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Saliency detection and edge feature matching approach for crater extraction

Journal of Systems Engineering and Electronics 2015年第6期26卷 1291-1300页

作者： An Liu Donghua Zhou Lixin Chen Maoyin Chen Department of Automation Tsinghua University Beijing Institute of Special Electronic-mechanical Technology

Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.

关键词： crater automatic extraction visual saliency featurematching edge detection.

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Research on Feature Matching Optimization Algorithm for Automotive Panoramic Surround View System

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Computer Systems Science & Engineering 2024年第5期48卷 1329-1348页

作者： Guangbing Xiao Ruijie Gu Ning Sun Yong Zhang College of Automobile and Traffic Engineering Nanjing Forestry UniversityNanjing210037China

In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational *** ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point *** further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature *** to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.

关键词： featurematching automotive panoramic surround view system principal component analysis euclidean distance dual-heap filtering

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