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Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa |
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Title |
Fine-tuning based deep convolutional networks for lepidopterous genus recognition |
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Conference Article |
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2016 |
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21st Ibero American Congress on Pattern Recognition |
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467-475 |
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This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. |
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Lima; Perú; November 2016 |
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CIARP |
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ADAS; 600.086 |
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Admin @ si @ CRS2016 |
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2913 |
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Author |
Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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Title |
On Offline Evaluation of Vision-based Driving Models |
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Conference Article |
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2018 |
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15th European Conference on Computer Vision |
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11219 |
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246-262 |
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Autonomous driving; deep learning |
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Autonomous driving models should ideally be evaluated by deploying
them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and
suitable offline metrics. |
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Munich; September 2018 |
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ECCV |
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ADAS; 600.124; 600.118 |
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Admin @ si @ CLK2018 |
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3162 |
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Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska |
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Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation |
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2018 |
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International MICCAI Brainlesion Workshop |
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11384 |
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393-405 |
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Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution |
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In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. |
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MICCAIW |
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ADAS; 600.118 |
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Admin @ si @ PSH2018 |
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3251 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Title |
Single view facial hair 3D reconstruction |
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Conference Article |
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2019 |
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9th Iberian Conference on Pattern Recognition and Image Analysis |
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11867 |
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423-436 |
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3D Vision; Shape Reconstruction; Facial Hair Modeling |
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n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
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Madrid; July 2019 |
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IbPRIA |
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ADAS; 600.086; 600.130; 600.122 |
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Admin @ si @ |
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3707 |
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