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| Author | Fadi Dornaika; Angel Sappa | ||||
| Title | A Featureless and Stochastic Approach to On-board Stereo Vision System Pose | Type | Journal Article | ||
| Year | 2009 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 27 | Issue | 9 | Pages | 1382–1393 |
| Keywords | On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping | ||||
| Abstract | This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ DoS2009b | Serial | 1152 | ||
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| Author | Jordi Gonzalez; Dani Rowe; Javier Varona; Xavier Roca | ||||
| Title | Understanding Dynamic Scenes based on Human Sequence Evaluation | Type | Journal Article | ||
| Year | 2009 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 27 | Issue | 10 | Pages | 1433–1444 |
| Keywords | Image Sequence Evaluation; High-level processing of monitored scenes; Segmentation and tracking in complex scenes; Event recognition in dynamic scenes; Human motion understanding; Human behaviour interpretation; Natural-language text generation; Realistic demonstrators | ||||
| Abstract | In this paper, a Cognitive Vision System (CVS) is presented, which explains the human behaviour of monitored scenes using natural-language texts. This cognitive analysis of human movements recorded in image sequences is here referred to as Human Sequence Evaluation (HSE) which defines a set of transformation modules involved in the automatic generation of semantic descriptions from pixel values. In essence, the trajectories of human agents are obtained to generate textual interpretations of their motion, and also to infer the conceptual relationships of each agent w.r.t. its environment. For this purpose, a human behaviour model based on Situation Graph Trees (SGTs) is considered, which permits both bottom-up (hypothesis generation) and top-down (hypothesis refinement) analysis of dynamic scenes. The resulting system prototype interprets different kinds of behaviour and reports textual descriptions in multiple languages. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ GRV2009 | Serial | 1211 | ||
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| Author | Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez | ||||
| Title | An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes | Type | Journal Article | ||
| Year | 2010 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 28 | Issue | 1 | Pages | 164-176 |
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| Abstract | Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach. | ||||
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| ISSN | 0262-8856 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ JSL2010 | Serial | 1278 | ||
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| Author | Bogdan Raducanu; Jordi Vitria; Ales Leonardis | ||||
| Title | Online pattern recognition and machine learning techniques for computer-vision: Theory and applications | Type | Journal Article | ||
| Year | 2010 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 28 | Issue | 7 | Pages | 1063–1064 |
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| Abstract | (Editorial for the Special Issue on Online pattern recognition and machine learning techniques)
In real life, visual learning is supposed to be a continuous process. This paradigm has found its way also in artificial vision systems. There is an increasing trend in pattern recognition represented by online learning approaches, which aims at continuously updating the data representation when new information arrives. Starting with a minimal dataset, the initial knowledge is expanded by incorporating incoming instances, which may have not been previously available or foreseen at the system’s design stage. An interesting characteristic of this strategy is that the train and test phases take place simultaneously. Given the increasing interest in this subject, the aim of this special issue is to be a landmark event in the development of online learning techniques and their applications with the hope that it will capture the interest of a wider audience and will attract even more researchers. We received 19 contributions, of which 9 have been accepted for publication, after having been subjected to usual peer review process. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0262-8856 | ISBN | Medium | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RVL2010 | Serial | 1280 | ||
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| Author | Oriol Pujol; Debora Gil; Petia Radeva | ||||
| Title | Fundamentals of Stop and Go active models | Type | Journal Article | ||
| Year | 2005 | Publication | Image and Vision Computing | Abbreviated Journal | |
| Volume | 23 | Issue | 8 | Pages | 681-691 |
| Keywords | Deformable models; Geodesic snakes; Region-based segmentation | ||||
| Abstract | An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation. | ||||
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| Publisher | Butterworth-Heinemann | Place of Publication | Newton, MA, USA | Editor | |
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| ISSN | 0262-8856 | ISBN | Medium | ||
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| Notes | IAM;MILAB;HuPBA | Approved | no | ||
| Call Number | IAM @ iam @ PGR2005 | Serial | 1629 | ||
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| Author | Noha Elfiky; Jordi Gonzalez; Xavier Roca | ||||
| Title | Compact and Adaptive Spatial Pyramids for Scene Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 30 | Issue | 8 | Pages | 492–500 |
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| Abstract | Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ EGR2012 | Serial | 2004 | ||
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| Author | Francisco Javier Orozco; Ognjen Rudovic; Jordi Gonzalez; Maja Pantic | ||||
| Title | Hierarchical On-line Appearance-Based Tracking for 3D Head Pose, Eyebrows, Lips, Eyelids and Irises | Type | Journal Article | ||
| Year | 2013 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 31 | Issue | 4 | Pages | 322-340 |
| Keywords | On-line appearance models; Levenberg–Marquardt algorithm; Line-search optimization; 3D face tracking; Facial action tracking; Eyelid tracking; Iris tracking | ||||
| Abstract | In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can also be used for eyelid and iris tracking, as well as 3D head pose, lips and eyebrows facial actions tracking. Furthermore, our approach applies an on-line learning of changes in the appearance of the tracked target. Hence, the prior training of appearance models, which usually requires a large amount of labeled facial images, is avoided. Moreover, the proposed method is built upon a hierarchical combination of three OABTs, which are optimized using a Levenberg–Marquardt Algorithm (LMA) enhanced with line-search procedures. This, in turn, makes the proposed method robust to changes in lighting conditions, occlusions and translucent textures, as evidenced by our experiments. Finally, the proposed method achieves head and facial actions tracking in real-time. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | ISE; 605.203; 302.012; 302.018; 600.049 | Approved | no | ||
| Call Number | ORG2013 | Serial | 2221 | ||
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| Author | Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez | ||||
| Title | Chromatic shadow detection and tracking for moving foreground segmentation | Type | Journal Article | ||
| Year | 2015 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 41 | Issue | Pages | 42-53 | |
| Keywords | Detecting moving objects; Chromatic shadow detection; Temporal local gradient; Spatial and Temporal brightness and angle distortions; Shadow tracking | ||||
| Abstract | Advanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently, umbra shadow regions are usually detected as part of moving objects, thus a ecting the performance of the nal detection. In this paper we address the detection of both penumbra and umbra shadow regions. First, a novel bottom-up approach is presented based on gradient and colour models, which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence, those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution re nes even further the segmentation results: a tracking-based top-down approach increases the performance of our bottom-up chromatic shadow detection algorithm by properly correcting non-detected shadows.
To do so, a combination of motion lters in a data association framework exploits the temporal consistency between objects and shadows to increase the shadow detection rate. Experimental results exceed current state-of-the- art in shadow accuracy for multiple well-known surveillance image databases which contain di erent shadowed materials and illumination conditions. |
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| Notes | ISE; 600.078; 600.063 | Approved | no | ||
| Call Number | Admin @ si @ HHM2015 | Serial | 2703 | ||
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| Author | Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari | ||||
| Title | Training my car to see using virtual worlds | Type | Journal Article | ||
| Year | 2017 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 38 | Issue | Pages | 102-118 | |
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| Abstract | Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LVS2017 | Serial | 2985 | ||
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| Author | Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez | ||||
| Title | Beyond Oneshot Encoding: lower dimensional target embedding | Type | Journal Article | ||
| Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 75 | Issue | Pages | 21-31 | |
| Keywords | Error correcting output codes; Output embeddings; Deep learning; Computer vision | ||||
| Abstract | Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates. | ||||
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| Notes | ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RBE2018 | Serial | 3120 | ||
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| Author | Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera | ||||
| Title | Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification | Type | Journal Article | ||
| Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 79 | Issue | Pages | 76-85 | |
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| Abstract | Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset. | ||||
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| Notes | HuPBA; 602.143 | Approved | no | ||
| Call Number | Admin @ si @ JBE2018 | Serial | 3138 | ||
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| Author | Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez | ||||
| Title | Top-down model fitting for hand pose recovery in sequences of depth images | Type | Journal Article | ||
| Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 79 | Issue | Pages | 63-75 | |
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| Abstract | State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs. | ||||
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| Notes | HUPBA; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ MEC2018 | Serial | 3203 | ||
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| Author | Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca | ||||
| Title | Camera pose estimation in multi-view environments: From virtual scenarios to the real world | Type | Journal Article | ||
| Year | 2021 | Publication | Image and Vision Computing | Abbreviated Journal | IVC |
| Volume | 110 | Issue | Pages | 104182 | |
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| Abstract | This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios. | ||||
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| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ CSV2021 | Serial | 3577 | ||
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| Author | Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer | ||||
| Title | Self-supervised blur detection from synthetically blurred scenes | Type | Journal Article | ||
| Year | 2019 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 92 | Issue | Pages | 103804 | |
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| Abstract | Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image. | ||||
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| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ AGG2019 | Serial | 3301 | ||
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