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| Author | Santiago Segui | ||||
| Title | Contributions to the Diagnosis of Intestinal Motility by Automatic Image Analysis | Type | Book Whole | ||
| Year | 2011 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In the early twenty first century Given Imaging Ltd. presented wireless capsule endoscopy (WCE) as a new technological breakthrough that allowed the visualization of
the intestine by using a small, swallowed camera. This small size device was received with a high enthusiasm within the medical community, and until now, it is still one of the medical devices with the highest use growth rate. WCE can be used as a novel diagnostic tool that presents several clinical advantages, since it is non-invasive and at the same time it provides, for the first time, a full picture of the small bowel morphology, contents and dynamics. Since its appearance, the WCE has been used to detect several intestinal dysfunctions such as: polyps, ulcers and bleeding. However, the visual analysis of WCE videos presents an important drawback: the long time required by the physicians for proper video visualization. In this sense and regarding to this limitation, the development of computer aided systems is required for the extensive use of WCE in the medical community. The work presented in this thesis is a set of contributions for the automatic image analysis and computer-aided diagnosis of intestinal motility disorders using WCE. Until now, the diagnosis of small bowel motility dysfunctions was basically performed by invasive techniques such as the manometry test, which can only be conducted at some referral centers around the world owing to the complexity of the procedure and the medial expertise required in the interpretation of the results. Our contributions are divided in three main blocks: 1. Image analysis by computer vision techniques to detect events in the endoluminal WCE scene. Several methods have been proposed to detect visual events such as: intestinal contractions, intestinal content, tunnel and wrinkles; 2. Machine learning techniques for the analysis and the manipulation of the data from WCE. These methods have been proposed in order to overcome the problems that the analysis of WCE presents such as: video acquisition cost, unlabeled data and large number of data; 3. Two different systems for the computer-aided diagnosis of intestinal motility disorders using WCE. The first system presents a fully automatic method that aids at discriminating healthy subjects from patients with severe intestinal motor disorders like pseudo-obstruction or food intolerance. The second system presents another automatic method that models healthy subjects and discriminate them from mild intestinal motility patients. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Vitria | |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Seg2011 | Serial | 1836 | ||
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| Author | Pierluigi Casale | ||||
| Title | Approximate Ensemble Methods for Physical Activity Recognition Applications | Type | Book Whole | ||
| Year | 2011 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The main interest of this thesis focuses on computational methodologies able to
reduce the degree of complexity of learning algorithms and its application to physical activity recognition. Random Projections will be used to reduce the computational complexity in Multiple Classifier Systems. A new boosting algorithm and a new one-class classification methodology have been developed. In both cases, random projections are used for reducing the dimensionality of the problem and for generating diversity, exploiting in this way the benefits that ensembles of classifiers provide in terms of performances and stability. Moreover, the new one-class classification methodology, based on an ensemble strategy able to approximate a multidimensional convex-hull, has been proved to over-perform state-of-the-art one-class classification methodologies. The practical focus of the thesis is towards Physical Activity Recognition. A new hardware platform for wearable computing application has been developed and used for collecting data of activities of daily living allowing to study the optimal features set able to successful classify activities. Based on the classification methodologies developed and the study conducted on physical activity classification, a machine learning architecture capable to provide a continuous authentication mechanism for mobile-devices users has been worked out, as last part of the thesis. The system, based on a personalized classifier, states on the analysis of the characteristic gait patterns typical of each individual ensuring an unobtrusive and continuous authentication mechanism |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Oriol Pujol;Petia Radeva | |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Cas2011 | Serial | 1837 | ||
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| Author | Fahad Shahbaz Khan | ||||
| Title | Coloring bag-of-words based image representations | Type | Book Whole | ||
| Year | 2011 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Put succinctly, the bag-of-words based image representation is the most successful approach for object and scene recognition. Within the bag-of-words framework the optimal fusion of multiple cues, such as shape, texture and color, still remains an active research domain. There exist two main approaches to combine color and shape information within the bag-of-words framework. The first approach called, early fusion, fuses color and shape at the feature level as a result of which a joint colorshape vocabulary is produced. The second approach, called late fusion, concatenates histogram representation of both color and shape, obtained independently. In the first part of this thesis, we analyze the theoretical implications of both early and late feature fusion. We demonstrate that both these approaches are suboptimal for a subset of object categories. Consequently, we propose a novel method for recognizing object categories when using multiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom-up and top-down attention maps. Subsequently, the color attention maps are used to modulate the weights of the shape features. Shape features are given more weight in regions with higher attention and vice versa. The approach is tested on several benchmark object recognition data sets and the results clearly demonstrate the effectiveness of our proposed method. In the second part of the thesis, we investigate the problem of obtaining compact spatial pyramid representations for object and scene recognition. Spatial pyramids have been successfully applied to incorporate spatial information into bag-of-words based image representation. However, a major drawback of spatial pyramids is that it leads to high dimensional image representations. We present a novel framework for obtaining compact pyramid representation. The approach reduces the size of a high dimensional pyramid representation upto an order of magnitude without any significant reduction in accuracy. Moreover, we also investigate the optimal combination of multiple features such as color and shape within the context of our compact pyramid representation. Finally, we describe a novel technique to build discriminative visual words from multiple cues learned independently from training images. To this end, we use an information theoretic vocabulary compression technique to find discriminative combinations of visual cues and the resulting visual vocabulary is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. The approach is tested on standard object recognition data sets. The results obtained clearly demonstrate the effectiveness of our approach. | ||||
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Joost Van de Weijer;Maria Vanrell | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Kha2011 | Serial | 1838 | ||
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| Author | Jorge Bernal; David Vazquez (eds) | ||||
| Title | Computer vision Trends and Challenges | Type | Book Whole | ||
| Year | 2013 | Publication | Computer vision Trends and Challenges | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | CVCRD; Computer Vision | ||||
| Abstract | This book contains the papers presented at the Eighth CVC Workshop on Computer Vision Trends and Challenges (CVCR&D'2013). The workshop was held at the Computer Vision Center (Universitat Autònoma de Barcelona), the October 25th, 2013. The CVC workshops provide an excellent opportunity for young researchers and project engineers to share new ideas and knowledge about the progress of their work, and also, to discuss about challenges and future perspectives. In addition, the workshop is the welcome event for new people that recently have joined the institute.
The program of CVCR&D is organized in a single-track single-day workshop. It comprises several sessions dedicated to specific topics. For each session, a doctor working on the topic introduces the general research lines. The PhD students expose their specific research. A poster session will be held for open questions. Session topics cover the current research lines and development projects of the CVC: Medical Imaging, Medical Imaging, Color & Texture Analysis, Object Recognition, Image Sequence Evaluation, Advanced Driver Assistance Systems, Machine Vision, Document Analysis, Pattern Recognition and Applications. We want to thank all paper authors and Program Committee members. Their contribution shows that the CVC has a dynamic, active, and promising scientific community. We hope you all enjoy this Eighth workshop and we are looking forward to meeting you and new people next year in the Ninth CVCR&D. |
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| Publisher | Place of Publication | Editor | Jorge Bernal; David Vazquez | ||
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| ISSN | ISBN | 978-84-940902-2-6 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | ADAS @ adas @ BeV2013 | Serial | 2339 | ||
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| Author | David Geronimo; Antonio Lopez | ||||
| Title | Vision-based Pedestrian Protection Systems for Intelligent Vehicles | Type | Book Whole | ||
| Year | 2014 | Publication | SpringerBriefs in Computer Science | Abbreviated Journal | |
| Volume | Issue | Pages | 1-114 | ||
| Keywords | Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users | ||||
| Abstract | Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented. | ||||
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| Publisher | Springer Briefs in Computer Vision | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-1-4614-7986-4 | Medium | ||
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| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | GeL2014 | Serial | 2325 | ||
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| Author | Muhammad Anwer Rao | ||||
| Title | Color for Object Detection and Action Recognition | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Recognizing object categories in real world images is a challenging problem in computer vision. The deformable part based framework is currently the most successful approach for object detection. Generally, HOG are used for image representation within the part-based framework. For action recognition, the bag-of-word framework has shown to provide promising results. Within the bag-of-words framework, local image patches are described by SIFT descriptor. Contrary to object detection and action recognition, combining color and shape has shown to provide the best performance for object and scene recognition.
In the first part of this thesis, we analyze the problem of person detection in still images. Standard person detection approaches rely on intensity based features for image representation while ignoring the color. Channel based descriptors is one of the most commonly used approaches in object recognition. This inspires us to evaluate incorporating color information using the channel based fusion approach for the task of person detection. In the second part of the thesis, we investigate the problem of object detection in still images. Due to high dimensionality, channel based fusion increases the computational cost. Moreover, channel based fusion has been found to obtain inferior results for object category where one of the visual varies significantly. On the other hand, late fusion is known to provide improved results for a wide range of object categories. A consequence of late fusion strategy is the need of a pure color descriptor. Therefore, we propose to use Color attributes as an explicit color representation for object detection. Color attributes are compact and computationally efficient. Consequently color attributes are combined with traditional shape features providing excellent results for object detection task. Finally, we focus on the problem of action detection and classification in still images. We investigate the potential of color for action classification and detection in still images. We also evaluate different fusion approaches for combining color and shape information for action recognition. Additionally, an analysis is performed to validate the contribution of color for action recognition. Our results clearly demonstrate that combining color and shape information significantly improve the performance of both action classification and detection in still images. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Joost Van de Weijer | |
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Rao2013 | Serial | 2281 | ||
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| Author | Javier Marin | ||||
| Title | Pedestrian Detection Based on Local Experts | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | During the last decade vision-based human detection systems have started to play a key rolein multiple applications linked to driver assistance, surveillance, robot sensing and home automation.
Detecting humans is by far one of the most challenging tasks in Computer Vision. This is mainly due to the high degree of variability in the human appearanceassociated to the clothing, pose, shape and size. Besides, other factors such as cluttered scenarios, partial occlusions, or environmental conditions can make the detection task even harder. Most promising methods of the state-of-the-art rely on discriminative learning paradigms which are fed with positive and negative examples. The training data is one of the most relevant elements in order to build a robust detector as it has to cope the large variability of the target. In order to create this dataset human supervision is required. The drawback at this point is the arduous effort of annotating as well as looking for such claimed variability. In this PhD thesis we address two recurrent problems in the literature. In the first stage,we aim to reduce the consuming task of annotating, namely, by using computer graphics. More concretely, we develop a virtual urban scenario for later generating a pedestrian dataset. Then, we train a detector using this dataset, and finally we assess if this detector can be successfully applied in a real scenario. In the second stage, we focus on increasing the robustness of our pedestrian detectors under partial occlusions. In particular, we present a novel occlusion handling approach to increase the performance of block-based holistic methods under partial occlusions. For this purpose, we make use of local experts via a RandomSubspaceMethod (RSM) to handle these cases. If the method infers a possible partial occlusion, then the RSM, based on performance statistics obtained from partially occluded data, is applied. The last objective of this thesis is to propose a robust pedestrian detector based on an ensemble of local experts. To achieve this goal, we use the random forest paradigm, where the trees act as ensembles an their nodesare the local experts. In particular, each expert focus on performing a robust classification ofa pedestrian body patch. This approach offers computational efficiency and far less design complexity when compared to other state-of-the-artmethods, while reaching better accuracy |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Jaume Amores | |
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Mar2013 | Serial | 2280 | ||
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| Author | Wenjuan Gong | ||||
| Title | 3D Motion Data aided Human Action Recognition and Pose Estimation | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In this work, we explore human action recognition and pose estimation prob-
lems. Different from traditional works of learning from 2D images or video sequences and their annotated output, we seek to solve the problems with ad- ditional 3D motion capture information, which helps to fill the gap between 2D image features and human interpretations. We first compare two different schools of approaches commonly used for 3D pose estimation from 2D pose configuration: modeling and learning methods. By looking into experiments results and considering our problems, we fixed a learning method as the following approaches to do pose estimation. We then establish a framework by adding a module of detecting 2D pose configuration from images with varied background, which widely extend the application of the approach. We also seek to directly estimate 3D poses from image features, instead of estimating 2D poses as a intermediate module. We explore a robust input feature, which combined with the proposed distance measure, provides a solution for noisy or corrupted inputs. We further utilize the above method to estimate weak poses,which is a concise representation of the original poses by using dimension deduction technologies, from image features. Weak pose space is where we calculate vocabulary and label action types using a bog of words pipeline. Temporal information of an action is taken into consideration by considering several consecutive frames as a single unit for computing vocabulary and histogram assignments. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Gon2013 | Serial | 2279 | ||
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| Author | Murad Al Haj | ||||
| Title | Looking at Faces: Detection, Tracking and Pose Estimation | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Humans can effortlessly perceive faces, follow them over space and time, and decode their rich content, such as pose, identity and expression. However, despite many decades of research on automatic facial perception in areas like face detection, expression recognition, pose estimation and face recognition, and despite many successes, a complete solution remains elusive. This thesis is dedicated to three problems in automatic face perception, namely face detection, face tracking and pose estimation.
In face detection, an initial simple model is presented that uses pixel-based heuristics to segment skin locations and hand-crafted rules to determine the locations of the faces present in an image. Different colorspaces are studied to judge whether a colorspace transformation can aid skin color detection. The output of this study is used in the design of a more complex face detector that is able to successfully generalize to different scenarios. In face tracking, a framework that combines estimation and control in a joint scheme is presented to track a face with a single pan-tilt-zoom camera. While this work is mainly motivated by tracking faces, it can be easily applied atop of any detector to track different objects. The applicability of this method is demonstrated on simulated as well as real-life scenarios. The last and most important part of this thesis is dedicate to monocular head pose estimation. In this part, a method based on partial least squares (PLS) regression is proposed to estimate pose and solve the alignment problem simultaneously. The contributions of this work are two-fold: 1) demonstrating that the proposed method achieves better than state-of-the-art results on the estimation problem and 2) developing a technique to reduce misalignment based on the learned PLS factors that outperform multiple instance learning (MIL) without the need for any re-training or the inclusion of misaligned samples in the training process, as normally done in MIL. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Haj2013 | Serial | 2278 | ||
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| Author | Albert Gordo | ||||
| Title | Document Image Representation, Classification and Retrieval in Large-Scale Domains | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements. Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ernest Valveny;Florent Perronnin | |
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Gor2013 | Serial | 2277 | ||
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| Author | David Vazquez | ||||
| Title | Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
| Volume | 1 | Issue | 1 | Pages | 1-105 |
| Keywords | Pedestrian Detection; Domain Adaptation | ||||
| Abstract | Pedestrian detection is of paramount interest for many applications, e.g. Advanced Driver Assistance Systems, Intelligent Video Surveillance and Multimedia systems. Most promising pedestrian detectors rely on appearance-based classifiers trained with annotated data. However, the required annotation step represents an intensive and subjective task for humans, what makes worth to minimize their intervention in this process by using computational tools like realistic virtual worlds. The reason to use these kind of tools relies in the fact that they allow the automatic generation of precise and rich annotations of visual information. Nevertheless, the use of this kind of data comes with the following question: can a pedestrian appearance model learnt with virtual-world data work successfully for pedestrian detection in real-world scenarios?. To answer this question, we conduct different experiments that suggest a positive answer. However, the pedestrian classifiers trained with virtual-world data can suffer the so called dataset shift problem as real-world based classifiers does. Accordingly, we have designed different domain adaptation techniques to face this problem, all of them integrated in a same framework (V-AYLA). We have explored different methods to train a domain adapted pedestrian classifiers by collecting a few pedestrian samples from the target domain (real world) and combining them with many samples of the source domain (virtual world). The extensive experiments we present show that pedestrian detectors developed within the V-AYLA framework do achieve domain adaptation. Ideally, we would like to adapt our system without any human intervention. Therefore, as a first proof of concept we also propose an unsupervised domain adaptation technique that avoids human intervention during the adaptation process. To the best of our knowledge, this Thesis work is the first demonstrating adaptation of virtual and real worlds for developing an object detector. Last but not least, we also assessed a different strategy to avoid the dataset shift that consists in collecting real-world samples and retrain with them in such a way that no bounding boxes of real-world pedestrians have to be provided. We show that the generated classifier is competitive with respect to the counterpart trained with samples collected by manually annotating pedestrian bounding boxes. The results presented on this Thesis not only end with a proposal for adapting a virtual-world pedestrian detector to the real world, but also it goes further by pointing out a new methodology that would allow the system to adapt to different situations, which we hope will provide the foundations for future research in this unexplored area. | ||||
| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Barcelona | Editor | Antonio Lopez;Daniel Ponsa |
| Language | English | Summary Language | Original Title | ||
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| ISSN | ISBN | 978-84-940530-1-6 | Medium | ||
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| Notes | adas | Approved | yes | ||
| Call Number | ADAS @ adas @ Vaz2013 | Serial | 2276 | ||
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| Author | Jean-Marc Ogier; Wenyin Liu; Josep Llados (eds) | ||||
| Title | Graphics Recognition: Achievements, Challenges, and Evolution | Type | Book Whole | ||
| Year | 2010 | Publication | 8th International Workshop GREC 2009. | Abbreviated Journal | |
| Volume | 6020 | Issue | Pages | ||
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| Address | La Rochelle | ||||
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| Publisher | Springer Link | Place of Publication | Editor | Jean-Marc Ogier; Wenyin Liu; Josep Llados | |
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| Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | LNCS | |
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| ISSN | ISBN | 978-3-642-13727-3 | Medium | ||
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Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ OLL2010 | Serial | 1976 | ||
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| Author | Angel Sappa; George A. Triantafyllid | ||||
| Title | Computer Graphics and Imaging | Type | Book Whole | ||
| Year | 2012 | Publication | Computer Graphics and Imaging | Abbreviated Journal | |
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| Address | Crete, Greece | ||||
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| ISSN | ISBN | 978-0-88986-921-9 | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Sap2012 | Serial | 2067 | ||
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| Author | Theo Gevers; Arjan Gijsenij; Joost Van de Weijer; J.M. Geusebroek | ||||
| Title | Color in Computer Vision: Fundamentals and Applications | Type | Book Whole | ||
| Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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| Publisher | The Wiley-IS&T Series in Imaging Science and Technology | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-0-470-89084-4 | Medium | ||
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| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ GGG2012a | Serial | 2068 | ||
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| Author | Mario Hernandez; Joao Sanchez; Jordi Vitria | ||||
| Title | Selected papers from Iberian Conference on Pattern Recognition and Image Analysis | Type | Book Whole | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | |
| Volume | 45 | Issue | 9 | Pages | 3047-3582 |
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area |
Expedition | Conference | |||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ HSV2012 | Serial | 2069 | ||
| Permanent link to this record | |||||