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Lluis Pere de las Heras, Oriol Ramos Terrades, Sergi Robles, & Gemma Sanchez. (2015). CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool. IJDAR - International Journal on Document Analysis and Recognition, 18(1), 15–30.
Abstract: Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research.
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Cristhian A. Aguilera-Carrasco, Angel Sappa, Cristhian Aguilera, & Ricardo Toledo. (2017). Cross-Spectral Local Descriptors via Quadruplet Network. SENS - Sensors, 17(4), 873.
Abstract: This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.
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A.F. Sole, Antonio Lopez, & G. Sapiro. (2001). Crease Enhancement Diffusion. Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298), .
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Angel Sappa, & Boris X. Vintimilla. (2007). Cost-Based Closed Contour Representations. Journal of Electronic Imaging, 16(2), 023009 (9 pages).
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Joan Serrat, Felipe Lumbreras, & Antonio Lopez. (2013). Cost estimation of custom hoses from STL files and CAD drawings. COMPUTIND - Computers in Industry, 64(3), 299–309.
Abstract: We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%.
Keywords: On-line quotation; STL format; Regression; Gaussian process
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