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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach |
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Conference Article |
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Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Keywords |
Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks |
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This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors
and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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Las Palmas de Gran Canaria; November 2018 |
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978-1-5386-9385-8 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018a |
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3193 |
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Author |
Jorge Charco; Boris X. Vintimilla; Angel Sappa |
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Title |
Deep learning based camera pose estimation in multi-view environment |
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Conference Article |
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Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Keywords |
Deep learning; Camera pose estimation; Multiview environment; Siamese architecture |
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This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
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Las Palmas de Gran Canaria; November 2018 |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ CVS2018 |
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3194 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
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Conference Article |
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Year |
2018 |
Publication |
25th International Conference on Image Processing |
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2237 - 2241 |
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Keywords |
Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization |
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This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics. |
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Athens; Greece; October 2018 |
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ICIP |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ SSV2018b |
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3195 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Vegetation Index Estimation from Monospectral Images |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th International Conference on Images Analysis and Recognition |
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Volume |
10882 |
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Pages |
353-362 |
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This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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Povoa de Varzim; Portugal; June 2018 |
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ICIAR |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Call Number |
Admin @ si @ SSV2018c |
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3196 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Deep Learning based Single Image Dehazing |
Type |
Conference Article |
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Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop |
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1250 - 12507 |
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Keywords |
Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis |
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Abstract |
This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ SSV2018d |
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3197 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine |
Type |
Journal Article |
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Year |
2018 |
Publication |
Entropy |
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ENTROPY |
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Volume |
20 |
Issue |
11 |
Pages |
809 |
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Keywords |
hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image |
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In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets. |
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HUPBA; no proj |
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no |
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Admin @ si @ RKE2018 |
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3198 |
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Author |
Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez |
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Title |
Top-down model fitting for hand pose recovery in sequences of depth images |
Type |
Journal Article |
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Year |
2018 |
Publication |
Image and Vision Computing |
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IMAVIS |
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79 |
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Pages |
63-75 |
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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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HUPBA; 600.098 |
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Admin @ si @ MEC2018 |
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3203 |
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Author |
Marc Oliu; Javier Selva; Sergio Escalera |
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Title |
Folded Recurrent Neural Networks for Future Video Prediction |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
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Volume |
11218 |
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Pages |
745-761 |
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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. |
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Munich; September 2018 |
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ECCV |
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HUPBA; no menciona |
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no |
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Admin @ si @ OSE2018 |
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3204 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera |
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Title |
Deep Structure Inference Network for Facial Action Unit Recognition |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
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11216 |
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309-324 |
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Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference |
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Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively. |
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Munich; September 2018 |
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ECCV |
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HUPBA; no proj |
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no |
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Admin @ si @ CME2018 |
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3205 |
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Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro |
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Title |
Residual Stacked RNNs for Action Recognition |
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Conference Article |
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2018 |
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9th International Workshop on Human Behavior Understanding |
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534-548 |
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Action recognition; Deep residual learning; Two-stream RNN |
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Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. |
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Munich; September 2018 |
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ECCVW |
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HUPBA; no proj |
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no |
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Admin @ si @ LCE2018b |
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3206 |
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Author |
Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera |
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Title |
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues |
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Conference Article |
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2018 |
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29th British Machine Vision Conference |
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Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included. |
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Newcastle; UK; September 2018 |
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BMVC |
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HUPBA; no proj |
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no |
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Admin @ si @ PSB2018 |
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3208 |
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Author |
Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier |
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Title |
Multimodal First Impression Analysis with Deep Residual Networks |
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Journal Article |
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Year |
2018 |
Publication |
IEEE Transactions on Affective Computing |
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TAC |
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8 |
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3 |
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316-329 |
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People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations |
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HUPBA; no proj |
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Admin @ si @ GGB2018 |
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3210 |
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Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon |
![find record details (via OpenURL) openurl](img/xref.gif)
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Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes |
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2018 |
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Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) |
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Beijing; China; August 2018 |
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ICPRW |
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HUPBA |
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Admin @ si @ RVI2018 |
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3211 |
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Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari |
![goto web page (via DOI) doi](img/doi.gif)
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Review on Emotion Recognition Databases |
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2018 |
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Human-Robot Interaction: Theory and Application |
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emotion; computer vision; databases |
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Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. |
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978-1-78923-316-2 |
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HUPBA; 602.133 |
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Admin @ si @ HRL2018 |
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3212 |
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Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya |
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Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” |
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2018 |
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Informaciones Psiquiatricas |
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232 |
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47-59 |
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0210-7279 |
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HUPBA; no menciona |
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no |
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Admin @ si @ FAA2018 |
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3214 |
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