo depth reconstruction technique, are applied to the captured images and the found disparity map is used to define candidate way-points of the trajectory. The initial and desired points are detected automatically using ArUco marker pose estimation and circle detection techniques. After presenting the mathematical model and vision techniques, the developed algorithm is compared with well-known algorithms on different virtual scenes created in the V-REP simulation program and a physical setup created in a laboratory environment. Results are promising and demonstrate effectiveness of the proposed algorithm.

Corner detection is a common method to obtain image features, and the detection effect influences the performance of matching and tracking directly. A FAST-Harris fusion corner detection algorithm is proposed to improve the shortcomings of the Harris algorithm, such as the low detection accuracy and low positioning accuracy, and a corner detection fusion model is established. First, the detected target image is padded, and then the FAST algorithm is used with a 25% reduced contrast points to achieve fast capture roughly; in this way, a candidate corner set is obtained. Then, screening the candidate corner is set one by one by calculating the response function of the Harris with Scharr operator to achieve capture accurately. Finally, the real corners are obtained using SAD for nonmaximum suppression. The positioning error, error detection rate, robustness, and running time of corner detection are obtained by the PyCharm platform. Compared with Harris, the error detection rate and localization error of the algorithm are reduced by 16.89% and 42.04%, respectively. Compared with 8 popular corner detection algorithms, the error detection rate and localization error of the algorithm in this paper are the lowest, which are 24.60% and 1.42 pixels. The robust performance in lossy JPEG compression is the best, with 17.37% shorter running time than Harris algorithm. The method in this paper can be used in scenarios such as autonomous driving and image search services.

/pdf/a-target-corner-detection-algorithm-based-on-the-fusion-of-2c6yiqik.pdf

A Target Corner Detection Algorithm Based on the Fusion of FAST and Harris

Aiming at the low matching accuracy and poor robustness in video sequence image matching, a video sequence image matching algorithm based on Adaptive Gamma Correction is proposed in the paper. Firstly, Adaptive Gamma Correction is used to enhance the contrast of video sequence images. Then feature extraction and description are realized by the ORB algorithm. The RANSAC algorithm is optimized in matching by improving the interior point ratio after Brute Force Match. The experimental results show that the algorithm proposed improves the correct matching rate by 19.51%, 12.53%, and 4.87% respectively compared with the SIFT, ORB, and ORB+RANSAC algorithms without increasing the running time. Meanwhile, the algorithm in this paper has good robustness in video sequence image matching.

Video Sequence Image Matching Algorithm Based on Adaptive Gamma Correction

For super high-rise buildings, reasonable structural layout and design optimization help reducing the engineering costs while ensuring structural safety. However, structural designers generally create structural models through manual modelling, which is inefficient and difficult to compare and optimize structural design schemes. In order to solve this problem, after combing through the structural logic of super high-rise building structure, this research adopts the parametric design mode, and proposes an integrated design framework for parametric modelling, analysis and optimization of super high-rise building structure, and developed the design tool on the Grasshopper software. Using this tool, you can quickly create a three-dimensional structure model, and call Etabs software for structural calculations. The tool can also be used in combination with evolutionary optimization algorithms to construct a parametric optimization process for structural optimization. The effectiveness and practicability of the developed design tool is verified through a super high-rise building with frame-core tube structure. The illustrative example has been shown that the tool can help engineers quickly create and modify structural models, and the concrete material consumption can be reduced by 40% after structural optimization. The tool and design process can be extended to similar construction projects to improve the design efficiency of structural engineers and reduce the construction cost of the project.

Yifan Wang

Papers

A Target Corner Detection Algorithm Based on the Fusion of FAST and Harris

Video Sequence Image Matching Algorithm Based on Adaptive Gamma Correction

An integrated parametric structural design tool for super high-rise buildings based on Grasshopper and intelligent algorithm