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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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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Mariella Dimiccoli. (2016). Figure-ground segregation: A fully nonlocal approach. VR - Vision Research, 126, 308–317.
Abstract: We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas.
Keywords: Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion
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Tadashi Araki, Sumit K. Banchhor, Narendra D. Londhe, Nobutaka Ikeda, Petia Radeva, Devarshi Shukla, et al. (2016). Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos. JMS - Journal of Medical Systems, 40(3), 51:1–51:20.
Abstract: Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm3, 27.79 ± 10.94 mm3, 46.44 ± 19.13 mm3 and 35.92 ± 16.44 mm3 respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student’s t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80 %. Out procedure and protocol is along the line with method previously published clinically.
Keywords: Interventional cardiology; Atherosclerosis; Coronary arteries; IVUS; calcium volume; Soft computing; Performance Reliability; Accuracy
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Mariella Dimiccoli, Jean-Pascal Jacob, & Lionel Moisan. (2016). Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach. MVAP - Journal of Machine Vision and Applications, 27, 511–527.
Abstract: In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor
quality data, particles and trajectories can be characterized by an a-contrario
model, that estimates the probability of observing the structures of interest
in random data. This approach, first introduced in the modeling of human visual
perception and then successfully applied in many image processing tasks, leads
to algorithms that do not require a previous learning stage, nor a tedious
parameter tuning and are very robust to noise. Comparative evaluations against
a well established baseline show that the proposed approach outperforms the
state of the art.
Keywords: particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2016). Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams. CVIU - Computer Vision and Image Understanding, 149, 146–156.
Abstract: Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.
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Mariella Dimiccoli. (2016). Fundamentals of cone regression. Journal of Statistics Surveys, 53–99.
Abstract: Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as an example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Matlab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution.
Keywords: cone regression; linear complementarity problems; proximal operators.
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Jean-Pascal Jacob, Mariella Dimiccoli, & L. Moisan. (2017). Active skeleton for bacteria modelling. CMBBE - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 5(4), 274–286.
Abstract: The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modelling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness and orientation), an improved boundary accuracy in noisy images and a natural bacteria-centred coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimising an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modelling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at http://fluobactracker.inrialpes.fr.
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G. de Oliveira, A. Cartas, Marc Bolaños, Mariella Dimiccoli, Xavier Giro, & Petia Radeva. (2016). LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task. In 12th NTCIR Conference on Evaluation of Information Access Technologies.
Abstract: Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.
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Maria Salamo, Inmaculada Rodriguez, Maite Lopez, Anna Puig, Simone Balocco, & Mariona Taule. (2016). Recurso docente para la atención de la diversidad en el aula mediante la predicción de notas. ReVision.
Abstract: Desde la implantación del Espacio Europeo de Educación Superior (EEES) en los diferentes grados, se ha puesto de manifiesto la necesidad de utilizar diversos mecanismos que permitan tratar la diversidad en el aula, evaluando automáticamente y proporcionando una retroalimentación rápida tanto al alumnado como al profesorado sobre la evolución de los alumnos en una asignatura. En este artículo se presenta la evaluación de la exactitud en las predicciones de GRADEFORESEER, un recurso docente para la predicción de notas basado en técnicas de aprendizaje automático que permite evaluar la evolución del alumnado y estimar su nota final al terminar el curso. Este recurso se ha complementado con una interfaz de usuario para el profesorado que puede ser usada en diferentes plataformas software (sistemas operativos) y en cualquier asignatura de un grado en la que se utilice evaluación continuada. Además de la descripción del recurso, este artículo presenta los resultados obtenidos al aplicar el sistema de predicción en cuatro asignaturas de disciplinas distintas: Programación I (PI), Diseño de Software (DSW) del grado de Ingeniería Informática, Tecnologías de la Información y la Comunicación (TIC) del grado de Lingüística y la asignatura Fundamentos de Tecnología (FDT) del grado de Información y Documentación, todas ellas impartidas en la Universidad de Barcelona.
La capacidad predictiva se ha evaluado de forma binaria (aprueba o no) y según un criterio de rango (suspenso, aprobado, notable o sobresaliente), obteniendo mejores predicciones en los resultados evaluados de forma binaria.
Keywords: Aprendizaje automatico; Sistema de prediccion de notas; Herramienta docente
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Jose Marone, Simone Balocco, Marc Bolaños, Jose Massa, & Petia Radeva. (2016). Learning the Lumen Border using a Convolutional Neural Networks classifier. In 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop.
Abstract: IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To
solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows.
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Sumit K. Banchhor, Tadashi Araki, Narendra D. Londhe, Nobutaka Ikeda, Petia Radeva, Ayman El-Baz, et al. (2016). Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach. CMPB - Computer Methods and Programs in Biomedicine, 134, 237–258.
Abstract: BACKGROUND AND OBJECTIVE:
Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames.
METHODS:
This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio.
RESULTS:
Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings.
CONCLUSIONS:
We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance.
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Alvaro Peris, Marc Bolaños, Petia Radeva, & Francisco Casacuberta. (2016). Video Description Using Bidirectional Recurrent Neural Networks. In 25th International Conference on Artificial Neural Networks (Vol. 2, pp. 3–11).
Abstract: Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.
Keywords: Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks
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Alejandro Cartas, Petia Radeva, & Mariella Dimiccoli. (2021). Modeling long-term interactions to enhance action recognition. In 25th International Conference on Pattern Recognition (pp. 10351–10358).
Abstract: In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information
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Petia Radeva, J. Guerrero, & C. Molina. (1998). A Physics-Based Kohonen Ring..
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