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| Author | Antonio Hernandez | ||||
| Title | From pixels to gestures: learning visual representations for human analysis in color and depth data sequences | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The visual analysis of humans from images is an important topic of interest due to its relevance to many computer vision applications like pedestrian detection, monitoring and surveillance, human-computer interaction, e-health or content-based image retrieval, among others.
In this dissertation we are interested in learning different visual representations of the human body that are helpful for the visual analysis of humans in images and video sequences. To that end, we analyze both RGB and depth image modalities and address the problem from three different research lines, at different levels of abstraction; from pixels to gestures: human segmentation, human pose estimation and gesture recognition. First, we show how binary segmentation (object vs. background) of the human body in image sequences is helpful to remove all the background clutter present in the scene. The presented method, based on Graph cuts optimization, enforces spatio-temporal consistency of the produced segmentation masks among consecutive frames. Secondly, we present a framework for multi-label segmentation for obtaining much more detailed segmentation masks: instead of just obtaining a binary representation separating the human body from the background, finer segmentation masks can be obtained separating the different body parts. At a higher level of abstraction, we aim for a simpler yet descriptive representation of the human body. Human pose estimation methods usually rely on skeletal models of the human body, formed by segments (or rectangles) that represent the body limbs, appropriately connected following the kinematic constraints of the human body. In practice, such skeletal models must fulfill some constraints in order to allow for efficient inference, while actually limiting the expressiveness of the model. In order to cope with this, we introduce a top-down approach for predicting the position of the body parts in the model, using a mid-level part representation based on Poselets. Finally, we propose a framework for gesture recognition based on the bag of visual words framework. We leverage the benefits of RGB and depth image modalities by combining modality-specific visual vocabularies in a late fusion fashion. A new rotation-variant depth descriptor is presented, yielding better results than other state-of-the-art descriptors. Moreover, spatio-temporal pyramids are used to encode rough spatial and temporal structure. In addition, we present a probabilistic reformulation of Dynamic Time Warping for gesture segmentation in video sequences. A Gaussian-based probabilistic model of a gesture is learnt, implicitly encoding possible deformations in both spatial and time domains. |
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| Address | January 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor  |
Sergio Escalera;Stan Sclaroff | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-940902-0-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ Her2015 | Serial | 2576 | ||
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| Author | Marc Serra | ||||
| Title | Modeling, estimation and evaluation of intrinsic images considering color information | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...). In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem. |
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| Address | September 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
Robert Benavente;Olivier Penacchio | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-4-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ Ser2015 | Serial | 2688 | ||
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| Author | Hongxing Gao | ||||
| Title | Focused Structural Document Image Retrieval in Digital Mailroom Applications | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | In this work, we develop a generic framework that is able to handle the document retrieval problem in various scenarios such as searching for full page matches or retrieving the counterparts for specific document areas, focusing on their structural similarity or letting their visual resemblance to play a dominant role. Based on the spatial indexing technique, we propose to search for matches of local key-region pairs carrying both structural and visual information from the collection while a scheme allowing to adjust the relative contribution of structural and visual similarity is presented.
Based on the fact that the structure of documents is tightly linked with the distance among their elements, we firstly introduce an efficient detector named Distance Transform based Maximally Stable Extremal Regions (DTMSER). We illustrate that this detector is able to efficiently extract the structure of a document image as a dendrogram (hierarchical tree) of multi-scale key-regions that roughly correspond to letters, words and paragraphs. We demonstrate that, without benefiting from the structure information, the key-regions extracted by the DTMSER algorithm achieve better results comparing with state-of-the-art methods while much less amount of key-regions are employed. We subsequently propose a pair-wise Bag of Words (BoW) framework to efficiently embed the explicit structure extracted by the DTMSER algorithm. We represent each document as a list of key-region pairs that correspond to the edges in the dendrogram where inclusion relationship is encoded. By employing those structural key-region pairs as the pooling elements for generating the histogram of features, the proposed method is able to encode the explicit inclusion relations into a BoW representation. The experimental results illustrate that the pair-wise BoW, powered by the embedded structural information, achieves remarkable improvement over the conventional BoW and spatial pyramidal BoW methods. To handle various retrieval scenarios in one framework, we propose to directly query a series of key-region pairs, carrying both structure and visual information, from the collection. We introduce the spatial indexing techniques to the document retrieval community to speed up the structural relationship computation for key-region pairs. We firstly test the proposed framework in a full page retrieval scenario where structurally similar matches are expected. In this case, the pair-wise querying method achieves notable improvement over the BoW and spatial pyramidal BoW frameworks. Furthermore, we illustrate that the proposed method is also able to handle focused retrieval situations where the queries are defined as a specific interesting partial areas of the images. We examine our method on two types of focused queries: structure-focused and exact queries. The experimental results show that, the proposed generic framework obtains nearly perfect precision on both types of focused queries while it is the first framework able to tackle structure-focused queries, setting a new state of the art in the field. Besides, we introduce a line verification method to check the spatial consistency among the matched key-region pairs. We propose a computationally efficient version of line verification through a two step implementation. We first compute tentative localizations of the query and subsequently employ them to divide the matched key-region pairs into several groups, then line verification is performed within each group while more precise bounding boxes are computed. We demonstrate that, comparing with the standard approach (based on RANSAC), the line verification proposed generally achieves much higher recall with slight loss on precision on specific queries. |
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| Address | January 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
Josep Llados;Dimosthenis Karatzas;Marçal Rusiñol | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-0-7 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ Gao2015 | Serial | 2577 | ||
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| Author | C. Alejandro Parraga | ||||
| Title | Perceptual Psychophysics | Type | Book Chapter | ||
| Year | 2015 | Publication | Biologically-Inspired Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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| Publisher | Place of Publication | Editor |
G.Cristobal; M.Keil; L.Perrinet | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-527-41264-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ Par2015 | Serial | 2600 | ||
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| Author | Joan M. Nuñez | ||||
| Title | Vascular Pattern Characterization in Colonoscopy Images | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Colorectal cancer is the third most common cancer worldwide and the second most common malignant tumor in Europe. Screening tests have shown to be very e ective in increasing the survival rates since they allow an early detection of polyps. Among the di erent screening techniques, colonoscopy is considered the gold standard although clinical studies mention several problems that have an impact in the quality of the procedure. The navigation through the rectum and colon track can be challenging for the physicians which can increase polyp miss rates. The thorough visualization of the colon track must be ensured so that
the chances of missing lesions are minimized. The visual analysis of colonoscopy images can provide important information to the physicians and support their navigation during the procedure. Blood vessels and their branching patterns can provide descriptive power to potentially develop biometric markers. Anatomical markers based on blood vessel patterns could be used to identify a particular scene in colonoscopy videos and to support endoscope navigation by generating a sequence of ordered scenes through the di erent colon sections. By verifying the presence of vascular content in the endoluminal scene it is also possible to certify a proper inspection of the colon mucosa and to improve polyp localization. Considering the potential uses of blood vessel description, this contribution studies the characterization of the vascular content and the analysis of the descriptive power of its branching patterns. Blood vessel characterization in colonoscopy images is shown to be a challenging task. The endoluminal scene is conformed by several elements whose similar characteristics hinder the development of particular models for each of them. To overcome such diculties we propose the use of the blood vessel branching characteristics as key features for pattern description. We present a model to characterize junctions in binary patterns. The implementation of the junction model allows us to develop a junction localization method. We created two data sets including manually labeled vessel information as well as manual ground truths of two types of keypoint landmarks: junctions and endpoints. The proposed method outperforms the available algorithms in the literature in experiments in both, our newly created colon vessel data set, and in DRIVE retinal fundus image data set. In the latter case, we created a manual ground truth of junction coordinates. Since we want to explore the descriptive potential of junctions and vessels, we propose a graph-based approach to create anatomical markers. In the context of polyp localization, we present a new method to inhibit the in uence of blood vessels in the extraction valley-pro le information. The results show that our methodology decreases vessel in uence, increases polyp information and leads to an improvement in state-of-the-art polyp localization performance. We also propose a polyp-speci c segmentation method that outperforms other general and speci c approaches. |
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| Address | November 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
Fernando Vilariño | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-6-9 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MV | Approved | no | ||
| Call Number | Admin @ si @ Nuñ2015 | Serial | 2709 | ||
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| Author | Sergio Vera | ||||
| Title | Anatomic Registration based on Medial Axis Parametrizations | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Image registration has been for many years the gold standard method to bring two images into correspondence. It has been used extensively in the eld of medical imaging in order to put images of di erent patients into a common overlapping spatial position. However, medical image registration is a slow, iterative optimization process, where many variables and prone to fall into the pit traps local minima.
A coordinate system parameterizing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to speci c anatomical sites, parameterizations ensure integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric meshes over the surface of anatomical shapes, given their ability to set values at speci c locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at discrete sites of limited geometric diversity. The medial surface of the shape can be used to provide a continuous basis for the de nition of a depth coordinate. However, given that di erent methods for generation of medial surfaces generate di erent manifolds, not all of them are equally suited to be the basis of radial coordinate for a parameterization. It would be desirable that the medial surface will be smooth, and robust to surface shape noise, with low number of spurious branches or surfaces. In this thesis we present methods for computation of smooth medial manifolds and apply them to the generation of for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the volume medial surface. This reference system sets a solid base for creating anatomical models of the anatomical shapes, and allows comparing several patients in a common framework of reference. |
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| Address | November 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
Debora Gil;Miguel Angel Gonzalez Ballester | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-8-3 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ Ver2015 | Serial | 2708 | ||
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| Author | David Roche | ||||
| Title | A Statistical Framework for Terminating Evolutionary Algorithms at their Steady State | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | As any iterative technique, it is a necessary condition a stop criterion for terminating Evolutionary Algorithms (EA). In the case of optimization methods, the algorithm should stop at the time it has reached a steady state so it can not improve results anymore. Assessing the reliability of termination conditions for EAs is of prime importance. A wrong or weak stop criterion can negatively a ect both the computational e ort and the nal result.
In this Thesis, we introduce a statistical framework for assessing whether a termination condition is able to stop EA at its steady state. In one hand a numeric approximation to steady states to detect the point in which EA population has lost its diversity has been presented for EA termination. This approximation has been applied to di erent EA paradigms based on diversity and a selection of functions covering the properties most relevant for EA convergence. Experiments show that our condition works regardless of the search space dimension and function landscape and Di erential Evolution (DE) arises as the best paradigm. On the other hand, we use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in xspace. Our theoretical framework is analyzed across several benchmark test functions and two standard termination criteria based on function improvement in f-space and EA population x-space distribution for the DE paradigm. Results validate our statistical framework as a powerful tool for determining the capability of a measure for terminating EA and select the x-space distribution as the best-suited for accurately stopping DE in real-world applications. |
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| Address | July 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
Debora Gil;Jesus Giraldo | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ Roc2015 | Serial | 2686 | ||
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| Author | Patricia Marquez | ||||
| Title | A Confidence Framework for the Assessment of Optical Flow Performance | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Optical Flow (OF) is the input of a wide range of decision support systems such as car driver assistance, UAV guiding or medical diagnose. In these real situations, the absence of ground truth forces to assess OF quality using quantities computed from either sequences or the computed optical flow itself. These quantities are generally known as Confidence Measures, CM. Even if we have a proper confidence measure we still need a way to evaluate its ability to discard pixels with an OF prone to have a large error. Current approaches only provide a descriptive evaluation of the CM performance but such approaches are not capable to fairly compare different confidence measures and optical flow algorithms. Thus, it is of prime importance to define a framework and a general road map for the evaluation of optical flow performance.
This thesis provides a framework able to decide which pairs “ optical flow – confidence measure” (OF-CM) are best suited for optical flow error bounding given a confidence level determined by a decision support system. To design this framework we cover the following points: Descriptive scores. As a first step, we summarize and analyze the sources of inaccuracies in the output of optical flow algorithms. Second, we present several descriptive plots that visually assess CM capabilities for OF error bounding. In addition to the descriptive plots, given a plot representing OF-CM capabilities to bound the error, we provide a numeric score that categorizes the plot according to its decreasing profile, that is, a score assessing CM performance. Statistical framework. We provide a comparison framework that assesses the best suited OF-CM pair for error bounding that uses a two stage cascade process. First of all we assess the predictive value of the confidence measures by means of a descriptive plot. Then, for a sample of descriptive plots computed over training frames, we obtain a generic curve that will be used for sequences with no ground truth. As a second step, we evaluate the obtained general curve and its capabilities to really reflect the predictive value of a confidence measure using the variability across train frames by means of ANOVA. The presented framework has shown its potential in the application on clinical decision support systems. In particular, we have analyzed the impact of the different image artifacts such as noise and decay to the output of optical flow in a cardiac diagnose system and we have improved the navigation inside the bronchial tree on bronchoscopy. |
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| Address | July 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
Debora Gil;Aura Hernandez | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-2-1 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ Mar2015 | Serial | 2687 | ||
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| Author | Alejandro Gonzalez Alzate | ||||
| Title | Multi-modal Pedestrian Detection | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Pedestrian detection continues to be an extremely challenging problem in real scenarios, in which situations like illumination changes, noisy images, unexpected objects, uncontrolled scenarios and variant appearance of objects occur constantly. All these problems force the development of more robust detectors for relevant applications like vision-based autonomous vehicles, intelligent surveillance, and pedestrian tracking for behavior analysis. Most reliable vision-based pedestrian detectors base their decision on features extracted using a single sensor capturing complementary features, e.g., appearance, and texture. These features usually are extracted from the current frame, ignoring temporal information, or including it in a post process step e.g., tracking or temporal coherence. Taking into account these issues we formulate the following question: can we generate more robust pedestrian detectors by introducing new information sources in the feature extraction step?
In order to answer this question we develop different approaches for introducing new information sources to well-known pedestrian detectors. We start by the inclusion of temporal information following the Stacked Sequential Learning (SSL) paradigm which suggests that information extracted from the neighboring samples in a sequence can improve the accuracy of a base classifier. We then focus on the inclusion of complementary information from different sensors like 3D point clouds (LIDAR – depth), far infrared images (FIR), or disparity maps (stereo pair cameras). For this end we develop a multi-modal framework in which information from different sensors is used for increasing detection accuracy (by increasing information redundancy). Finally we propose a multi-view pedestrian detector, this multi-view approach splits the detection problem in n sub-problems. Each sub-problem will detect objects in a given specific view reducing in that way the variability problem faced when a single detectors is used for the whole problem. We show that these approaches obtain competitive results with other state-of-the-art methods but instead of design new features, we reuse existing ones boosting their performance. |
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| Address | November 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
David Vazquez;Antonio Lopez; | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-7-6 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ Gon2015 | Serial | 2706 | ||
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| Author | Adriana Romero | ||||
| Title | Assisting the training of deep neural networks with applications to computer vision | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Deep learning has recently been enjoying an increasing popularity due to its success in solving challenging tasks. In particular, deep learning has proven to be effective in a large variety of computer vision tasks, such as image classification, object recognition and image parsing. Contrary to previous research, which required engineered feature representations, designed by experts, in order to succeed, deep learning attempts to learn representation hierarchies automatically from data. More recently, the trend has been to go deeper with representation hierarchies.
Learning (very) deep representation hierarchies is a challenging task, which involves the optimization of highly non-convex functions. Therefore, the search for algorithms to ease the learning of (very) deep representation hierarchies from data is extensive and ongoing. In this thesis, we tackle the challenging problem of easing the learning of (very) deep representation hierarchies. We present a hyper-parameter free, off-the-shelf, simple and fast unsupervised algorithm to discover hidden structure from the input data by enforcing a very strong form of sparsity. We study the applicability and potential of the algorithm to learn representations of varying depth in a handful of applications and domains, highlighting the ability of the algorithm to provide discriminative feature representations that are able to achieve top performance. Yet, while emphasizing the great value of unsupervised learning methods when labeled data is scarce, the recent industrial success of deep learning has revolved around supervised learning. Supervised learning is currently the focus of many recent research advances, which have shown to excel at many computer vision tasks. Top performing systems often involve very large and deep models, which are not well suited for applications with time or memory limitations. More in line with the current trends, we engage in making top performing models more efficient, by designing very deep and thin models. Since training such very deep models still appears to be a challenging task, we introduce a novel algorithm that guides the training of very thin and deep models by hinting their intermediate representations. Very deep and thin models trained by the proposed algorithm end up extracting feature representations that are comparable or even better performing than the ones extracted by large state-of-the-art models, while compellingly reducing the time and memory consumption of the model. |
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| Address | October 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor |
Carlo Gatta;Petia Radeva | |
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Rom2015 | Serial | 2707 | ||
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| Author | Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta | ||||
| Title | Large-scale Graph Indexing using Binary Embeddings of Node Contexts | Type | Conference Article | ||
| Year | 2015 | Publication | 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition | Abbreviated Journal | |
| Volume | 9069 | Issue | Pages | 208-217 | |
| Keywords | Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding | ||||
| Abstract | Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents. | ||||
| Address | Beijing; China; May 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor |
C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-18223-0 | Medium | |
| Area | Expedition | Conference | GbRPR | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RLF2015a | Serial | 2618 | ||
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| Author | Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh | ||||
| Title | Facial expression recognition based on multi observations with application to social robotics | Type | Book Chapter | ||
| Year | 2015 | Publication | Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance | Abbreviated Journal | |
| Volume | Issue | Pages | 153-166 | ||
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| Abstract | Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial expression. |
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| Publisher | Nova Science publishers | Place of Publication | Editor |
Bruce Flores | |
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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ DRB2015 | Serial | 2720 | ||
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| Author | Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Towards the Alignment of Handwritten Music Scores | Type | Conference Article | ||
| Year | 2015 | Publication | 11th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract | It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. | ||||
| Address | Nancy; France; August 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor |
Bart Lamiroy; Rafael Dueire Lins | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-52158-9 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2874 | ||
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| Author | Jiaolong Xu | ||||
| Title | Domain Adaptation of Deformable Part-based Models | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | On-board pedestrian detection is crucial for Advanced Driver Assistance Systems
(ADAS). An accurate classi cation is fundamental for vision-based pedestrian detection. The underlying assumption for learning classi ers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classi ers. However, in practice, there are di erent reasons that can break this constancy assumption. Accordingly, reusing existing classi ers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset. We start by exploiting the maximum detection accuracy of the virtual-world trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still su er from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area. |
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| Address | April 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor |
Antonio Lopez | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-1-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ Xu2015 | Serial | 2631 | ||
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| Author | Jaume Amores | ||||
| Title | MILDE: multiple instance learning by discriminative embedding | Type | Journal Article | ||
| Year | 2015 | Publication | Knowledge and Information Systems | Abbreviated Journal | KAIS |
| Volume | 42 | Issue | 2 | Pages | 381-407 |
| Keywords | Multi-instance learning; Codebook; Bag of words | ||||
| Abstract | While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. | ||||
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| Publisher | Springer London | Place of Publication | Editor |
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0219-1377 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 601.042; 600.057; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ Amo2015 | Serial | 2383 | ||
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