Gilberto Ochoa‐Ruiz

Tecnológico de Monterrey

Author Statistics

Papers

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H-Index

i-10 index

Research Trends

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Document Type

Co-Authors

Miguel González-Mendoza

Tecnológico de Monterrey

Christian Daul

Université de Lorraine

Daniel Flores-Araiza

Tecnológico de Monterrey

Andres Méndez-Vázquez

Center for Research and Advanced Studies of the National Polytechnic Institute

Francisco Lopez-Tiro

Tecnológico de Monterrey

Jacques Hubert

Centre Hospitalier Régional et Universitaire de Nancy

Ivan Reyes-Amezcua

Center for Research and Advanced Studies of the National Polytechnic Institute

El‐Bay Bourennane

ImViA - Imagerie et Vision Artificielle

Sharib Ali

University of Leeds

Christian Mata

Hospital Sant Joan de Déu Barcelona

Cooperative Institutions

Tecnológico de Monterrey

Centre National de la Recherche Scientifique

Instituto Politécnico Nacional

Center for Research and Advanced Studies of the National Polytechnic Institute

Université de Lorraine

Universidad Autónoma de Guadalajara

Université de Bourgogne

Tecnológico Nacional de México

Université de Bretagne Occidentale

Universidad de Guadalajara

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management

Lecture notes in computer science (2019)

Andres Angulo Juan Antonio Vega-Fernández Lina María Aguilar-Lobo Shailendra Natraj Gilberto Ochoa‐Ruiz

Benchmark (surveying)

10.1007/978-3-030-33749-0_1

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Citations (32)

Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach

arXiv (Cornell University) (2022)

Carmina Pérez-Guerrero Jorge Francisco Ciprián-Sánchez Adriana Palacios Gilberto Ochoa‐Ruiz Miguel González-Mendoza

Among the different kinds of fire accidents that can occur during industrial activities that involve hazardous materials, jet fires are one of the lesser-known types. This is because they are often involved in a process that generates a sequence of other accidents of greater magnitude, known as domino effect. Flame impingement usually causes domino effects, and jet fires present specific features that can significantly increase the probability of this happening. These features become relevant from a risk analysis perspective, making their proper characterization a crucial task. Deep Learning approaches have become extensively used for tasks such as jet fire characterization; however, these methods are heavily dependent on the amount of data and the quality of the labels. Data acquisition of jet fires involve expensive experiments, especially so if infrared imagery is used. Therefore, this paper proposes the use of Generative Adversarial Networks to produce plausible infrared images from visible ones, making experiments less expensive and allowing for other potential applications. The results suggest that it is possible to realistically replicate the results for experiments carried out using both visible and infrared cameras. The obtained results are compared with some previous experiments, and it is shown that similar results were obtained.

Replicate

Domino

Characterization

10.48550/arxiv.2206.02110

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Citations (0)

Finding Significant Features for Few-Shot Learning using Dimensionality Reduction.

arXiv (Cornell University) (2021)

Mauricio Méndez-Ruiz Ivan Garcia Jorge Gonzalez-Zapata Gilberto Ochoa‐Ruiz Andres Méndez-Vázquez

Few-shot learning is a relatively new technique that specializes in problems where we have little amounts of data. The goal of these methods is to classify categories that have not been seen before with just a handful of samples. Recent approaches, such as metric learning, adopt the meta-learning strategy in which we have episodic tasks conformed by support (training) data and query (test) data. Metric learning methods have demonstrated that simple models can achieve good performance by learning a similarity function to compare the support and the query data. However, the feature space learned by a given metric learning approach may not exploit the information given by a specific few-shot task. In this work, we explore the use of dimension reduction techniques as a way to find task-significant features helping to make better predictions. We measure the performance of the reduced features by assigning a score based on the intra-class and inter-class distance, and selecting a feature reduction method in which instances of different classes are far away and instances of the same class are close. This module helps to improve the accuracy performance by allowing the similarity function, given by the metric learning method, to have more discriminative features for the classification. Our method outperforms the metric learning baselines in the miniImageNet dataset by around 2% in accuracy performance.

Discriminative model

Similarity (geometry)

Feature (linguistics)

Feature vector

Source

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Citations (0)

Comparing ML based Segmentation Models on Jet Fire Radiation Zone.

arXiv (Cornell University) (2021)

Carmina Pérez-Guerrero Adriana Palacios Gilberto Ochoa‐Ruiz Christian Mata Miguel González-Mendoza

Risk assessment is relevant in any workplace, however there is a degree of unpredictability when dealing with flammable or hazardous materials so that detection of fire accidents by itself may not be enough. An example of this is the impingement of jet fires, where the heat fluxes of the flame could reach nearby equipment and dramatically increase the probability of a domino effect with catastrophic results. Because of this, the characterization of such fire accidents is important from a risk management point of view. One such characterization would be the segmentation of different radiation zones within the flame, so this paper presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem. A data set of propane jet fires is used to train and evaluate the different approaches and given the difference in the distribution of the zones and background of the images, different loss functions, that seek to alleviate data imbalance, are also explored. Additionally, different metrics are correlated to a manual ranking performed by experts to make an evaluation that closely resembles the expert's criteria. The Hausdorff Distance and Adjsted Random Index were the metrics with the highest correlation and the best results were obtained from the UNet architecture with a Weighted Cross-Entropy Loss. These results can be used in future research to extract more geometric information from the segmentation masks or could even be implemented on other types of fire accidents.

Flammable liquid

Source

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Citations (0)

LatinX in AI at Computer Vision and Pattern Recognition Conference 2022

Jorge Gonzalez-Zapata Ivan Reyes-Amezcua Daniel Flores-Araiza Mauricio Méndez-Ruiz Gilberto Ochoa‐Ruiz

Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning.However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused by a distributional shift in the data.These represent a significant drawback when attempting to learn the underlying data manifold.Therefore, there is a pressing need to develop better ways of obtaining generalization and representation of the underlying manifold.In this paper, we propose a novel approach to DML that we call Guided Deep Metric Learning, a novel architecture oriented to learning more compact clusters, improving generalization under distributional shifts in DML.This novel architecture consists of two independent models: A multi-branch master model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced hypothesis space based on prior knowledge from labeled data, which guides or regularizes the decision boundary of a student model during training under an offline knowledge distillation scheme.Experiments have shown that the proposed method is capable of a better manifold generalization and representation to up to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave et al. to perform a more fair and realistic comparison, which is currently absent in the literature.

10.52591/lxai202206241

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On the in vivo recognition of kidney stones using machine learning

arXiv (Cornell University) (2022)

Gilberto Ochoa‐Ruiz V. Estrade Francisco J. Martínez‐López Daniel Flores-Araiza Jonathan El Beze

Determining the type of kidney stones allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies. This contribution details the database construction and the design of the tested kidney stones classifiers. Even if the best results were obtained by the Inception v3 architecture (weighted precision, recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted precision, recall and F1-score values of 0.96). This paper is the first one that explores the most discriminant features to be extracted from images acquired during ureteroscopies.

10.48550/arxiv.2201.08865

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Citations (5)

EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction

Lecture notes in computer science (2024)

Ivan Reyes-Amezcua Ricardo Espinosa Christian Daul Gilberto Ochoa‐Ruiz Andres Méndez-Vázquez

Robustness

Benchmark (surveying)

Strengths and weaknesses

Monocular

10.1007/978-3-031-73748-0_9

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Citations (0)

An FPGA Smart Camera Implementation of Segmentation Models for Drone Wildfire Imagery

Computación y Sistemas (2023)

Eduardo Garduño Jorge Francisco Ciprián-Sánchez Valente Vazquez-Garcia Miguel González-Mendoza Gerardo Rodriguez‐Hernandez

Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels. Thus, a significant amount of research has been carried out to investigate and apply computer vision techniques to address this problem. One of the most promising approaches for wildfire fighting is the use of drones equipped with visible and infrared cameras for the detection, monitoring, and fire spread assessment in a remote manner but in close proximity to the affected areas. However, implementing effective computer vision algorithms on board is often prohibitive since deploying full-precision deep learning models running on GPU is not a viable option, due to their high power consumption and the limited payload a drone can handle. Thus, in this work, we posit that smart cameras, based on low-power consumption field-programmable gate arrays (FPGAs), in tandem with binarized neural networks (BNNs), represent a cost-effective alternative for implementing onboard computing on the edge. Herein we present the implementation of a segmentation model applied to the Corsican Fire Database. We optimized an existing U-Net model for such a task and ported the model to an edge device (a Xilinx Ultra96-v2 FPGA). By pruning and quantizing the original model, we reduce the number of parameters by 90%. Furthermore, additional optimizations enabled us to increase the throughput of the original model from 8 frames per second (FPS) to 33.63 FPS without loss in the segmentation performance: our model obtained 0.912 in Matthews correlation coefficient (MCC), 0.915 in F1 score and 0.870 in Hafiane quality index (HAF), and comparable qualitative segmentation results when contrasted to the original full-precision model. The final model was integrated into a low-cost FPGA, which was used to implement a neural network accelerator.

Drone

Smart camera

10.13053/cys-27-4-4773

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Citations (1)

Run-Time Scalable NoC for FPGA Based Virtualized IPs

Hiliwi Leake Kidane El‐Bay Bourennane Gilberto Ochoa‐Ruiz

The integration of virtualized FPGA-based hardware accelerators in a cloud computing is progressing from time to time. As the FPGA has limited resources, the dynamic partial reconfiguration capability of the FPGA is considered to share resources among different virtualized IPs during runtime. On the other hand, the NoC is a promising solution for communication among virtualized FPGA-based IPs. However, not all the virtualized regions of the FPGA will be active all the time. When there is no demand for virtualized IPs, the virtualized regions are loaded with blank bitstreams to save power. However, keeping active the idle components of the NoC connecting with the idle virtualized regions is not optimal for the total power consumption. In this paper, we have proposed a runtime scalable NoC where the NoC size is scaled up and down on demand. The experimental result shows that the total power consumption of the NoC can be reduced by more than 15% if the runtime scalable Noc is used.

Control reconfiguration

Idle

10.1109/mcsoc.2017.32

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Citations (4)

A high-level methodology for automatically generating dynamic partially reconfigurable systems using IP-XACT and the UML MARTE profile

Design Automation for Embedded Systems (2012)

Gilberto Ochoa‐Ruiz Ouassila Labbani El‐Bay Bourennane Philippe Soulard Sana Cherif

Design flow

Control reconfiguration

Executable

Netlist

Software portability

10.1007/s10617-012-9098-6

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Citations (7)