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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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David Aldavert, Marçal Rusiñol, Ricardo Toledo, & Josep Llados. (2015). A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting. IJDAR - International Journal on Document Analysis and Recognition, 18(3), 223–234.
Abstract: The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014.
Keywords: Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation
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Xavier Soria, Angel Sappa, & Riad I. Hammoud. (2018). Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images. SENS - Sensors, 18(7), 2059.
Abstract: Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
Keywords: RGB-NIR sensor; multispectral imaging; deep learning; CNNs
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Fadi Dornaika, & Angel Sappa. (2008). Evaluation of an Appearance-based 3D Face Tracker using Dense 3D Data. Machine Vision and Applications, 427–441.
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Francisco Blanco, Felipe Lumbreras, Joan Serrat, Roswitha Siener, Silvia Serranti, Giuseppe Bonifazi, et al. (2014). Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy. JBiO - Journal of Biomedical Optics, 19(12), 126004–1 - 126004–9.
Abstract: The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories.
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