Laura Igual, Joan Carles Soliva, Antonio Hernandez, Sergio Escalera, Oscar Vilarroya, & Petia Radeva. (2012). Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder. In High Performance Computing and Simulation, International Conference on (pp. 182–187). IEEE Xplore.
Abstract: This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation.
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Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2012). Error Analysis for Lucas-Kanade Based Schemes. In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 184–191). LNCS. Springer-Verlag Berlin Heidelberg.
Abstract: Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Keywords: Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
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Aura Hernandez-Sabate, & Debora Gil. (2012). The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries. In Yasuhiro Honda (Ed.), Intravascular Ultrasound (pp. 185–206). Intech.
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Arnau Ramisa, David Aldavert, Shrihari Vasudevan, Ricardo Toledo, & Ramon Lopez de Mantaras. (2012). Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot. JIRC - Journal of Intelligent and Robotic Systems, 68(2), 185–208.
Abstract: This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.
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Wenjuan Gong, Jordi Gonzalez, Joao Manuel R. S. Taveres, & Xavier Roca. (2012). A New Image Dataset on Human Interactions. In 7th Conference on Articulated Motion and Deformable Objects (Vol. 7378, pp. 204–209). Springer Berlin Heidelberg.
Abstract: This article describes a new collection of still image dataset which are dedicated to interactions between people. Human action recognition from still images have been a hot topic recently, but most of them are actions performed by a single person, like running, walking, riding bikes, phoning and so on and there is no interactions between people in one image. The dataset collected in this paper are concentrating on human interaction between two people aiming to explore this new topic in the research area of action recognition from still images.
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Bogdan Raducanu, & D. Gatica-Perez. (2012). Inferring competitive role patterns in reality TV show through nonverbal analysis. MTAP - Multimedia Tools and Applications, 56(1), 207–226.
Abstract: This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority.
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Olivier Penacchio, Laura Dempere-Marco, & Xavier Otazu. (2012). Switching off brightness induction through induction-reversed images. In Perception (Vol. 41, 208).
Abstract: Brightness induction is the modulation of the perceived intensity of an
area by the luminance of surrounding areas. Although V1 is traditionally regarded as
an area mostly responsive to retinal information, neurophysiological evidence
suggests that it may explicitly represent brightness information. In this work, we
investigate possible neural mechanisms underlying brightness induction. To this end,
we consider the model by Z Li (1999 Computation and Neural Systems10187-212)
which is constrained by neurophysiological data and focuses on the part of V1
responsible for contextual influences. This model, which has proven to account for
phenomena such as contour detection and preattentive segmentation, shares with
brightness induction the relevant effect of contextual influences. Importantly, the
input to our network model derives from a complete multiscale and multiorientation
wavelet decomposition, which makes it possible to recover an image reflecting the
perceived luminance and successfully accounts for well known psychophysical
effects for both static and dynamic contexts. By further considering inverse problem
techniques we define induction-reversed images: given a target image, we build an
image whose perceived luminance matches the actual luminance of the original
stimulus, thus effectively canceling out brightness induction effects. We suggest that
induction-reversed images may help remove undesired perceptual effects and can
find potential applications in fields such as radiological image interpretation
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David Masip, Alexander Todorov, & Jordi Vitria. (2012). The Role of Facial Regions in Evaluating Social Dime. In Rita Cucchiara V. M. Andrea Fusiello (Ed.), 12th European Conference on Computer Vision – Workshops and Demonstrations (Vol. 7584, pp. 210–219). LNCS. Springer Berlin Heidelberg.
Abstract: Facial trait judgments are an important information cue for people. Recent works in the Psychology field have stated the basis of face evaluation, defining a set of traits that we evaluate from faces (e.g. dominance, trustworthiness, aggressiveness, attractiveness, threatening or intelligence among others). We rapidly infer information from others faces, usually after a short period of time (< 1000ms) we perceive a certain degree of dominance or trustworthiness of another person from the face. Although these perceptions are not necessarily accurate, they influence many important social outcomes (such as the results of the elections or the court decisions). This topic has also attracted the attention of Computer Vision scientists, and recently a computational model to automatically predict trait evaluations from faces has been proposed. These systems try to mimic the human perception by means of applying machine learning classifiers to a set of labeled data. In this paper we perform an experimental study on the specific facial features that trigger the social inferences. Using previous results from the literature, we propose to use simple similarity maps to evaluate which regions of the face influence the most the trait inferences. The correlation analysis is performed using only appearance, and the results from the experiments suggest that each trait is correlated with specific facial characteristics.
Keywords: Workshops and Demonstrations
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Laura Igual, Joan Carles Soliva, Roger Gimeno, Sergio Escalera, Oscar Vilarroya, & Petia Radeva. (2012). Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention Deficit Hyperactivity Disorder. In 9th International Conference on Image Analysis and Recognition (Vol. 7325, pp. 222–229). LNCS.
Abstract: Poster
Studies on volumetric brain Magnetic Resonance Imaging (MRI) showed neuroanatomical abnormalities in pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). In particular, the diminished right caudate volume is one of the most replicated findings among ADHD samples in morphometric MRI studies. In this paper, we propose a fully-automatic method for internal caudate nucleus segmentation based on machine learning. Moreover, the ratio between right caudate body volume and the bilateral caudate body volume is applied in a ADHD diagnostic test. We separately validate the automatic internal segmentation of caudate in head and body structures and the diagnostic test using real data from ADHD and control subjects. As a result, we show accurate internal caudate segmentation and similar performance among the proposed automatic diagnostic test and the manual annotation.
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Carolina Malagelada, F.De Lorio, Santiago Segui, S. Mendez, Michal Drozdzal, Jordi Vitria, et al. (2012). Functional gut disorders or disordered gut function? Small bowel dysmotility evidenced by an original technique. NEUMOT - Neurogastroenterology & Motility, 24(3), 223–230.
Abstract: JCR Impact Factor 2010: 3.349
Background This study aimed to determine the proportion of cases with abnormal intestinal motility among patients with functional bowel disorders. To this end, we applied an original method, previously developed in our laboratory, for analysis of endoluminal images obtained by capsule endoscopy. This novel technology is based on computer vision and machine learning techniques.
Methods The endoscopic capsule (Pillcam SB1; Given Imaging, Yokneam, Israel) was administered to 80 patients with functional bowel disorders and 70 healthy subjects. Endoluminal image analysis was performed with a computer vision program developed for the evaluation of contractile events (luminal occlusions and radial wrinkles), non-contractile patterns (open tunnel and smooth wall patterns), type of content (secretions, chyme) and motion of wall and contents. Normality range and discrimination of abnormal cases were established by a machine learning technique. Specifically, an iterative classifier (one-class support vector machine) was applied in a random population of 50 healthy subjects as a training set and the remaining subjects (20 healthy subjects and 80 patients) as a test set.
Key Results The classifier identified as abnormal 29% of patients with functional diseases of the bowel (23 of 80), and as normal 97% of healthy subjects (68 of 70) (P < 0.05 by chi-squared test). Patients identified as abnormal clustered in two groups, which exhibited either a hyper- or a hypodynamic motility pattern. The motor behavior was unrelated to clinical features.
Conclusions & Inferences With appropriate methodology, abnormal intestinal motility can be demonstrated in a significant proportion of patients with functional bowel disorders, implying a pathologic disturbance of gut physiology.
Keywords: capsule endoscopy;computer vision analysis;machine learning technique;small bowel motility
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Sergio Vera, Debora Gil, Agnes Borras, F. Javier Sanchez, Frederic Perez, Marius G. Linguraru, et al. (2012). Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs. In H. Yoshida et al (Ed.), Workshop on Computational and Clinical Applications in Abdominal Imaging (Vol. 7029, 223–230). LNCS. Berlin: Springer Link.
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D
objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial
manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our
method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs,
exploring the use of medial manifolds for the representation of multi-organ relations.
Keywords: medial manifolds, abdomen.
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Simeon Petkov, Adriana Romero, Xavier Carrillo, Petia Radeva, & Carlo Gatta. (2012). Robust and accurate diaphragm border detection in cardiac X-Ray angiographies. In Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges (Vol. 7746, pp. 225–234). LNCS.
Abstract: Workshop STACOM, dins del MICCAI
X-ray angiography is the most common imaging modality employed in the diagnosis of coronary diseases prior to or during a catheter-based intervention. The analysis of the patient X-Ray sequence can provide useful information about the degree of arterial stenosis, the myocardial perfusion and other clinical parameters. If the sequence has been acquired to evaluate the perfusion grade, the opacity due to the diaphragm could potentially hinder any kind of visual inspection and make more difficult a computer aided measurements. In this paper we propose an accurate and robust method to automatically identify the diaphragm border in each frame. Quantitative evaluation on a set of 11 sequences shows that the proposed algorithm outperforms previous methods.
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Karel Paleček, David Geronimo, & Frederic Lerasle. (2012). Pre-attention cues for person detection. In Cognitive Behavioural Systems, COST 2102 International Training School (pp. 225–235). LNCS. Springer Berlin Heidelberg.
Abstract: Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2012). Text line extraction in graphical documents using background and foreground. IJDAR - International Journal on Document Analysis and Recognition, 15(3), 227–241.
Abstract: 0,405 JCR
In graphical documents (e.g., maps, engineering drawings), artistic documents etc., the text lines are annotated in multiple orientations or curvilinear way to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted. In this paper, we propose a novel method to segment such text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. In the proposed scheme, at first, individual components are detected and grouped into character clusters in a hierarchical way using size and positional information. Next, the clusters are extended in two extreme sides to determine potential candidate regions. Finally, with the help of these candidate regions,
individual lines are extracted. The experimental results are presented on different datasets of graphical documents, camera-based warped documents, noisy images containing seals, etc. The results demonstrate that our approach is robust and invariant to size and orientation of the text lines present in
the document.
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Muhammad Muzzamil Luqman, Jean-Yves Ramel, & Josep Llados. (2012). Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 243–253). LNCS. Springer Berlin Heidelberg.
Abstract: Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.
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