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Cristina Palmero; Albert Clapes; Chris Bahnsen; Andreas Møgelmose; Thomas B. Moeslund; Sergio Escalera |
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Title |
Multi-modal RGB-Depth-Thermal Human Body Segmentation |
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Journal Article |
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2016 |
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International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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118 |
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2 |
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217-239 |
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Human body segmentation; RGB ; Depth Thermal |
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This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations. |
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Springer US |
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HuPBA;MILAB; |
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no |
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Admin @ si @ PCB2016 |
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2767 |
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Author |
Javier Marin; Sergio Escalera |
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Title |
SSSGAN: Satellite Style and Structure Generative Adversarial Networks |
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2021 |
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Remote Sensing |
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13 |
Issue |
19 |
Pages |
3984 |
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This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area. |
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HUPBA; no proj |
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no |
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Admin @ si @ MaE2021 |
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3651 |
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Author |
Andre Litvin; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Thomas B. Moeslund; Gholamreza Anbarjafari |
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Title |
A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition |
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2019 |
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Multimedia Tools and Applications |
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MTAP |
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78 |
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18 |
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25259–25271 |
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Fully convolutional networks; FusionNet; Thermal imaging; Face recognition |
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This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network. |
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HuPBA; no menciona |
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no |
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Admin @ si @ LNE2019 |
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3318 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Traffic sign recognition system with β -correction |
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2010 |
Publication |
Machine Vision and Applications |
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MVA |
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21 |
Issue |
2 |
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99–111 |
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Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. |
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Springer-Verlag |
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0932-8092 |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2010a |
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1276 |
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Author |
Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund |
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Title |
Back-dropout Transfer Learning for Action Recognition |
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Journal Article |
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Year |
2018 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
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12 |
Issue |
4 |
Pages |
484-491 |
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Keywords |
Learning (artificial intelligence); Pattern Recognition |
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Abstract |
Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate. |
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HUPBA; no proj |
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no |
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Admin @ si @ RKM2018 |
Serial |
3071 |
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