Examinando por Autor "S. Montemayor, Antonio"

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A non-smooth, non-local variational approach to saliency detection in real time
(2023-12) Alcaín, Eduardo; Muñoz Montalvo, Ana Isabel; Schiavi, Emanuele; S. Montemayor, Antonio
In this paper, we propose and solve numerically a general non-smooth, non-local variational model to tackle the saliency detection problem in natural images. In order to overcome the typical drawback of the non-local methods in image processing, which mainly is the inherent computational complexity of non-local calculus, as the non-local derivatives are computed w.r.t every point of the domain, we propose a diferent scenario. We present a novel convex energy minimization problem in the feature space, which is eficiently solved by means of a non-local primal-dual method. Several implementations and discussions are presented taking care of the computing platforms, CPU and GPU, achieving up to 33 fps and 62 fps respectively for 300×400 image resolution, making the method eligible for real time applications.
Multiview 3D human pose estimation using improved least-squares and LSTM networks
(Elsevier, 2019-01-05) Núñez, Juan Carlos; Cabido, Raúl; Vélez, José F.; S. Montemayor, Antonio; Pantrigo, Juan José
In this paper we present a deep learning based method to estimate the human pose in 3D when multiple 2D views are available. Our system is composed of a cascade of specialized systems. Firstly, 2D poses are obtained using a deep neural network for the detection of skeleton keypoints in each available view. Then, the 3D coordinates of each keypoint are reconstructed with our proposed least squares optimization method, that analyzes the quality of the 2D detections to decide either to consider or reject them. Once the 3D poses are obtained for each time step, full body pose estimation is performed with a long short-term memory (LSTM) neural network, that takes advantage of the process history to refine the final pose estimation. We provide evidence of the suitability of our contributions in an extensive experimental study. Finally, we were able to prove experimentally that our method obtains competitive results when it is compared to recent representative works in the literature.
Pedestrian detection with LeNet-like convolutional networks
(Springer Nature, 2020-09) Cuesta-Infante, Alfredo; García, Francisco J.; Pantrigo, Juan J.; S. Montemayor, Antonio
We present a detection method that is able to detect a learned target and is valid for both static and moving cameras. As an application, we detect pedestrians, but could be anything if there is a large set of images of it. The data set is fed into a number of deep convolutional networks, and then, two of these models are set in cascade in order to filter the cutouts of a multi-resolution window that scans the frames in a video sequence. We demonstrate that the excellent performance of deep convolutional networks is very difficult to match when dealing with real problems, and yet we obtain competitive results.