A. M. Here, B. C. Lopez, Debora Gil, J. J. Camarero, & Jordi Martinez-Vilalta. (2013). A new software to analyse wood anatomical features in conifer species. In International Symposium on Wood Structure in Plant Biology and Ecology.
Abstract: International Symposium on Wood Structure in Plant Biology and Ecology
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A.S. Coquel, Jean-Pascal Jacob, M. Primet, A. Demarez, Mariella Dimiccoli, T. Julou, et al. (2013). Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect. PCB - Plos Computational Biology, 9(4).
Abstract: Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of “soft” intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli.
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Abel Gonzalez-Garcia, Robert Benavente, Olivier Penacchio, Javier Vazquez, Maria Vanrell, & C. Alejandro Parraga. (2013). Coloresia: An Interactive Colour Perception Device for the Visually Impaired. In Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 47–66). Springer Berlin Heidelberg.
Abstract: A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves.
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Adriana Romero, & Carlo Gatta. (2013). Do We Really Need All These Neurons? In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 460–467). LNCS. Springer Berlin Heidelberg.
Abstract: Restricted Boltzmann Machines (RBMs) are generative neural networks that have received much attention recently. In particular, choosing the appropriate number of hidden units is important as it might hinder their representative power. According to the literature, RBM require numerous hidden units to approximate any distribution properly. In this paper, we present an experiment to determine whether such amount of hidden units is required in a classification context. We then propose an incremental algorithm that trains RBM reusing the previously trained parameters using a trade-off measure to determine the appropriate number of hidden units. Results on the MNIST and OCR letters databases show that using a number of hidden units, which is one order of magnitude smaller than the literature estimate, suffices to achieve similar performance. Moreover, the proposed algorithm allows to estimate the required number of hidden units without the need of training many RBM from scratch.
Keywords: Retricted Boltzmann Machine; hidden units; unsupervised learning; classification
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Albert Clapes, Miguel Reyes, & Sergio Escalera. (2013). Multi-modal User Identification and Object Recognition Surveillance System. PRL - Pattern Recognition Letters, 34(7), 799–808.
Abstract: We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
Keywords: Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning
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Albert Gordo. (2013). Document Image Representation, Classification and Retrieval in Large-Scale Domains (Ernest Valveny, & Florent Perronnin, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements.
Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time.
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Albert Gordo, Alicia Fornes, & Ernest Valveny. (2013). Writer identification in handwritten musical scores with bags of notes. PR - Pattern Recognition, 46(5), 1337–1345.
Abstract: Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset.
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Albert Gordo, Florent Perronnin, & Ernest Valveny. (2013). Large-scale document image retrieval and classification with runlength histograms and binary embeddings. PR - Pattern Recognition, 46(7), 1898–1905.
Abstract: We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits.
Keywords: visual document descriptor; compression; large-scale; retrieval; classification
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Albert Gordo, Marçal Rusiñol, Dimosthenis Karatzas, & Andrew Bagdanov. (2013). Document Classification and Page Stream Segmentation for Digital Mailroom Applications. In 12th International Conference on Document Analysis and Recognition (pp. 621–625).
Abstract: In this paper we present a method for the segmentation of continuous page streams into multipage documents and the simultaneous classification of the resulting documents. We first present an approach to combine the multiple pages of a document into a single feature vector that represents the whole document. Despite its simplicity and low computational cost, the proposed representation yields results comparable to more complex methods in multipage document classification tasks. We then exploit this representation in the context of page stream segmentation. The most plausible segmentation of a page stream into a sequence of multipage documents is obtained by optimizing a statistical model that represents the probability of each segmented multipage document belonging to a particular class. Experimental results are reported on a large sample of real administrative multipage documents.
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Alex Pardo, Albert Clapes, Sergio Escalera, & Oriol Pujol. (2013). Actions in Context: System for people with Dementia. In 2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems (pp. 3–14). Springer International Publishing.
Abstract: In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities – ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios.
Keywords: Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia
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Alicia Fornes, Xavier Otazu, & Josep Llados. (2013). Show through cancellation and image enhancement by multiresolution contrast processing. In 12th International Conference on Document Analysis and Recognition (pp. 200–204).
Abstract: Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities.
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Alvaro Cepero, Albert Clapes, & Sergio Escalera. (2013). Quantitative analysis of non-verbal communication for competence analysis. In 16th Catalan Conference on Artificial Intelligence (Vol. 256, pp. 105–114).
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Anastasios Doulamis, Nikolaos Doulamis, Marco Bertini, Jordi Gonzalez, & Thomas B. Moeslund. (2013). Analysis and Retrieval of Tracked Events and Motion in Imagery Streams.
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Andreas Fischer, Ching Y. Suen, Volkmar Frinken, Kaspar Riesen, & Horst Bunke. (2013). A Fast Matching Algorithm for Graph-Based Handwriting Recognition. In 9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition (Vol. 7877, pp. 194–203). LNCS. Springer Berlin Heidelberg.
Abstract: The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.
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Andreas Fischer, Volkmar Frinken, Horst Bunke, & Ching Y. Suen. (2013). Improving HMM-Based Keyword Spotting with Character Language Models. In 12th International Conference on Document Analysis and Recognition (pp. 506–510).
Abstract: Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance.
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