Pau Torras, Mohamed Ali Souibgui, Jialuo Chen, & Alicia Fornes. (2021). A Transcription Is All You Need: Learning to Align through Attention. In 14th IAPR International Workshop on Graphics Recognition (Vol. 12916, 141–146). LNCS.
Abstract: Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset.
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Oscar Amoros, Sergio Escalera, & Anna Puig. (2011). Adaboost GPU-based Classifier for Direct Volume Rendering. In International Conference on Computer Graphics Theory and Applications (pp. 215–219).
Abstract: In volume visualization, the voxel visibitity and materials are carried out through an interactive editing of Transfer Function. In this paper, we present a two-level GPU-based labeling method that computes in times of rendering a set of labeled structures using the Adaboost machine learning classifier. In a pre-processing step, Adaboost trains a binary classifier from a pre-labeled dataset and, in each sample, takes into account a set of features. This binary classifier is a weighted combination of weak classifiers, which can be expressed as simple decision functions estimated on a single feature values. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. We propose an alternative representation of these classifiers that allow a GPU-based parallelizated testing stage embedded into the visualization pipeline. The empirical results confirm the OpenCL-based classification of biomedical datasets as a tough problem where an opportunity for further research emerges.
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Mikhail Mozerov, Ariel Amato, & Xavier Roca. (2009). Occlusion Handling in Trinocular Stereo using Composite Disparity Space Image. In 19th International Conference on Computer Graphics and Vision (69–73).
Abstract: In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) can be merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. A dense disparity map is obtained with a global optimization algorithm using the proposed composite DSI. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. One of the top positions in the rank of the Middlebury website confirms the performance of our method to be competitive with the best stereo matching.
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Lei Kang, Juan Ignacio Toledo, Pau Riba, Mauricio Villegas, Alicia Fornes, & Marçal Rusiñol. (2018). Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition. In 40th German Conference on Pattern Recognition (pp. 459–472).
Abstract: This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR.
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Jaume Gibert, Ernest Valveny, & Horst Bunke. (2011). Dimensionality Reduction for Graph of Words Embedding. In Xiaoyi Jiang, Miquel Ferrer, & Andrea Torsello (Eds.), 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition (Vol. 6658, pp. 22–31). LNCS.
Abstract: The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs.
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Pau Riba, Josep Llados, Alicia Fornes, & Anjan Dutta. (2015). Large-scale Graph Indexing using Binary Embeddings of Node Contexts. In C.-L.Liu, B.Luo, W.G.Kropatsch, & J.Cheng (Eds.), 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition (Vol. 9069, pp. 208–217). LNCS. Springer International Publishing.
Abstract: Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
Keywords: Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding
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Pau Riba, Josep Llados, & Alicia Fornes. (2017). Error-tolerant coarse-to-fine matching model for hierarchical graphs. In Pasquale Foggia, Cheng-Lin Liu, & Mario Vento (Eds.), 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition (Vol. 10310, pp. 107–117). Springer International Publishing.
Abstract: Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.
Keywords: Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching
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Miquel Ferrer, Dimosthenis Karatzas, Ernest Valveny, & Horst Bunke. (2009). A Recursive Embedding Approach to Median Graph Computation. In 7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition (Vol. 5534, 113–123). LNCS. Springer Berlin Heidelberg.
Abstract: The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches.
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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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Aura Hernandez-Sabate, Lluis Albarracin, Daniel Calvo, & Nuria Gorgorio. (2016). EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game. In 5th International Conference Games and Learning Alliance (Vol. 10056, pp. 50–59). LNCS.
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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Laura Lopez-Fuentes, Sebastia Massanet, & Manuel Gonzalez-Hidalgo. (2017). Image vignetting reduction via a maximization of fuzzy entropy. In IEEE International Conference on Fuzzy Systems.
Abstract: In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view.
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Oscar Camara, Estanislao Oubel, Gemma Piella, Simone Balocco, Mathieu De Craene, & Alejandro F. Frangi. (2009). Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging. In 5th International Conference on Functional Imaging and Modeling of the Heart (Vol. 5528, 330–338). LNCS. Springer Berlin Heidelberg.
Abstract: In this work, we present a registration framework for cardiac cine MRI (cMRI), tagged (tMRI) and delay-enhancement MRI (deMRI), where the two main issues to find an accurate alignment between these images have been taking into account: the presence of tags in tMRI and respiration artifacts in all sequences. A steerable pyramid image decomposition has been used for detagging purposes since it is suitable to extract high-order oriented structures by directional adaptive filtering. Shift correction of cMRI is achieved by firstly maximizing the similarity between the Long Axis and Short Axis cMRI. Subsequently, these shift-corrected images are used as target images in a rigid registration procedure with their corresponding tMRI/deMRI in order to correct their shift. The proposed registration framework has been evaluated by 840 registration tests, considerably improving the alignment of the MR images (mean RMS error of 2.04mm vs. 5.44mm).
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Debora Gil, Aura Hernandez-Sabate, Antoni Carol, Oriol Rodriguez, & Petia Radeva. (2005). A Deterministic-Statistic Adventitia Detection in IVUS Images. In 3rd International workshop on International Workshop on Functional Imaging and Modeling of the Heart (pp. 65–74).
Abstract: Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles.
Keywords: Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation
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Rain Eric Haamer, Kaustubh Kulkarni, Nasrin Imanpour, Mohammad Ahsanul Haque, Egils Avots, Michelle Breisch, et al. (2018). Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification. In 8th International Workshop on Human Behavior Understanding.
Abstract: Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.
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Ciprian Corneanu, Meysam Madadi, Sergio Escalera, & Aleix Martinez. (2020). Explainable Early Stopping for Action Unit Recognition. In Faces and Gestures in E-health and welfare workshop (pp. 693–699).
Abstract: A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it.
In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it,
we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs.
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