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| Author | M. Oliver; G. Haro; Mariella Dimiccoli; B. Mazin; C. Ballester | ||||
| Title | A Computational Model for Amodal Completion | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
| Volume | 56 | Issue | 3 | Pages | 511–534 |
| Keywords | Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica | ||||
| Abstract | This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
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| Notes | MILAB; 601.235 | Approved | no | ||
Call Number  |
Admin @ si @ OHD2016b | Serial | 2745 | ||
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| Author | Jean-Marc Ogier; Wenyin Liu; Josep Llados (eds) | ||||
| Title | Graphics Recognition: Achievements, Challenges, and Evolution | Type | Book Whole | ||
| Year | 2010 | Publication | 8th International Workshop GREC 2009. | Abbreviated Journal | |
| Volume | 6020 | Issue | Pages | ||
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| Address | La Rochelle | ||||
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| Publisher | Springer Link | Place of Publication | Editor | Jean-Marc Ogier; Wenyin Liu; Josep Llados | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | LNCS | |
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-642-13727-3 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ OLL2010 | Serial | 1976 | ||
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| Author | Naveen Onkarappa; Angel Sappa | ||||
| Title | A Novel Space Variant Image Representation | Type | Journal Article | ||
| Year | 2013 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
| Volume | 47 | Issue | 1-2 | Pages | 48-59 |
| Keywords | Space-variant representation; Log-polar mapping; Onboard vision applications | ||||
| Abstract | Traditionally, in machine vision images are represented using cartesian coordinates with uniform sampling along the axes. On the contrary, biological vision systems represent images using polar coordinates with non-uniform sampling. For various advantages provided by space-variant representations many researchers are interested in space-variant computer vision. In this direction the current work proposes a novel and simple space variant representation of images. The proposed representation is compared with the classical log-polar mapping. The log-polar representation is motivated by biological vision having the characteristic of higher resolution at the fovea and reduced resolution at the periphery. On the contrary to the log-polar, the proposed new representation has higher resolution at the periphery and lower resolution at the fovea. Our proposal is proved to be a better representation in navigational scenarios such as driver assistance systems and robotics. The experimental results involve analysis of optical flow fields computed on both proposed and log-polar representations. Additionally, an egomotion estimation application is also shown as an illustrative example. The experimental analysis comprises results from synthetic as well as real sequences. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0924-9907 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.055; 605.203; 601.215 | Approved | no | ||
| Call Number |
Admin @ si @ OnS2013a | Serial | 2243 | ||
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| Author | Naveen Onkarappa; Angel Sappa | ||||
| Title | Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario | Type | Conference Article | ||
| Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 8048 | Issue | Pages | 483-490 | |
| Keywords | Optical flow; regularization; Driver Assistance Systems; Performance Evaluation | ||||
| Abstract | Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI). | ||||
| Address | York; UK; August 2013 | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-40245-6 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | ADAS; 600.055; 601.215 | Approved | no | ||
| Call Number |
Admin @ si @ OnS2013b | Serial | 2244 | ||
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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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| Abstract | 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 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 | Naveen Onkarappa; Angel Sappa | ||||
| Title | Synthetic sequences and ground-truth flow field generation for algorithm validation | Type | Journal Article | ||
| Year | 2015 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 74 | Issue | 9 | Pages | 3121-3135 |
| Keywords | Ground-truth optical flow; Synthetic sequence; Algorithm validation | ||||
| Abstract | Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.055; 601.215; 600.076 | Approved | no | ||
| Call Number |
Admin @ si @ OnS2014b | Serial | 2472 | ||
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| Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company | ||||
| Title | Brightness and colour induction through contextual influences in V1 | Type | Conference Article | ||
| Year | 2015 | Publication | Scottish Vision Group 2015 SGV2015 | Abbreviated Journal | |
| Volume | 12 | Issue | 9 | Pages | 1208-2012 |
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| Address | Carnoustie; Scotland; March 2015 | ||||
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| Area | Expedition | Conference | SGV | ||
| Notes | NEUROBIT;CIC | Approved | no | ||
| Call Number |
Admin @ si @ OPC2015a | Serial | 2632 | ||
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| Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company | ||||
| Title | An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort | Type | Conference Article | ||
| Year | 2015 | Publication | Barcelona Computational, Cognitive and Systems Neuroscience | Abbreviated Journal | |
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| Address | Barcelona; June 2015 | ||||
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| Area | Expedition | Conference | BARCCSYN | ||
| Notes | NEUROBIT;CIC | Approved | no | ||
| Call Number |
Admin @ si @ OPC2015b | Serial | 2634 | ||
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| Author | Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco | ||||
| Title | An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction | Type | Conference Article | ||
| Year | 2012 | Publication | 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | |
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| Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. From a purely computational perspective, we built a low-level computational model (CIWaM) of early sensory processing based on multi-resolution wavelets with the aim of replicating brightness and colour (Otazu et al., 2010, Journal of Vision, 10(12):5) induction effects. Furthermore, we successfully used the CIWaM architecture to define a computational saliency model (Murray et al, 2011, CVPR, 433-440; Vanrell et al, submitted to AVA/BMVA'12). From a biological perspective, neurophysiological evidence suggests that perceived brightness information may be explicitly represented in V1. In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (Li, 1999, Network:Comput. Neural Syst., 10, 187-212) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as visual saliency, which share with brightness induction the relevant effect of contextual influences (the ones modelled by CIWaM). In the proposed model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition taken from the computational model (CIWaM).
This model successfully accounts for well known pyschophysical effects (among them: the White's and modied White's effects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction effects) for static contexts and also for brigthness induction in dynamic contexts defined by modulating the luminance of surrounding areas. From a methodological point of view, we conclude that the results obtained by the computational model (CIWaM) are compatible with the ones obtained by the neurodynamical model proposed here. |
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| Area | Expedition | Conference | AV A | ||
| Notes | CIC | Approved | no | ||
| Call Number |
Admin @ si @ OPD2012a | Serial | 2132 | ||
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| Author | Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco | ||||
| Title | Brightness induction by contextual influences in V1: a neurodynamical account | Type | Abstract | ||
| Year | 2012 | Publication | Journal of Vision | Abbreviated Journal | VSS |
| Volume | 12 | Issue | 9 | Pages | |
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| Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas and reveals fundamental properties of neural organization in the visual system. Several phenomenological models have been proposed that successfully account for psychophysical data (Pessoa et al. 1995, Blakeslee and McCourt 2004, Barkan et al. 2008, Otazu et al. 2008).
Neurophysiological evidence suggests that brightness information is explicitly represented in V1 and neuronal response modulations have been observed followingluminance changes outside their receptive fields (Rossi and Paradiso, 1999). In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (1999) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2–3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant effect of contextual influences. In our model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition which makes it possible to recover an image reflecting the perceived intensity. The proposed model successfully accounts for well known pyschophysical effects (among them: the White's and modified White's effects, the Todorović, Chevreul, achromatic ring patterns, and grating induction effects). Our work suggests that intra-cortical interactions in the primary visual cortex could partially explain perceptual brightness induction effects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual pathway. |
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| Notes | CIC | Approved | no | ||
| Call Number |
Admin @ si @ OPD2012b | Serial | 2178 | ||
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| Author | Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste | ||||
| Title | TADAM: Task dependent adaptive metric for improved few-shot learning | Type | Conference Article | ||
| Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. | ||||
| Address | Montreal; Canada; December 2018 | ||||
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| Area | Expedition | Conference | NIPS | ||
| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number |
Admin @ si @ ORL2018 | Serial | 3140 | ||
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| Author | Francisco Javier Orozco | ||||
| Title | Face Detection and Tracking for Facial Expression Analysis | Type | Report | ||
| Year | 2007 | Publication | CVC Technical Report #103 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ Oro2007 | Serial | 818 | ||
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| Author | Francisco Javier Orozco | ||||
| Title | Human Emotion Evaluation on Facial Image Sequences | Type | Book Whole | ||
| Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Psychological evidence has emphasized the importance of affective behaviour understanding due to its high impact in nowadays interaction humans and computers. All
type of affective and behavioural patterns such as gestures, emotions and mental states are highly displayed through the face, head and body. Therefore, this thesis is focused to analyse affective behaviours on head and face. To this end, head and facial movements are encoded by using appearance based tracking methods. Specifically, a wise combination of deformable models captures rigid and non-rigid movements of different kinematics; 3D head pose, eyebrows, mouth, eyelids and irises are taken into account as basis for extracting features from databases of video sequences. This approach combines the strengths of adaptive appearance models, optimization methods and backtracking techniques. For about thirty years, computer sciences have addressed the investigation on human emotions to the automatic recognition of six prototypic emotions suggested by Darwin and systematized by Paul Ekman in the seventies. The Facial Action Coding System (FACS) which uses discrete movements of the face (called Action units or AUs) to code the six facial emotions named anger, disgust, fear, happy-Joy, sadness and surprise. However, human emotions are much complex patterns that have not received the same attention from computer scientists. Simon Baron-Cohen proposed a new taxonomy of emotions and mental states without a system coding of the facial actions. These 426 affective behaviours are more challenging for the understanding of human emotions. Beyond of classically classifying the six basic facial expressions, more subtle gestures, facial actions and spontaneous emotions are considered here. By assessing confidence on the recognition results, exploring spatial and temporal relationships of the features, some methods are combined and enhanced for developing new taxonomy of expressions and emotions. The objective of this dissertation is to develop a computer vision system, including both facial feature extraction, expression recognition and emotion understanding by building a bottom-up reasoning process. Building a detailed taxonomy of human affective behaviours is an interesting challenge for head-face-based image analysis methods. In this paper, we exploit the strengths of Canonical Correlation Analysis (CCA) to enhance an on-line head-face tracker. A relationship between head pose and local facial movements is studied according to their cognitive interpretation on affective expressions and emotions. Active Shape Models are synthesized for AAMs based on CCA-regression. Head pose and facial actions are fused into a maximally correlated space in order to assess expressiveness, confidence and classification in a CBR system. The CBR solutions are also correlated to the cognitive features, which allow avoiding exhaustive search when recognizing new head-face features. Subsequently, Support Vector Machines (SVMs) and Bayesian Networks are applied for learning the spatial relationships of facial expressions. Similarly, the temporal evolution of facial expressions, emotion and mental states are analysed based on Factorized Dynamic Bayesian Networks (FaDBN). As results, the bottom-up system recognizes six facial expressions, six basic emotions and six mental states, plus enhancing this categorization with confidence assessment at each level, intensity of expressions and a complete taxonomy |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| ISSN | ISBN | 978-84-936529-3-7 | Medium | ||
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| Notes | Approved | no | |||
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Admin @ si @ Oro2010 | Serial | 1335 | ||
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| Author | X. Orriols | ||||
| Title | Models locals lineals per a l´analisi d´imatges | Type | Report | ||
| Year | 1999 | Publication | CVC Technical Report #31 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ Orr1999 | Serial | 192 | ||
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| Author | Marc Oliu; Javier Selva; Sergio Escalera | ||||
| Title | Folded Recurrent Neural Networks for Future Video Prediction | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 11218 | Issue | Pages | 745-761 | |
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| Abstract | Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach. | ||||
| Address | Munich; September 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCV | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number |
Admin @ si @ OSE2018 | Serial | 3204 | ||
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