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Fadi Dornaika, & Bogdan Raducanu. (2008). Detecting and Tracking of 3D Face Pose for Human-Robot Interaction. In IEEE International Conference on Robotics and Automation, (1716–1721).
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Adriana Romero, Petia Radeva, & Carlo Gatta. (2015). Meta-parameter free unsupervised sparse feature learning. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1716–1722.
Abstract: We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well.
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Sergio Escalera, Xavier Baro, Jordi Vitria, Petia Radeva, & Bogdan Raducanu. (2012). Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction. SENS - Sensors, 12(2), 1702–1719.
Abstract: IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog.
The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.
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Mohamed Ali Souibgui, Sanket Biswas, Sana Khamekhem Jemni, Yousri Kessentini, Alicia Fornes, Josep Llados, et al. (2022). DocEnTr: An End-to-End Document Image Enhancement Transformer. In 26th International Conference on Pattern Recognition (pp. 1699–1705).
Abstract: Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR
Keywords: Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads
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Marina Alberti, Simone Balocco, Xavier Carrillo, Josefina Mauri, & Petia Radeva. (2013). Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation. UMB - Ultrasound in Medicine and Biology, 39(9), 1698–712.
Abstract: Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages.
Keywords: Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy
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Minesh Mathew, Viraj Bagal, Ruben Tito, Dimosthenis Karatzas, Ernest Valveny, & C.V. Jawahar. (2022). InfographicVQA. In Winter Conference on Applications of Computer Vision (pp. 1697–1706).
Abstract: Infographics communicate information using a combination of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reasoning and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results suggest that VQA on infographics--images that are designed to communicate information quickly and clearly to human brain--is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa. org
Keywords: Document Analysis Datasets; Evaluation and Comparison of Vision Algorithms; Vision and Languages
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Carlo Gatta, & Francesco Ciompi. (2014). Stacked Sequential Scale-Space Taylor Context. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1694–1700.
Abstract: We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.
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Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2012). A medial map capturing the essential geometry of organs. In ISBI Workshop on Open Source Medical Image Analysis software (1691 - 1694). IEEE.
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Accurate computation of one pixel wide medial surfaces is mandatory. Those surfaces must represent faithfully the geometry of the volume. Although morphological methods produce excellent results in 2D, their complexity and quality drops across dimensions, due to a more complex description of pixel neighborhoods. This paper introduces a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. Our experiments show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume
Keywords: Medial Surface Representation, Volume Reconstruction,Geometry , Image reconstruction , Liver , Manifolds , Shape , Surface morphology , Surface reconstruction
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Murad Al Haj, Andrew Bagdanov, Jordi Gonzalez, & Xavier Roca. (2010). Reactive object tracking with a single PTZ camera. In 20th International Conference on Pattern Recognition (1690–1693).
Abstract: In this paper we describe a novel approach to reactive tracking of moving targets with a pan-tilt-zoom camera. The approach uses an extended Kalman filter to jointly track the object position in the real world, its velocity in 3D and the camera intrinsics, in addition to the rate of change of these parameters. The filter outputs are used as inputs to PID controllers which continuously adjust the camera motion in order to reactively track the object at a constant image velocity while simultaneously maintaining a desirable target scale in the image plane. We provide experimental results on simulated and real tracking sequences to show how our tracker is able to accurately estimate both 3D object position and camera intrinsics with very high precision over a wide range of focal lengths.
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Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang, & Joost van de Weijer. (2024). Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning. In Winter Conference on Applications of Computer Vision (pp. 1690–1700).
Abstract: Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL methods using SSL can learn high-quality representations without any labels, but with a notable performance drop when learning on a many-tasks data stream. We hypothesize that this is caused by the regularization losses that are imposed to prevent forgetting, leading to a suboptimal plasticity-stability trade-off: they either do not adapt fully to the incoming data (low plasticity), or incur significant forgetting when allowed to fully adapt to a new SSL pretext-task (low stability). In this work, we propose to train an expert network that is relieved of the duty of keeping the previous knowledge and can focus on performing optimally on the new tasks (optimizing plasticity). In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network. We perform several experiments showing that our proposed approach outperforms other CURL exemplar-free methods in few- and many-task split settings. Furthermore, we show how to adapt our approach to semi-supervised continual learning (Semi-SCL) and show that we surpass the accuracy of other exemplar-free Semi-SCL methods and reach the results of some others that use exemplars.
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Simone Balocco, O. Camara, E. Vivas, T. Sola, L. Guimaraens, H. A. van Andel, et al. (2010). Feasibility of Estimating Regional Mechanical Properties of Cerebral Aneurysms In Vivo. MEDPHYS - Medical Physics, 37(4), 1689–1706.
Abstract: PURPOSE:
In this article, the authors studied the feasibility of estimating regional mechanical properties in cerebral aneurysms, integrating information extracted from imaging and physiological data with generic computational models of the arterial wall behavior.
METHODS:
A data assimilation framework was developed to incorporate patient-specific geometries into a given biomechanical model, whereas wall motion estimates were obtained from applying registration techniques to a pair of simulated MR images and guided the mechanical parameter estimation. A simple incompressible linear and isotropic Hookean model coupled with computational fluid-dynamics was employed as a first approximation for computational purposes. Additionally, an automatic clustering technique was developed to reduce the number of parameters to assimilate at the optimization stage and it considerably accelerated the convergence of the simulations. Several in silico experiments were designed to assess the influence of aneurysm geometrical characteristics and the accuracy of wall motion estimates on the mechanical property estimates. Hence, the proposed methodology was applied to six real cerebral aneurysms and tested against a varying number of regions with different elasticity, different mesh discretization, imaging resolution, and registration configurations.
RESULTS:
Several in silico experiments were conducted to investigate the feasibility of the proposed workflow, results found suggesting that the estimation of the mechanical properties was mainly influenced by the image spatial resolution and the chosen registration configuration. According to the in silico experiments, the minimal spatial resolution needed to extract wall pulsation measurements with enough accuracy to guide the proposed data assimilation framework was of 0.1 mm.
CONCLUSIONS:
Current routine imaging modalities do not have such a high spatial resolution and therefore the proposed data assimilation framework cannot currently be used on in vivo data to reliably estimate regional properties in cerebral aneurysms. Besides, it was observed that the incorporation of fluid-structure interaction in a biomechanical model with linear and isotropic material properties did not have a substantial influence in the final results.
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Palaiahnakote Shivakumara, Anjan Dutta, Trung Quy Phan, Chew Lim Tan, & Umapada Pal. (2011). A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video. PR - Pattern Recognition, 44(8), 1671–1683.
Abstract: In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels.
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Aura Hernandez-Sabate, Debora Gil, Eduard Fernandez-Nofrerias, Petia Radeva, & Enric Marti. (2009). Approaching Artery Rigid Dynamics in IVUS. TMI - IEEE Transactions on Medical Imaging, 28(11), 1670–1680.
Abstract: Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel-catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical cases.
Keywords: Fourier analysis; intravascular ultrasound (IVUS) dynamics; longitudinal motion; quality measures; tissue deformation.
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Anjan Dutta, Jaume Gibert, Josep Llados, Horst Bunke, & Umapada Pal. (2012). Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents. In 21st International Conference on Pattern Recognition (pp. 1663–1666).
Abstract: This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging.
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Veronica Romero, Alicia Fornes, Nicolas Serrano, Joan Andreu Sanchez, A.H. Toselli, Volkmar Frinken, et al. (2013). The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. PR - Pattern Recognition, 46(6), 1658–1669.
Abstract: Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies.
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