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Carrasco, M., Mery, D., Concha, A., Velazquez, R., De Fazio, R., & Visconti, P. (2021). An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses. Electronics, 10(3), 246.
Abstract: Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.
Keywords: computer vision; correspondence problem; fundamental matrix; multiple view geometry; point matching; trifocal tensor
Mery, D., Riffo, V., Zscherpel, U., Mondragon, G., Lillo, I., Zuccar, I., et al. (2015). GDXray: The Database of X-ray Images for Nondestructive Testing. J. Nondestruct. Eval., 34(4), 12 pp.
Abstract: In this paper, we present a new dataset consisting of 19,407 X-ray images. The images are organized in a public database called GDXray that can be used free of charge, but for research and educational purposes only. The database includes five groups of X-ray images: castings, welds, baggage, natural objects and settings. Each group has several series, and each series several X-ray images. Most of the series are annotated or labeled. In such cases, the coordinates of the bounding boxes of the objects of interest or the labels of the images are available in standard text files. The size of GDXray is 3.5 GB and it can be downloaded from our website. We believe that GDXray represents a relevant contribution to the X-ray testing community. On the one hand, students, researchers and engineers can use these X-ray images to develop, test and evaluate image analysis and computer vision algorithms without purchasing expensive X-ray equipment. On the other hand, these images can be used as a benchmark in order to test and compare the performance of different approaches on the same data. Moreover, the database can be used in the training programs of human inspectors.
Keywords: X-ray testing; Datasets; X-ray images; Computer vision; Image analysis