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Victor Borjas, Jordi Vitria, & Petia Radeva. (2013). Gradient Histogram Background Modeling for People Detection in Stationary Camera Environments. In 13th IAPR Conference on Machine Vision Applications.
Abstract: Best Poster AwardOne of the big challenges of today person detectors is the decreasing of the false positive rate. In this paper, we propose a novel framework to customize person detectors in static camera scenarios in order to reduce this rate. This scheme includes background modeling for subtraction based on gradient histograms and Mean-Shift clustering. Our experiments show that the detection improved compared to using only the output from the pedestrian detector reducing 87% of the false positives and therefore the overall precision of the detection
was increased signicantly.
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Antonio Hernandez, Miguel Angel Bautista, Xavier Perez Sala, Victor Ponce, Xavier Baro, Oriol Pujol, et al. (2012). BoVDW: Bag-of-Visual-and-Depth-Words for Gesture Recognition. In 21st International Conference on Pattern Recognition.
Abstract: We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition pipeline, where Dynamic Time Warping (DTW) algorithm is used to perform prior segmentation of gestures. Results of the method in public data sets, within our gesture recognition pipeline, show better performance in comparison to a standard BoVW model.
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Carles Sanchez, Debora Gil, Antoni Rosell, Albert Andaluz, & F. Javier Sanchez. (2013). Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance. In Sebastiano Battiato and José Braz (Ed.), Proceedings of the International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 153–161). LNCS. Portugal: SciTePress.
Abstract: Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability
Keywords: Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model
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Anjan Dutta, Jaume Gibert, Josep Llados, Horst Bunke, & Umapada Pal. (2012). Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents. In 21st International Conference on Pattern Recognition (pp. 1663–1666).
Abstract: This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2012). Noise suppression over bi-level graphical documents using a sparse representation. In Colloque International Francophone sur l'Écrit et le Document.
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Adriana Romero, Simeon Petkov, Carlo Gatta, M.Sabate, & Petia Radeva. (2012). Efficient automatic segmentation of vessels. In 16th Conference on Medical Image Understanding and Analysis.
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Francesco Ciompi. (2012). Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound (Petia Radeva, & Oriol Pujol, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: In this thesis we tackle the problem of automatic characterization of human coronary vessel in Intravascular Ultrasound (IVUS) image modality. The basis for the whole characterization process is machine learning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes (ECOC) framework is used as central element for the design of multi-class classifiers.
Two main topics are tackled in this thesis. First, the automatic detection of the vessel borders is presented. For this purpose, a novel context-aware classifier for multi-class classification of the vessel morphology is presented, namely ECOC-DRF. Based on ECOC-DRF, the lumen border and the media-adventitia border in IVUS are robustly detected by means of a novel holistic approach, achieving an error comparable with inter-observer variability and with state of the art methods.
The two vessel borders define the atheroma area of the vessel. In this area, tissue characterization is required. For this purpose, we present a framework for automatic plaque characterization by processing both texture in IVUS images and spectral information in raw Radio Frequency data. Furthermore, a novel method for fusing in-vivo and in-vitro IVUS data for plaque characterization is presented, namely pSFFS. The method demonstrates to effectively fuse data generating a classifier that improves the tissue characterization in both in-vitro and in-vivo datasets.
A novel method for automatic video summarization in IVUS sequences is also presented. The method aims to detect the key frames of the sequence, i.e., the frames representative of morphological changes. This novel method represents the basis for video summarization in IVUS as well as the markers for the partition of the vessel into morphological and clinically interesting events.
Finally, multi-class learning based on ECOC is applied to lung tissue characterization in Computed Tomography. The novel proposed approach, based on supervised and unsupervised learning, achieves accurate tissue classification on a large and heterogeneous dataset.
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Joan Arnedo-Moreno, & Agata Lapedriza. (2010). Visualizing key authenticity: turning your face into your public key. In 6th China International Conference on Information Security and Cryptology (pp. 605–618). LNCS.
Abstract: Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process.
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Miguel Oliveira, V.Santos, & Angel Sappa. (2012). Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition. In IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles.
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Noha Elfiky. (2009). Enhancing Local Binary Patterns with Spatial Pyramid Kernel: Application to Scene Classification (Vol. 129). Master's thesis, , Bellaterra, Barcelona.
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Onur Ferhat. (2012). Eye-Tracking with Webcam-Based Setups: Implementation of a Real-Time System and an Analysis of Factors Affecting Performance (Fernando Vilariño, Ed.) (Vol. 172). Master's thesis, , .
Abstract: In the recent years commercial eye-tracking hardware has become more common, with the introduction of new models from several brands that have better performance and easier setup procedures. A cause and at the same time a result of this phenomenon is the popularity of eye-tracking research directed at marketing, accessibility and usability, among others.
One problem with these hardware components is scalability, because both the price and the necessary expertise to operate them makes it practically impossible in the large scale. In this work, we analyze the feasibility of a software eye-tracking system based on a single, ordinary webcam. Our aim is to discover the limits of such a system and to see whether it provides acceptable performances.
The significance of this setup is that it is the most common setup found in consumer environments, off-the-shelf electronic devices such as laptops, mobile phones and tablet computers. As no special equipment such as infrared lights, mirrors or zoom lenses are used; setting up and calibrating the system is easier compared to other approaches using these components.
Our work is based on the open source application Opengazer, which provides a good starting point for our contributions. We propose several improvements in order to push the system's performance further and make it feasible as a robust, real-time device. Then we carry out an elaborate experiment involving 18 human subjects and 4 different system setups. Finally, we give an analysis of the results and discuss the effects of setup changes, subject differences and modifications in the software.
Keywords: Computer vision, eye-tracking, gaussian process, feature selection, optical flow
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Pedro Martins, Paulo Carvalho, & Carlo Gatta. (2012). Stable Salient Shapes. In International Conference on Digital Image Computing: Techniques and Applications.
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Rui Hua, Oriol Pujol, Francesco Ciompi, Marina Alberti, Simone Balocco, Josepa Mauri, et al. (2012). Stent Strut Detection by Classifying a Wide Set of IVUS Features. In Computed Assisted Stenting Workshop.
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Jaime Moreno, & Xavier Otazu. (2011). Image coder based on Hilbert scanning of embedded quadTrees. In Data Compression Conference (p. 470).
Abstract: In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels.
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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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