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Juan Ignacio Toledo, Jordi Cucurull, Jordi Puiggali, Alicia Fornes, & Josep Llados. (2015). Document Analysis Techniques for Automatic Electoral Document Processing: A Survey. In E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 (pp. 139–141). LNCS.
Abstract: In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents.
Keywords: Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally
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Juan Ignacio Toledo, Alicia Fornes, Jordi Cucurull, & Josep Llados. (2016). Election Tally Sheets Processing System. In 12th IAPR Workshop on Document Analysis Systems (pp. 364–368).
Abstract: In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost.
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Juan Ignacio Toledo. (2019). Information Extraction from Heterogeneous Handwritten Documents (Alicia Fornes, & Josep Llados, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: In this thesis we explore information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes roughly equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words.
In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
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Juan Diego Gomez. (2009). Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography (Vol. 134). Master's thesis, , Bellaterra, Barcelona.
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Juan Borrego-Carazo, Carles Sanchez, David Castells, Jordi Carrabina, & Debora Gil. (2023). BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation. CMPB - Computer Methods and Programs in Biomedicine, 228, 107241.
Abstract: Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy.
Keywords: Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation
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Juan Borrego-Carazo, Carles Sanchez, David Castells, Jordi Carrabina, & Debora Gil. (2022). A benchmark for the evaluation of computational methods for bronchoscopic navigation. IJCARS - International Journal of Computer Assisted Radiology and Surgery, 17(1).
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Juan Andrade, T. Alejandra Vidal, & A. Sanfeliu. (2005). Multirobot C-SLAM: Simultaneous localization, control, and mapping.
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Juan Andrade, T. Alejandra Vidal, & A. Sanfeliu. (2005). Stochastic state estimation for simultaneous localization and map building in mobile robotics. In Vedran Kordic, Aleksandar Lazinica, and Munir Merdan (Eds.), Cutting Edge Robotics, Advanced Robotic Systems Press, 3.3:223–242.
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Juan Andrade, T. Alejandra Vidal, & A. Sanfeliu. (2005). Unscented transformation of vehicle states in SLAM.
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Juan Andrade, & F. Thomas. (2006). Wire-Based Tracking using Mutual Information.
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Juan Andrade, & A. Sanfeliu. (2005). The effects of partial observability when building fully correlated maps. IEEE Transactions on Robotics, 21(4):771–777 (IF: 1.486).
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Juan A. Carvajal Ayala, Dennis Romero, & Angel Sappa. (2016). Fine-tuning based deep convolutional networks for lepidopterous genus recognition. In 21st Ibero American Congress on Pattern Recognition (pp. 467–475). LNCS.
Abstract: This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%.
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Josep M. Gonfaus, Xavier Boix, Joost Van de Weijer, Andrew Bagdanov, Joan Serrat, & Jordi Gonzalez. (2010). Harmony Potentials for Joint Classification and Segmentation. In 23rd IEEE Conference on Computer Vision and Pattern Recognition (3280–3287).
Abstract: Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21.
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Josep M. Gonfaus, Theo Gevers, Arjan Gijsenij, Xavier Roca, & Jordi Gonzalez. (2012). Edge Classification using Photo-Geo metric features. In 21st International Conference on Pattern Recognition (pp. 1497–1500).
Abstract: Edges are caused by several imaging cues such as shadow, material and illumination transitions. Classification methods have been proposed which are solely based on photometric information, ignoring geometry to classify the physical nature of edges in images. In this paper, the aim is to present a novel strategy to handle both photometric and geometric information for edge classification. Photometric information is obtained through the use of quasi-invariants while geometric information is derived from the orientation and contrast of edges. Different combination frameworks are compared with a new principled approach that captures both information into the same descriptor. From large scale experiments on different datasets, it is shown that, in addition to photometric information, the geometry of edges is an important visual cue to distinguish between different edge types. It is concluded that by combining both cues the performance improves by more than 7% for shadows and highlights.
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Josep M. Gonfaus, Marco Pedersoli, Jordi Gonzalez, Andrea Vedaldi, & Xavier Roca. (2015). Factorized appearances for object detection. CVIU - Computer Vision and Image Understanding, 138, 92–101.
Abstract: Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories.
Keywords: Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts
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