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Author |
Antonio Hernandez |
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Title |
Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation |
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Report |
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2010 |
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CVC Technical Report |
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153 |
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HUPBA;MILAB |
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no |
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Admin @ si @ Her2010 |
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1347 |
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Ahmed Mounir Gad |
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Title |
Object Localization Enhancement by Multiple Segmentation Fusion |
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2010 |
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CVC Technical Report |
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152 |
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no |
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Admin @ si @ Mou2010 |
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1346 |
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Author |
Marc Serra |
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Title |
Estimating Intrinsic Images from Physical and Categorical Color Cues |
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2010 |
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CVC Technical Report |
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151 |
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CIC |
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no |
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Admin @ si @ Ser2010 |
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1345 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title |
Color encoding in biologically-inspired convolutional neural networks |
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Journal Article |
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2018 |
Publication |
Vision Research |
Abbreviated Journal |
VR |
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Volume |
151 |
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7-17 |
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Color coding; Computer vision; Deep learning; Convolutional neural networks |
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Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. |
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CIC; 600.051; 600.087 |
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no |
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Admin @ si @RaV2018 |
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3114 |
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Author |
Javier Marin |
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Title |
Virtual learning for real testing |
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Report |
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2009 |
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CVC Technical Report |
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150 |
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Computer Vision Center |
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Master's thesis |
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bell |
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ADAS |
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no |
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Admin @ si @ Mar2009c |
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2403 |
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Author |
Monica Piñol; Angel Sappa; Ricardo Toledo |
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Title |
Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy |
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Journal Article |
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Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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Volume |
150 |
Issue |
A |
Pages |
106–115 |
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Reinforcement learning; Q-learning; Bag of features; Descriptors |
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This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. |
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ADAS; 600.055; 600.076 |
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no |
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Admin @ si @ PST2015 |
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2473 |
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Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi |
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Title |
Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars |
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Journal Article |
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Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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Volume |
150 |
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A |
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147-154 |
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document image analysis; stochastic context-free grammars; text classication features |
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In this paper we dene a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classication features are used to perform an initial classication of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation. |
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DAG; 601.158; 600.077; 600.061 |
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no |
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Admin @ si @ ACS2015 |
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2531 |
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Author |
Daniel Sanchez; Miguel Angel Bautista; Sergio Escalera |
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Title |
HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs |
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Journal Article |
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2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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150 |
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A |
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173–188 |
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Human limb segmentation; ECOC; Graph-Cuts |
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Human multi-limb segmentation in RGB images has attracted a lot of interest in the research community because of the huge amount of possible applications in fields like Human-Computer Interaction, Surveillance, eHealth, or Gaming. Nevertheless, human multi-limb segmentation is a very hard task because of the changes in appearance produced by different points of view, clothing, lighting conditions, occlusions, and number of articulations of the human body. Furthermore, this huge pose variability makes the availability of large annotated datasets difficult. In this paper, we introduce the HuPBA8k+ dataset. The dataset contains more than 8000 labeled frames at pixel precision, including more than 120000 manually labeled samples of 14 different limbs. For completeness, the dataset is also labeled at frame-level with action annotations drawn from an 11 action dictionary which includes both single person actions and person-person interactive actions. Furthermore, we also propose a two-stage approach for the segmentation of human limbs. In a first stage, human limbs are trained using cascades of classifiers to be split in a tree-structure way, which is included in an Error-Correcting Output Codes (ECOC) framework to define a body-like probability map. This map is used to obtain a binary mask of the subject by means of GMM color modelling and GraphCuts theory. In a second stage, we embed a similar tree-structure in an ECOC framework to build a more accurate set of limb-like probability maps within the segmented user mask, that are fed to a multi-label GraphCut procedure to obtain final multi-limb segmentation. The methodology is tested on the novel HuPBA8k+ dataset, showing performance improvements in comparison to state-of-the-art approaches. In addition, a baseline of standard action recognition methods for the 11 actions categories of the novel dataset is also provided. |
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HuPBA;MILAB |
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no |
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Admin @ si @ SBE2015 |
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2552 |
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Author |
Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell |
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Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
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Journal Article |
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2016 |
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Chest Journal |
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CHEST |
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150 |
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4 |
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1003A |
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IAM; 600.096; 600.075 |
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no |
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Admin @ si @ DGC2016 |
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3099 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Hand sign language recognition using multi-view hand skeleton |
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2020 |
Publication |
Expert Systems With Applications |
Abbreviated Journal |
ESWA |
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150 |
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113336 |
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Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition |
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Abstract |
Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition. |
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HuPBA; no proj |
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no |
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Admin @ si @ RKE2020a |
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3411 |
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Author |
Carola Figueroa Flores; David Berga; Joost Van de Weijer; Bogdan Raducanu |
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Title |
Saliency for free: Saliency prediction as a side-effect of object recognition |
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2021 |
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Pattern Recognition Letters |
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PRL |
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150 |
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1-7 |
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Saliency maps; Unsupervised learning; Object recognition |
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Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods. |
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LAMP; 600.147; 600.120 |
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no |
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Admin @ si @ FBW2021 |
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3559 |
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Author |
Kai Wang; Joost Van de Weijer; Luis Herranz |
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ACAE-REMIND for online continual learning with compressed feature replay |
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2021 |
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Pattern Recognition Letters |
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PRL |
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150 |
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122-129 |
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online continual learning; autoencoders; vector quantization |
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Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. |
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LAMP; 600.147; 601.379; 600.120; 600.141 |
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Admin @ si @ WWH2021 |
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3575 |
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Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
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Multimodal grid features and cell pointers for scene text visual question answering |
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2021 |
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Pattern Recognition Letters |
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PRL |
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150 |
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242-249 |
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This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link. |
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DAG; 600.084; 600.121 |
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no |
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Admin @ si @ GBT2021 |
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3620 |
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David Vazquez; David Geronimo; Antonio Lopez |
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The effect of the distance in pedestrian detection |
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2009 |
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CVC Technical Report |
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Pedestrian Detection |
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Pedestrian accidents are one of the leading preventable causes of death. In order to reduce the number of accidents, in the last decade the pedestrian protection systems have been introduced, a special type of advanced driver assistance systems, in witch an on-board camera explores the road ahead for possible collisions with pedestrians in order to warn the driver or perform braking actions. As a result of the variability of the appearance, pose and size, pedestrian detection is a very challenging task. So many techniques, models and features have been proposed to solve the problem. As the appearance of pedestrians varies signicantly as a function of distance, a system based on multiple classiers specialized on diferent depths is likely to improve the overall performance with respect to a typical system based on a general detector. Accordingly, the main aim of this work is to explore the eect of the distance in pedestrian detection. We have evaluated three pedestrian detectors (HOG, HAAR and EOH) in two dierent databases (INRIA and Daimler09) for two dierent sizes (small and big). By a extensive set of experiments we answer to questions like which datasets and evaluation methods are the most adequate, which is the best method for each size of the pedestrians and why or how do the method optimum parameters vary with respect to the distance |
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Master's thesis |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams |
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Journal Article |
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2016 |
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Computer Vision and Image Understanding |
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CVIU |
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149 |
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146-156 |
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Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness. |
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MILAB; |
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Admin @ si @ ADR2016b |
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2742 |
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