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| Author | Laura Igual; Joan Carles Soliva; Sergio Escalera; Roger Gimeno; Oscar Vilarroya; Petia Radeva | ||||
| Title | Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder | Type | Journal Article | ||
| Year | 2012 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 36 | Issue | 8 | Pages | 591-600 |
| Keywords | Automatic caudate segmentation; Attention-Deficit/Hyperactivity Disorder; Diagnostic test; Machine learning; Decision stumps; Dissociated dipoles | ||||
| Abstract | We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. | ||||
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| Notes | OR; HuPBA; MILAB | Approved | no | ||
| Call Number | Admin @ si @ ISE2012 | Serial | 2143 | ||
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| Author | Laura Igual; Agata Lapedriza; Ricard Borras | ||||
| Title | Robust Gait-Based Gender Classification using Depth Cameras | Type | Journal Article | ||
| Year | 2013 | Publication | EURASIP Journal on Advances in Signal Processing | Abbreviated Journal | EURASIPJ |
| Volume | 37 | Issue | 1 | Pages | 72-80 |
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| Abstract | This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section. | ||||
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| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ILB2013 | Serial | 2144 | ||
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| Author | Francesco Ciompi | ||||
| Title | Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| 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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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Petia Radeva;Oriol Pujol | |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Cio2012 | Serial | 2146 | ||
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| Author | Antonio Hernandez; Miguel Reyes; Victor Ponce; Sergio Escalera | ||||
| Title | GrabCut-Based Human Segmentation in Video Sequences | Type | Journal Article | ||
| Year | 2012 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 12 | Issue | 11 | Pages | 15376-15393 |
| Keywords | segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field | ||||
| Abstract | In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology. | ||||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ HRP2012 | Serial | 2147 | ||
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| Author | Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras | ||||
| Title | Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot | Type | Journal Article | ||
| Year | 2012 | Publication | Journal of Intelligent and Robotic Systems | Abbreviated Journal | JIRC |
| Volume | 68 | Issue | 2 | Pages | 185-208 |
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| 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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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
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| ISSN | 0921-0296 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RAV2012 | Serial | 2150 | ||
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| Author | Michal Drozdzal; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Petia Radeva | ||||
| Title | Adaptable image cuts for motility inspection using WCE | Type | Journal Article | ||
| Year | 2013 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 37 | Issue | 1 | Pages | 72-80 |
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| Abstract | The Wireless Capsule Endoscopy (WCE) technology allows the visualization of the whole small intestine tract. Since the capsule is freely moving, mainly by the means of peristalsis, the data acquired during the study gives a lot of information about the intestinal motility. However, due to: (1) huge amount of frames, (2) complex intestinal scene appearance and (3) intestinal dynamics that make difficult the visualization of the small intestine physiological phenomena, the analysis of the WCE data requires computer-aided systems to speed up the analysis. In this paper, we propose an efficient algorithm for building a novel representation of the WCE video data, optimal for motility analysis and inspection. The algorithm transforms the 3D video data into 2D longitudinal view by choosing the most informative, from the intestinal motility point of view, part of each frame. This step maximizes the lumen visibility in its longitudinal extension. The task of finding “the best longitudinal view” has been defined as a cost function optimization problem which global minimum is obtained by using Dynamic Programming. Validation on both synthetic data and WCE data shows that the adaptive longitudinal view is a good alternative to the traditional motility analysis done by video analysis. The proposed novel data representation a new, holistic insight into the small intestine motility, allowing to easily define and analyze motility events that are difficult to spot by analyzing WCE video. Moreover, the visual inspection of small intestine motility is 4 times faster then by means of video skimming of the WCE. | ||||
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| Notes | MILAB; OR; 600.046; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ DSM2012 | Serial | 2151 | ||
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| Author | Petia Radeva; Michal Drozdzal; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
| Title | Active labeling: Application to wireless endoscopy analysis | Type | Conference Article | ||
| Year | 2012 | Publication | High Performance Computing and Simulation, International Conference on | Abbreviated Journal | |
| Volume | Issue | Pages | 174-181 | ||
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| Abstract | Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”. | ||||
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| ISSN | ISBN | 978-1-4673-2359-8 | Medium | ||
| Area | Expedition | Conference | HPCS | ||
| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RDS2012 | Serial | 2152 | ||
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| Author | Cristhian Aguilera; Fernando Barrera; Felipe Lumbreras; Angel Sappa; Ricardo Toledo | ||||
| Title | Multispectral Image Feature Points | Type | Journal Article | ||
| Year | 2012 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 12 | Issue | 9 | Pages | 12661-12672 |
| Keywords | multispectral image descriptor; color and infrared images; feature point descriptor | ||||
| Abstract | Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ABL2012 | Serial | 2154 | ||
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| Author | Fernando Barrera; Felipe Lumbreras; Angel Sappa | ||||
| Title | Multimodal Stereo Vision System: 3D Data Extraction and Algorithm Evaluation | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Journal of Selected Topics in Signal Processing | Abbreviated Journal | J-STSP |
| Volume | 6 | Issue | 5 | Pages | 437-446 |
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| Abstract | This paper proposes an imaging system for computing sparse depth maps from multispectral images. A special stereo head consisting of an infrared and a color camera defines the proposed multimodal acquisition system. The cameras are rigidly attached so that their image planes are parallel. Details about the calibration and image rectification procedure are provided. Sparse disparity maps are obtained by the combined use of mutual information enriched with gradient information. The proposed approach is evaluated using a Receiver Operating Characteristics curve. Furthermore, a multispectral dataset, color and infrared images, together with their corresponding ground truth disparity maps, is generated and used as a test bed. Experimental results in real outdoor scenarios are provided showing its viability and that the proposed approach is not restricted to a specific domain. | ||||
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| ISSN | 1932-4553 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ BLS2012b | Serial | 2155 | ||
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| Author | Cristhian Aguilera; M.Ramos; Angel Sappa | ||||
| Title | Simulated Annealing: A Novel Application of Image Processing in the Wood Area | Type | Book Chapter | ||
| Year | 2012 | Publication | Simulated Annealing – Advances, Applications and Hybridizations | Abbreviated Journal | |
| Volume | Issue | Pages | 91-104 | ||
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| Publisher | Place of Publication | Editor | Marcos de Sales Guerra Tsuzuki | ||
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| ISSN | ISBN | 978-953-51-0710-1 | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ARS2012 | Serial | 2156 | ||
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| Author | Miguel Oliveira; V.Santos; Angel Sappa | ||||
| Title | Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition | Type | Conference Article | ||
| Year | 2012 | Publication | IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles | Abbreviated Journal | |
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| Address | Algarve; Portugal | ||||
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| Area | Expedition | Conference | PPNIV | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ OSS2012c | Serial | 2159 | ||
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| Author | David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich | ||||
| Title | Traffic sign recognition for computer vision project-based learning | Type | Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Education | Abbreviated Journal | T-EDUC |
| Volume | 56 | Issue | 3 | Pages | 364-371 |
| Keywords | traffic signs | ||||
| Abstract | This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. | ||||
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| ISSN | 0018-9359 | ISBN | Medium | ||
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| Notes | ADAS; CIC | Approved | no | ||
| Call Number | Admin @ si @ GSL2013; ADAS @ adas @ | Serial | 2160 | ||
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| Author | Joan Serrat; Felipe Lumbreras; Antonio Lopez | ||||
| Title | Cost estimation of custom hoses from STL files and CAD drawings | Type | Journal Article | ||
| Year | 2013 | Publication | Computers in Industry | Abbreviated Journal | COMPUTIND |
| Volume | 64 | Issue | 3 | Pages | 299-309 |
| Keywords | On-line quotation; STL format; Regression; Gaussian process | ||||
| Abstract | We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | ADAS; 600.057; 600.054; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ SLL2013; ADAS @ adas @ | Serial | 2161 | ||
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| Author | Marina Alberti; Simone Balocco; Xavier Carrillo; Josepa Mauri; Petia Radeva | ||||
| Title | Automatic Non-Rigid Temporal Alignment of IVUS Sequences | Type | Conference Article | ||
| Year | 2012 | Publication | 15th International Conference on Medical Image Computing and Computer Assisted Intervention | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 642-650 | |
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| Abstract | Clinical studies on atherosclerosis regression/progression performed by Intravascular Ultrasound analysis require the alignment of pullbacks of the same patient before and after clinical interventions. In this paper, a methodology for the automatic alignment of IVUS sequences based on the Dynamic Time Warping technique is proposed. The method is adapted to the specific IVUS alignment task by applying the non-rigid alignment technique to multidimensional morphological signals, and by introducing a sliding window approach together with a regularization term. To show the effectiveness of our method, an extensive validation is performed both on synthetic data and in-vivo IVUS sequences. The proposed method is robust to stent deployment and post dilation surgery and reaches an alignment error of approximately 0.7 mm for in-vivo data, which is comparable to the inter-observer variability. | ||||
| Address | Nice, France | ||||
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| Publisher | Springer-Verlag Berlin, Heidelberg | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-3-642-33414-6 | Medium | ||
| Area | Expedition | Conference | MICCAI | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ABC2012 | Serial | 2168 | ||
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| Author | Noha Elfiky | ||||
| Title | Enhancing Local Binary Patterns with Spatial Pyramid Kernel: Application to Scene Classification | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 129 | Issue | Pages | ||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Elf2009 | Serial | 2388 | ||
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