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Edgar Riba
Researcher at Autonomous University of Barcelona
Publications - 10
Citations - 1143
Edgar Riba is an academic researcher from Autonomous University of Barcelona. The author has contributed to research in topics: Edge detection & Camera resectioning. The author has an hindex of 6, co-authored 9 publications receiving 584 citations.
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Proceedings ArticleDOI
Learning local feature descriptors with triplets and shallow convolutional neural networks.
TL;DR: This work proposes to utilize triplets of training samples, together with in-triplet mining of hard negatives, and shows that this method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture.
Proceedings ArticleDOI
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters
TL;DR: In this article, a shallow multi-scale architecture is proposed for keypoint detection, which combines handcrafted and learned CNN filters within a shallow multiscale architecture, which localize, score and rank repeatable features.
Proceedings ArticleDOI
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
TL;DR: In this paper, a deep learning based edge detector is proposed, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks; the proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process.
Proceedings ArticleDOI
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
TL;DR: Kornia as mentioned in this paper is an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems, such as image transformations, camera calibration, epipolar geometry, and low level image processing techniques.
Posted Content
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
TL;DR: Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations.