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Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
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Title |
Monocular Depth Estimation by Learning from Heterogeneous Datasets |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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IEEE Intelligent Vehicles Symposium |
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2176 - 2181 |
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Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ GUH2018 |
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3183 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
![download PDF file pdf](img/file_PDF.gif)
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Title |
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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European Conference on Computer Vision workshops |
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423-433 |
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First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. |
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ECCVW |
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MILAB; no menciona |
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Admin @ si @ SRR2018b |
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3185 |
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Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Automatic Verification of Properly Signed Multi-page Document Images |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2015 |
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Proceedings of the Eleventh International Symposium on Visual Computing |
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9475 |
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327-336 |
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Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow |
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In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. |
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Las Vegas, Nevada, USA; December 2015 |
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9475 |
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ISVC |
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DAG; 600.077 |
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no |
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Admin @ si @ |
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3189 |
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Author |
Xavier Soria; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Improving Edge Detection in RGB Images by Adding NIR Channel |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images |
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The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum. |
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Las Palmas de Gran Canaria; November 2018 |
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MSIAU; 600.122 |
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Admin @ si @ SoS2018 |
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3192 |
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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 |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
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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 |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Deep learning based camera pose estimation in multi-view environment |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
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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 |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
Publication |
25th International Conference on Image Processing |
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2237 - 2241 |
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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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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 ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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15th International Conference on Images Analysis and Recognition |
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10882 |
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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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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 ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop |
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1250 - 12507 |
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Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis |
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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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Admin @ si @ SSV2018d |
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3197 |
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Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
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Title |
Lost in Transcription of Graphic Signs in Ciphers |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2022 |
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International Conference on Historical Cryptology (HistoCrypt 2022) |
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153-158 |
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transcription of ciphers; hand-written text recognition of symbols; graphic signs |
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Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
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Amsterdam, Netherlands, June 20-22, 2022 |
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HystoCrypt |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ MBS2022 |
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3731 |
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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 ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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15th European Conference on Computer Vision |
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11218 |
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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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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 ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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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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Admin @ si @ CME2018 |
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3205 |
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Author |
Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Residual Stacked RNNs for Action Recognition |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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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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Admin @ si @ LCE2018b |
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3206 |
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Author |
Cristina Palmero; Javier Selva; Mohammad Ali Bagueri; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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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 |
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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Title |
Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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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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no |
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Admin @ si @ RVI2018 |
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3211 |
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