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Author | Jaume Amores; Petia Radeva | ||||
Title | Registration and Retrieval of Highly Elastic Bodies using Contextual Information | Type | Journal Article | ||
Year | 2005 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 26 | Issue | 11 | Pages | 1720–1731 |
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IF: 1.138 | ||||
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Area | Expedition | Conference | |||
Notes | ADAS;MILAB | Approved | no | ||
Call Number | ADAS @ adas @ AmR2005b | Serial | 592 | ||
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Author | Naveen Onkarappa; Angel Sappa | ||||
Title | Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 1 | Pages | 136-147 |
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IF: 3.064
Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields. |
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Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ OnS2014a | Serial | 2386 | ||
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Author | Bogdan Raducanu; Fadi Dornaika | ||||
Title | A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning | Type | Journal Article | ||
Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 45 | Issue | 6 | Pages | 2432-2444 |
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IF= 2.61
IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 0031-3203 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | OR; MV | Approved | no | ||
Call Number | Admin @ si @ RaD2012a | Serial | 1884 | ||
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Author | Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu | ||||
Title | Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction | Type | Journal Article | ||
Year | 2012 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 12 | Issue | 2 | Pages | 1702-1719 |
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IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. |
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Publisher | Molecular Diversity Preservation International | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | |||
Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ EBV2012 | Serial | 1885 | ||
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Author | Michael Teutsch; Angel Sappa; Riad I. Hammoud | ||||
Title | Image and Video Enhancement | Type | Book Chapter | ||
Year | 2022 | Publication | Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 9-21 | ||
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Abstract ![]() |
Image and video enhancement aims at improving the signal quality relative to imaging artifacts such as noise and blur or atmospheric perturbations such as turbulence and haze. It is usually performed in order to assist humans in analyzing image and video content or simply to present humans visually appealing images and videos. However, image and video enhancement can also be used as a preprocessing technique to ease the task and thus improve the performance of subsequent automatic image content analysis algorithms: preceding dehazing can improve object detection as shown by [23] or explicit turbulence modeling can improve moving object detection as discussed by [24]. But it remains an open question whether image and video enhancement should rather be performed explicitly as a preprocessing step or implicitly for example by feeding affected images directly to a neural network for image content analysis like object detection [25]. Especially for real-time video processing at low latency it can be better to handle image perturbation implicitly in order to minimize the processing time of an algorithm. This can be achieved by making algorithms for image content analysis robust or even invariant to perturbations such as noise or blur. Additionally, mistakes of an individual preprocessing module can obviously affect the quality of the entire processing pipeline. | ||||
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Publisher | Springer | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
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Area | Expedition | Conference | |||
Notes | MSIAU; MACO | Approved | no | ||
Call Number | Admin @ si @ TSH2022a | Serial | 3807 | ||
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Author | David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin | ||||
Title | Virtual Worlds and Active Learning for Human Detection | Type | Conference Article | ||
Year | 2011 | Publication | 13th International Conference on Multimodal Interaction | Abbreviated Journal | |
Volume | Issue | Pages | 393-400 | ||
Keywords | Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning | ||||
Abstract ![]() |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. | ||||
Address | Alicante, Spain | ||||
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Publisher | ACM DL | Place of Publication | New York, NY, USA, USA | Editor | |
Language | English | Summary Language | English | Original Title | Virtual Worlds and Active Learning for Human Detection |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-4503-0641-6 | Medium | ||
Area | Expedition | Conference | ICMI | ||
Notes | ADAS | Approved | yes | ||
Call Number | ADAS @ adas @ VLP2011a | Serial | 1683 | ||
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Author | David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin | ||||
Title | Cool world: domain adaptation of virtual and real worlds for human detection using active learning | Type | Conference Article | ||
Year | 2011 | Publication | NIPS Domain Adaptation Workshop: Theory and Application | Abbreviated Journal | NIPS-DA |
Volume | Issue | Pages | |||
Keywords | Pedestrian Detection; Virtual; Domain Adaptation; Active Learning | ||||
Abstract ![]() |
Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity. | ||||
Address | Granada, Spain | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Granada, Spain | Editor | ||
Language | English | Summary Language | English | Original Title | |
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | DA-NIPS | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ VLP2011b | Serial | 1756 | ||
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Author | David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo | ||||
Title | Interactive Training of Human Detectors | Type | Book Chapter | ||
Year | 2013 | Publication | Multiodal Interaction in Image and Video Applications | Abbreviated Journal | |
Volume | 48 | Issue | Pages | 169-182 | |
Keywords | Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation | ||||
Abstract ![]() |
Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations. | ||||
Address | Springer Heidelberg New York Dordrecht London | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | English | Summary Language | Original Title | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
Area | Expedition | Conference | |||
Notes | ADAS; 600.057; 600.054; 605.203 | Approved | no | ||
Call Number | VLP2013; ADAS @ adas @ vlp2013 | Serial | 2193 | ||
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Author | Eduardo Aguilar; Petia Radeva | ||||
Title | Class-Conditional Data Augmentation Applied to Image Classification | Type | Conference Article | ||
Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 11679 | Issue | Pages | 182-192 | |
Keywords | CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition | ||||
Abstract ![]() |
Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty. | ||||
Address | Salermo; Italy; September 2019 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CAIP | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ AgR2019 | Serial | 3366 | ||
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Author | Mohammed Al Rawi; Dimosthenis Karatzas | ||||
Title | On the Labeling Correctness in Computer Vision Datasets | Type | Conference Article | ||
Year | 2018 | Publication | Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | Abbreviated Journal | |
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Abstract ![]() |
Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble. |
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Area | Expedition | Conference | ECML-PKDDW | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ RaK2018 | Serial | 3144 | ||
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Author | Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio | ||||
Title | On the Duality Between Retinex and Image Dehazing | Type | Conference Article | ||
Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 8212–8221 | ||
Keywords | Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting | ||||
Abstract ![]() |
Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem. | ||||
Address | Salt Lake City; USA; June 2018 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ GAB2018 | Serial | 3146 | ||
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Author | Yaxing Wang | ||||
Title | Transferring and Learning Representations for Image Generation and Translation | Type | Book Whole | ||
Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract ![]() |
Image generation is arguably one of the most attractive, compelling, and challenging tasks in computer vision. Among the methods which perform image generation, generative adversarial networks (GANs) play a key role. The most common image generation models based on GANs can be divided into two main approaches. The first one, called simply image generation takes random noise as an input and synthesizes an image which follows the same distribution as the images in the training set. The second class, which is called image-to-image translation, aims to map an image from a source domain to one that is indistinguishable from those in the target domain. Image-to-image translation methods can further be divided into paired and unpaired image-to-image translation based on whether they require paired data or not. In this thesis, we aim to address some challenges of both image generation and image-to-image generation.GANs highly rely upon having access to vast quantities of data, and fail to generate realistic images from random noise when applied to domains with few images. To address this problem, we aim to transfer knowledge from a model trained on a large dataset (source domain) to the one learned on limited data (target domain). We find that both GANs andconditional GANs can benefit from models trained on large datasets. Our experiments show that transferring the discriminator is more important than the generator. Using both the generator and discriminator results in the best performance. We found, however, that this method suffers from overfitting, since we update all parameters to adapt to the target data. We propose a novel architecture, which is tailored to address knowledge transfer to very small target domains. Our approach effectively exploreswhich part of the latent space is more related to the target domain. Additionally, the proposed method is able to transfer knowledge from multiple pretrained GANs. Although image-to-image translation has achieved outstanding performance, it still facesseveral problems. First, for translation between complex domains (such as translations between different modalities) image-to-image translation methods require paired data. We show that when only some of the pairwise translations have been seen (i.e. during training), we can infer the remaining unseen translations (where training pairs are not available). We propose a new approach where we align multiple encoders and decoders in such a way that the desired translation can be obtained by simply cascadingthe source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). Second, we address the issue of bias in image-to-image translation. Biased datasets unavoidably contain undesired changes, which are dueto the fact that the target dataset has a particular underlying visual distribution. We use carefully designed semantic constraints to reduce the effects of the bias. The semantic constraint aims to enforce the preservation of desired image properties. Finally, current approaches fail to generate diverse outputs or perform scalable image transfer in a single model. To alleviate this problem, we propose a scalable and diverse image-to-image translation. We employ random noise to control the diversity. The scalabitlity is determined by conditioning the domain label.computer vision, deep learning, imitation learning, adversarial generative networks, image generation, image-to-image translation. | ||||
Address | January 2020 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Abel Gonzalez;Luis Herranz | |
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ISSN | ISBN | 978-84-121011-5-7 | Medium | ||
Area | Expedition | Conference | |||
Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ Wan2020 | Serial | 3397 | ||
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Author | Gemma Roig; Xavier Boix; Fernando De la Torre | ||||
Title | Optimal Feature Selection for Subspace Image Matching | Type | Conference Article | ||
Year | 2009 | Publication | 2nd IEEE International Workshop on Subspace Methods in conjunction | Abbreviated Journal | |
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Abstract ![]() |
Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching feature points between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naïve gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features. | ||||
Address | Kyoto, Japan | ||||
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Area | Expedition | Conference | ICCV | ||
Notes | Approved | no | |||
Call Number | Admin @ si @ RBT2009 | Serial | 1233 | ||
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Author | M. Olivera; Angel Sappa; Victor Santos | ||||
Title | A probabilistic approach for color correction in image mosaicking applications | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 14 | Issue | 2 | Pages | 508 - 523 |
Keywords | Color correction; image mosaicking; color transfer; color palette mapping functions | ||||
Abstract ![]() |
Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ OSS2015b | Serial | 2554 | ||
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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 dierent 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 specic 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 specic 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 denition of a depth coordinate. However, given that dierent methods for generation of medial surfaces generate dierent 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 | |
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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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