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Jaume Gibert, Ernest Valveny, & Horst Bunke. (2010). Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis. In 15th Iberoamerican Congress on Pattern Recognition (Vol. 6419, 30–37). LNCS.
Abstract: Structure-Activity relationship analysis aims at discovering chemical activity of molecular compounds based on their structure. In this article we make use of a particular graph representation of molecules and propose a new graph embedding procedure to solve the problem of structure-activity relationship analysis. The embedding is essentially an arrangement of a molecule in the form of a vector by considering frequencies of appearing atoms and frequencies of covalent bonds between them. Results on two benchmark databases show the effectiveness of the proposed technique in terms of recognition accuracy while avoiding high operational costs in the transformation.
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Jaume Gibert, & Ernest Valveny. (2010). Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. In I. Ulusoy and F. Escolano T. Windeatt R. C. W. In E.R. Hancock (Ed.), 13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition (Vol. 6218, 223–232). LNCS. Springer Berlin Heidelberg.
Abstract: Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster.
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Jose Manuel Alvarez, Felipe Lumbreras, Theo Gevers, & Antonio Lopez. (2010). Geographic Information for vision-based Road Detection. In IEEE Intelligent Vehicles Symposium (621–626).
Abstract: Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.
Keywords: road detection
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Mathieu Nicolas Delalandre, Ernest Valveny, Tony Pridmore, & Dimosthenis Karatzas. (2010). Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems. IJDAR - International Journal on Document Analysis and Recognition, 13(3), 187–207.
Abstract: This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.
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Miquel Ferrer, Ernest Valveny, F. Serratosa, K. Riesen, & Horst Bunke. (2010). Generalized Median Graph Computation by Means of Graph Embedding in Vector Spaces. PR - Pattern Recognition, 43(4), 1642–1655.
Abstract: The median graph has been presented as a useful tool to represent a set of graphs. Nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data. In this paper we propose a new approach for the computation of the median graph based on graph embedding. Graphs are embedded into a vector space and the median is computed in the vector domain. We have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph. Experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. These experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed.
Keywords: Graph matching; Weighted mean of graphs; Median graph; Graph embedding; Vector spaces
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Arjan Gijsenij, Theo Gevers, & Joost Van de Weijer. (2010). Generalized Gamut Mapping using Image Derivative Structures for Color Constancy. IJCV - International Journal of Computer Vision, 86(2-3), 127–139.
Abstract: The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut
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Francesco Ciompi, Oriol Pujol, Carlo Gatta, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2010). Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization. IJCI - International Journal of Cardiovascular Imaging, 26(7), 763–779.
Abstract: Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect.
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Ivan Huerta. (2010). Foreground Object Segmentation and Shadow Detection for Video Sequences in Uncontrolled Environments (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: This Thesis is mainly divided in two parts. The first one presents a study of motion
segmentation problems. Based on this study, a novel algorithm for mobile-object
segmentation from a static background scene is also presented. This approach is
demonstrated robust and accurate under most of the common problems in motion
segmentation. The second one tackles the problem of shadows in depth. Firstly, a
bottom-up approach based on a chromatic shadow detector is presented to deal with
umbra shadows. Secondly, a top-down approach based on a tracking system has been
developed in order to enhance the chromatic shadow detection.
In our first contribution, a case analysis of motion segmentation problems is presented by taking into account the problems associated with different cues, namely
colour, edge and intensity. Our second contribution is a hybrid architecture which
handles the main problems observed in such a case analysis, by fusing (i) the knowledge from these three cues and (ii) a temporal difference algorithm. On the one hand,
we enhance the colour and edge models to solve both global/local illumination changes
(shadows and highlights) and camouflage in intensity. In addition, local information is
exploited to cope with a very challenging problem such as the camouflage in chroma.
On the other hand, the intensity cue is also applied when colour and edge cues are not
available, such as when beyond the dynamic range. Additionally, temporal difference
is included to segment motion when these three cues are not available, such as that
background not visible during the training period. Lastly, the approach is enhanced
for allowing ghost detection. As a result, our approach obtains very accurate and robust motion segmentation in both indoor and outdoor scenarios, as quantitatively and
qualitatively demonstrated in the experimental results, by comparing our approach
with most best-known state-of-the-art approaches.
Motion Segmentation has to deal with shadows to avoid distortions when detecting
moving objects. Most segmentation approaches dealing with shadow detection are
typically restricted to penumbra shadows. Therefore, such techniques cannot cope
well with umbra shadows. Consequently, umbra shadows are usually detected as part
of moving objects.
Firstly, a bottom-up approach for detection and removal of chromatic moving
shadows in surveillance scenarios is proposed. Secondly, a top-down approach based
on kalman filters to detect and track shadows has been developed in order to enhance
the chromatic shadow detection. In the Bottom-up part, the shadow detection approach applies a novel technique based on gradient and colour models for separating
chromatic moving shadows from moving objects.
Well-known colour and gradient models are extended and improved into an invariant colour cone model and an invariant gradient model, respectively, to perform
automatic segmentation while detecting potential shadows. Hereafter, the regions corresponding to potential shadows are grouped by considering ”a bluish effect” and an
edge partitioning. Lastly, (i) temporal similarities between local gradient structures
and (ii) spatial similarities between chrominance angle and brightness distortions are
analysed for all potential shadow regions in order to finally identify umbra shadows.
In the top-down process, after detection of objects and shadows both are tracked
using Kalman filters, in order to enhance the chromatic shadow detection, when it
fails to detect a shadow. Firstly, this implies a data association between the blobs
(foreground and shadow) and Kalman filters. Secondly, an event analysis of the different data association cases is performed, and occlusion handling is managed by a
Probabilistic Appearance Model (PAM). Based on this association, temporal consistency is looked for the association between foregrounds and shadows and their
respective Kalman Filters. From this association several cases are studied, as a result
lost chromatic shadows are correctly detected. Finally, the tracking results are used
as feedback to improve the shadow and object detection.
Unlike other approaches, our method does not make any a-priori assumptions
about camera location, surface geometries, surface textures, shapes and types of
shadows, objects, and background. Experimental results show the performance and
accuracy of our approach in different shadowed materials and illumination conditions.
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Sergio Vera. (2010). Finger joint modelling from hand X-ray images for assessing rheumatoid arthritis (Vol. 164). Master's thesis, , Bellaterra 01893, Barcelona, Spain.
Abstract: Rheumatoid arthritis is an autoimmune, systemic, inflammatory disorder that mainly af- fects bone joints. While there is no cure for this disease, continuous advances on palliative treatments require frequent verification of patient’s illness evolution. Such evolution is mea- sured through several available semi-quantitative methods that require evaluation of hand and foot X-ray images. Accurate assessment is a time consuming task that requires highly trained personnel. This hinders a generalized use in clinical practice for early diagnose and disease follow-up. In the context of the automatization of such evaluation methods we present a method for detection and characterization of finger joints in hand radiography images. Several measures for assessing the reduction of joint space width are proposed. We compare for the first time such measures to the Van der Heijde score, the gold standard method for rheumatoid arthritis assessment. The proposed method outperforms existing strategies with a detection rate above 95%. Our comparison to Van der Heijde index shows a promising correlation that encourages further research.
Keywords: Rheumatoid arthritis; joint detection; X-ray; Van der Heijde score
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Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2010). Feature Detectors and Feature Descriptors: Where We Are Now (Vol. 154).
Abstract: Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.
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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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David Aldavert, Arnau Ramisa, Ramon Lopez de Mantaras, & Ricardo Toledo. (2010). Fast and Robust Object Segmentation with the Integral Linear Classifier. In 23rd IEEE Conference on Computer Vision and Pattern Recognition (1046–1053).
Abstract: We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.
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Herve Locteau, Sebastien Mace, Ernest Valveny, & Salvatore Tabbone. (2010). Extraction des pieces de un plan de habitation. In Colloque Internacional Francophone de l´Ecrit et le Document (1–12).
Abstract: In this article, a method to extract the rooms of an architectural floor plan image is described. We first present a line detection algorithm to extract long lines in the image. Those lines are analyzed to identify the existing walls. From this point, room extraction can be seen as a classical segmentation task for which each region corresponds to a room. The chosen resolution strategy consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines can also be rough. Thus, we take advantage of knowledge associated to architectural floor plans in order to obtain mainly rectangular rooms. Preliminary tests on a set of real documents show promising results.
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Koen E.A. van de Sande, Theo Gevers, & C.G.M. Snoek. (2010). Evaluating Color Descriptors for Object and Scene Recognition. TPAMI - IEEE Transaction on Pattern Analysis and Machine Intelligence, 32(9), 1582–1596.
Abstract: Impact factor: 5.308
Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.
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Simone Balocco, O. Basset, G. Courbebaisse, E. Boni, Alejandro F. Frangi, P. Tortoli, et al. (2010). Estimation Of Viscoelastic Properties Of Vessel Walls Using a Computational Model and Doppler Ultrasound. PMB - Physics in Medicine and Biology, 55(12), 3557–3575.
Abstract: Human arteries affected by atherosclerosis are characterized by altered wall viscoelastic properties. The possibility of noninvasively assessing arterial viscoelasticity in vivo would significantly contribute to the early diagnosis and prevention of this disease. This paper presents a noniterative technique to estimate the viscoelastic parameters of a vascular wall Zener model. The approach requires the simultaneous measurement of flow variations and wall displacements, which can be provided by suitable ultrasound Doppler instruments. Viscoelastic parameters are estimated by fitting the theoretical constitutive equations to the experimental measurements using an ARMA parameter approach. The accuracy and sensitivity of the proposed method are tested using reference data generated by numerical simulations of arterial pulsation in which the physiological conditions and the viscoelastic parameters of the model can be suitably varied. The estimated values quantitatively agree with the reference values, showing that the only parameter affected by changing the physiological conditions is viscosity, whose relative error was about 27% even when a poor signal-to-noise ratio is simulated. Finally, the feasibility of the method is illustrated through three measurements made at different flow regimes on a cylindrical vessel phantom, yielding a parameter mean estimation error of 25%.
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