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Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
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Title |
Human Action Recognition Using an Ensemble of Body-Part Detectors |
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2013 |
Publication |
Expert Systems |
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EXSY |
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30 |
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2 |
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101-114 |
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Human action recognition;body-part detection;hidden Markov model |
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This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods. |
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Admin @ si @ CBG2013 |
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1809 |
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Sophie Wuerger; Kaida Xiao; Chenyang Fu; Dimosthenis Karatzas |
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Title |
Colour-opponent mechanisms are not affected by age-related chromatic sensitivity changes |
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2010 |
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Ophthalmic and Physiological Optics |
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OPO |
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30 |
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5 |
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635-659 |
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The purpose of this study was to assess whether age-related chromatic sensitivity changes are associated with corresponding changes in hue perception in a large sample of colour-normal observers over a wide age range (n = 185; age range: 18-75 years). In these observers we determined both the sensitivity along the protan, deutan and tritan line; and settings for the four unique hues, from which the characteristics of the higher-order colour mechanisms can be derived. We found a significant decrease in chromatic sensitivity due to ageing, in particular along the tritan line. From the unique hue settings we derived the cone weightings associated with the colour mechanisms that are at equilibrium for the four unique hues. We found that the relative cone weightings (w(L) /w(M) and w(L) /w(S)) associated with the unique hues were independent of age. Our results are consistent with previous findings that the unique hues are rather constant with age while chromatic sensitivity declines. They also provide evidence in favour of the hypothesis that higher-order colour mechanisms are equipped with flexible cone weightings, as opposed to fixed weights. The mechanism underlying this compensation is still poorly understood. |
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DAG; IF: 1.259 |
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Admin @ si @ WXF2010 |
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1826 |
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Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo |
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Title |
Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes |
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2011 |
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Computer Graphics Forum |
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CGF |
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30 |
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7 |
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2107-2115 |
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IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. |
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MILAB; HuPBA |
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no |
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Admin @ si @ EPA2011 |
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1881 |
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Author |
Noha Elfiky; Jordi Gonzalez; Xavier Roca |
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Title |
Compact and Adaptive Spatial Pyramids for Scene Recognition |
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2012 |
Publication |
Image and Vision Computing |
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IMAVIS |
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30 |
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8 |
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492–500 |
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Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets. |
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ISE |
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Admin @ si @ EGR2012 |
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2004 |
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Author |
Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez |
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Title |
Distributed Learning and Inference with Compressed Images |
Type |
Journal Article |
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Year |
2021 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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30 |
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3069 - 3083 |
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Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task. |
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LAMP; ADAS; 600.120; 600.118 |
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no |
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Admin @ si @ KEH2021 |
Serial |
3543 |
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Author |
Luca Ginanni Corradini; Simone Balocco; Luciano Maresca; Silvio Vitale; Matteo Stefanini |
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Title |
Anatomical Modifications After Stent Implantation: A Comparative Analysis Between CGuard, Wallstent, and Roadsaver Carotid Stents |
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Journal Article |
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Year |
2023 |
Publication |
Journal of Endovascular Therapy |
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Volume |
30 |
Issue |
1 |
Pages |
18-24 |
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Ginanni Corradini L, Balocco S, Maresca L, Vitale S, Stefanini M. |
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Abstract
Purpose:
Carotid revascularization can be associated with modifications of the vascular geometry, which may lead to complications. The changes on the vessel angulation before and after a carotid WallStent (WS) implantation are compared against 2 new dual-layer devices, CGuard (CG) and RoadSaver (RS).
Materials and Methods:
The study prospectively recruited 217 consecutive patients (112 GC, 73 WS, and 32 RS, respectively). Angiography projections were explored and the one having a higher arterial angle was selected as a basal view. After stent implantation, a stent control angiography was performed selecting the projection having the maximal angle. The same procedure is followed in all the 3 stent types to guarantee comparable conditions. The angulation changes on the stented segments were quantified from both angiographies. The statistical analysis quantitatively compared the pre-and post-angles for the 3 stent types. The results are qualitatively illustrated using boxplots. Finally, the relation between pre- and post-angles measurements is analyzed using linear regression.
Results:
For CG, no statistical difference in the axial vessel geometry between the basal and postprocedural angles was found. For WS and RS, statistical difference was found between pre- and post-angles. The regression analysis shows that CG induces lower changes from the original curvature with respect to WS and RS.
Conclusion:
Based on our results, CG determines minor changes over the basal morphology than WS and RS stents. Hence, CG respects better the native vessel anatomy than the other stents.
Level of Evidence: Level 4, Case Series. |
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xxx |
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Admin @ si @ GBM2023 |
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4006 |
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Author |
Marçal Rusiñol; Agnes Borras; Josep Llados |
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Title |
Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images |
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Journal Article |
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2010 |
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Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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31 |
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3 |
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188–201 |
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Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings |
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This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results. |
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DAG |
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DAG @ dag @ RBL2010 |
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1177 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
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Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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Journal Article |
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2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
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9 |
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1630–1644 |
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The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG @ dag @ RVT2009 |
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1220 |
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Oriol Pujol; David Masip |
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Title |
Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary |
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2009 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
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6 |
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1140–1146 |
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This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention |
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OR;HuPBA;MV |
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BCNPCL @ bcnpcl @ PuM2009 |
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1252 |
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Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Unsupervised writer adaptation of whole-word HMMs with application to word-spotting |
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2010 |
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Pattern Recognition Letters |
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PRL |
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31 |
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8 |
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742–749 |
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Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis |
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In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition. |
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DAG @ dag @ RPS2010 |
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1290 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Re-coding ECOCs without retraining |
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2010 |
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Pattern Recognition Letters |
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PRL |
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31 |
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7 |
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555–562 |
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A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations. |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010e |
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1338 |
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Aura Hernandez-Sabate; Debora Gil; Petia Radeva; E.N.Nofrerias |
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Anisotropic processing of image structures for adventitia detection in intravascular ultrasound images |
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2004 |
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Proc. Computers in Cardiology |
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31 |
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229-232 |
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The adventitia layer appears as a weak edge in IVUS images with a non-uniform grey level, which difficulties its detection. In order to enhance edges, we apply an anisotropic filter that homogenizes the grey level along the image significant structures (ridges, valleys and edges). A standard edge detector applied to the filtered image yields a set of candidate points prone to be unconnected. The final model is obtained by interpolating the former line segments along the tangent direction to the level curves of the filtered image with an anisotropic contour closing technique based on functional extension principles |
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Chicago (USA) |
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IAM; MILAB |
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IAM @ iam @ HGR2004 |
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1555 |
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Francisco Javier Orozco; Ognjen Rudovic; Jordi Gonzalez; Maja Pantic |
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Hierarchical On-line Appearance-Based Tracking for 3D Head Pose, Eyebrows, Lips, Eyelids and Irises |
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2013 |
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Image and Vision Computing |
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IMAVIS |
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31 |
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4 |
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322-340 |
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On-line appearance models; Levenberg–Marquardt algorithm; Line-search optimization; 3D face tracking; Facial action tracking; Eyelid tracking; Iris tracking |
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In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can also be used for eyelid and iris tracking, as well as 3D head pose, lips and eyebrows facial actions tracking. Furthermore, our approach applies an on-line learning of changes in the appearance of the tracked target. Hence, the prior training of appearance models, which usually requires a large amount of labeled facial images, is avoided. Moreover, the proposed method is built upon a hierarchical combination of three OABTs, which are optimized using a Levenberg–Marquardt Algorithm (LMA) enhanced with line-search procedures. This, in turn, makes the proposed method robust to changes in lighting conditions, occlusions and translucent textures, as evidenced by our experiments. Finally, the proposed method achieves head and facial actions tracking in real-time. |
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Elsevier |
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ISE; 605.203; 302.012; 302.018; 600.049 |
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ORG2013 |
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2221 |
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G.Blasco; Simone Balocco; J.Puig; J.Sanchez-Gonzalez; W.Ricart; J.Daunis-I-Estadella; X.Molina; S.Pedraza; J.M.Fernandez-Real |
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Carotid pulse wave velocity by magnetic resonance imaging is increased in middle-aged subjects with the metabolic syndrome |
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2015 |
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International Journal of Cardiovascular Imaging |
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ICJI |
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31 |
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3 |
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603-612 |
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Metabolic syndrome; Arterial stiffness; Pulse wave velocity; Carotid artery; Magnetic resonance |
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Arterial pulse wave velocity (PWV), an independent predictor of cardiovascular disease, physiologically increases with age; however, growing evidence suggests metabolic syndrome (MetS) accelerates this increase. Magnetic resonance imaging (MRI) enables reliable noninvasive assessment of arterial stiffness by measuring arterial PWV in specific vascular segments. We investigated the association between the presence of MetS and its components with carotid PWV (cPWV) in asymptomatic subjects without diabetes. We assessed cPWV by MRI in 61 individuals (mean age, 55.3 ± 14.1 years; median age, 55 years): 30 with MetS and 31 controls with similar age, sex, body mass index, and LDL-cholesterol levels. The study population was dichotomized by the median age. To remove the physiological association between PWV and age, unpaired t tests and multiple regression analyses were performed using the residuals of the regression between PWV and age. cPWV was higher in middle-aged subjects with MetS than in those without (p = 0.001), but no differences were found in elder subjects (p = 0.313). cPWV was associated with diastolic blood pressure (r = 0.276, p = 0.033) and waist circumference (r = 0.268, p = 0.038). The presence of MetS was associated with increased cPWV regardless of age, sex, blood pressure, and waist (p = 0.007). The MetS components contributing independently to an increased cPWV were hypertension (p = 0.018) and hypertriglyceridemia (p = 0.002). The presence of MetS is associated with an increased cPWV in middle-aged subjects. In particular, hypertension and hypertriglyceridemia may contribute to early progression of carotid stiffness. |
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Springer Netherlands |
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1569-5794 |
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MILAB |
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Admin @ si @ BBP2015 |
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2670 |
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Rada Deeb; Joost Van de Weijer; Damien Muselet; Mathieu Hebert; Alain Tremeau |
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Deep spectral reflectance and illuminant estimation from self-interreflections |
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2019 |
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Journal of the Optical Society of America A |
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JOSA A |
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31 |
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1 |
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105-114 |
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In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches. |
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LAMP; 600.120 |
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Admin @ si @ DWM2019 |
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3362 |
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