Abstract: The amount of multimedia data collected in museum databases is growing fast, while the capacity of museums to display information to visitors is acutely limited by physical space. Museums must seek the perfect balance of information given on individual pieces in order to provide sufficient information to aid visitor understanding while maintaining sparse usage of the walls and guaranteeing high appreciation of the exhibit. Moreover, museums often target the interests of average visitors instead of the entire spectrum of different interests each individual visitor might have. Finally, visiting a museum should not be an experience contained in the physical space of the museum but a door opened onto a broader context of related artworks, authors, artistic trends, etc. In this paper we describe the MNEMOSYNE system that attempts to address these issues through a new multimedia museum experience. Based on passive observation, the system builds a profile of the artworks of interest for each visitor. These profiles of interest are then used to drive an interactive table that personalizes multimedia content delivery. The natural user interface on the interactive table uses the visitor’s profile, an ontology of museum content and a recommendation system to personalize exploration of multimedia content. At the end of their visit, the visitor can take home a personalized summary of their visit on a custom mobile application. In this article we describe in detail each component of our approach as well as the first field trials of our prototype system built and deployed at our permanent exhibition space at LeMurate (http://www.lemurate.comune.fi.it/lemurate/) in Florence together with the first results of the evaluation process during the official installation in the National Museum of Bargello (http://www.uffizi.firenze.it/musei/?m=bargello).

Abstract: Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset.

Abstract: One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes.

Abstract: The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers.

Abstract: 0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches.

Abstract: Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data.

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.