Mr. Huilin Wang Profile

Huilin Wang

at Nanjing University

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Publications (2)

Proceedings Article | 18 October 2016 Paper

Downscaling soil moisture using multisource data in China

Ru An, Hui-Lin Wang, Jia-jun You, Ying Wang, Xiao-ji Shen, Wei Gao, Yi-nan Wang, Yu Zhang, Zhe Wang, Jonathan Arthur Quaye-Ballardd, Yuehong Chen

Proceedings Volume 10004, 100041Z (2016) https://doi.org/10.1117/12.2241247

KEYWORDS: Soil science, Error analysis, Spatial resolution, Environmental sensing, Data modeling, Microwave radiation, Climatology, Vegetation, Temperature metrology, Performance modeling

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Proceedings Article | 14 May 2014 Paper

A fast image matching algorithm based on key points

Huilin Wang, Ying Wang, Ru An, Peng Yan

Proceedings Volume 9158, 91580A (2014) https://doi.org/10.1117/12.2063850

KEYWORDS: Evolutionary algorithms, Algorithm development, Distortion, Principal component analysis, Image sensors, Image processing, Detection and tracking algorithms, Remote sensing, Sensors, Image registration

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Image matching is a very important technique in image processing. It has been widely used for object recognition and tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes: (1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher stability. This approach in detecting key points has characteristics of small amount of computation, high positioning accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector. These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to overcome rotation. Results show the effectiveness of the approach.

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