@article {aLin19,
	title = {Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network},
	journal = {Sensors},
	volume = {19},
	year = {2019},
	month = {04/2019},
	chapter = {1795},
	abstract = {<p>Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.</p>},
	keywords = {depth estimation; semantic segmentation; convolutional neural networks; hybrid architecture},
	issn = {1424-8220},
	doi = {10.3390/s19081795},
	url = {https://www.mdpi.com/1424-8220/19/8/1795},
	author = {X. Lin and D. Sanchez-Escobedo and Casas, J. and M. Pard{\`a}s}
}
@conference {cSanchez-Escobedo,
	title = {HybridNet for Depth Estimation and Semantic Segmentation},
	booktitle = {ICASSP 2018},
	year = {2018},
	month = {04/2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Calgary, Alberta, Canada},
	abstract = {<p>Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation, depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network. Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks, the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both. Experimental results demonstrate that HybridNet results are comparable with the state of the art methods, as well as the single task methods that HybridNet is based on.</p>},
	doi = {10.1109/ICASSP.2018.8462433},
	url = {https://ieeexplore.ieee.org/abstract/document/8462433},
	author = {D. Sanchez-Escobedo and X. Lin and Casas, J. and M. Pard{\`a}s}
}
@article {aPetrone17,
	title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening},
	journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association},
	volume = {13},
	year = {2017},
	month = {07/2017},
	pages = {P1245},
	doi = {10.1016/j.jalz.2017.07.457},
	url = {https://doi.org/10.1016/j.jalz.2017.07.457},
	author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and D. Sanchez-Escobedo and A. Tucholka and R. Cacciaglia and G. Operto and S. Skouras and C. Falcon and J.L. Molinuevo and J.D. Gispert}
}
