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Associate Professor and principal researcher at the UCLM (Univ. de Castilla-La Mancha, Spain) in the Dept. of Electrical and Electronics Engineering since January 1999. Electrical Engineer and PhD on Robotics and Artificial Intelligence from UPM (Univ. Politécnica de Madrid, Spain). He has made his previous research in the IAI-CSIC (Instituto de Automática Industrial, Consejo Superior de Investigaciones Científicas, Spain) and The Robotics Institute (RI), Carnegie Mellon University (CMU, Pittsburgh-USA). He participated in national and international projects in the areas of Distributed Artificial Intelligence and Advanced Robotics. His current interests are focused on the implementation of intelligent systems and computer vision applications.
There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.
Jesus Salido; Vanesa Lomas; Jesus Ruiz-Santaquiteria; Oscar Deniz. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Applied Sciences 2021, 11, 6085 .
AMA StyleJesus Salido, Vanesa Lomas, Jesus Ruiz-Santaquiteria, Oscar Deniz. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Applied Sciences. 2021; 11 (13):6085.
Chicago/Turabian StyleJesus Salido; Vanesa Lomas; Jesus Ruiz-Santaquiteria; Oscar Deniz. 2021. "Automatic Handgun Detection with Deep Learning in Video Surveillance Images." Applied Sciences 11, no. 13: 6085.
Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.
Jesús Salido; Carlos Sánchez; Jesús Ruiz-Santaquiteria; Gabriel Cristóbal; Saul Blanco; Gloria Bueno. A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms. Applied Sciences 2020, 10, 6033 .
AMA StyleJesús Salido, Carlos Sánchez, Jesús Ruiz-Santaquiteria, Gabriel Cristóbal, Saul Blanco, Gloria Bueno. A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms. Applied Sciences. 2020; 10 (17):6033.
Chicago/Turabian StyleJesús Salido; Carlos Sánchez; Jesús Ruiz-Santaquiteria; Gabriel Cristóbal; Saul Blanco; Gloria Bueno. 2020. "A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms." Applied Sciences 10, no. 17: 6033.
This chapter presents the most relevant image processing techniques and algorithms related to computing features that are able to characterize diatoms as objects in the computer vision field for further analysis and classification. For this purpose, a wide revision of the most important contributions to diatom classification is performed. Moreover, features that have been found to be suitable for this task are covered. Later on, the reader will find the main techniques for diatom classification for the two paradigms that are used nowadays: machine learning with classical methods that rely on previously selected features, or deep learning, which learns the features from the images automatically.
Noelia Vallez; Anibal Pedraza; Carlos Sánchez; Jesus Salido; Oscar Deniz; Gloria Bueno. Diatom Feature Extraction and Classification. Modern Trends in Diatom Identification 2020, 151 -164.
AMA StyleNoelia Vallez, Anibal Pedraza, Carlos Sánchez, Jesus Salido, Oscar Deniz, Gloria Bueno. Diatom Feature Extraction and Classification. Modern Trends in Diatom Identification. 2020; ():151-164.
Chicago/Turabian StyleNoelia Vallez; Anibal Pedraza; Carlos Sánchez; Jesus Salido; Oscar Deniz; Gloria Bueno. 2020. "Diatom Feature Extraction and Classification." Modern Trends in Diatom Identification , no. : 151-164.
Deep learning (henceforth DL) has become most powerful machine learning methodology. Under specific circumstances recognition rates even surpass those obtained by humans. Despite this, several works have shown that deep learning produces outputs that are very far from human responses when confronted with the same task. This the case of the so-called “adversarial examples” (henceforth AE). The fact that such implausible misclassifications exist points to a fundamental difference between machine and human learning. This paper focuses on the possible causes of this intriguing phenomenon. We first argue that the error in adversarial examples is caused by high bias, i.e. by regularization that has local negative effects. This idea is supported by our experiments in which the robustness to adversarial examples is measured with respect to the level of fitting to training samples. Higher fitting was associated to higher robustness to adversarial examples. This ties the phenomenon to the trade-off that exists in machine learning between fitting and generalization.
Oscar Deniz; Anibal Pedraza; Noelia Vallez; Jesus Salido; Gloria Bueno. Robustness to adversarial examples can be improved with overfitting. International Journal of Machine Learning and Cybernetics 2020, 11, 935 -944.
AMA StyleOscar Deniz, Anibal Pedraza, Noelia Vallez, Jesus Salido, Gloria Bueno. Robustness to adversarial examples can be improved with overfitting. International Journal of Machine Learning and Cybernetics. 2020; 11 (4):935-944.
Chicago/Turabian StyleOscar Deniz; Anibal Pedraza; Noelia Vallez; Jesus Salido; Gloria Bueno. 2020. "Robustness to adversarial examples can be improved with overfitting." International Journal of Machine Learning and Cybernetics 11, no. 4: 935-944.
This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.
Gloria Bueno; Oscar Deniz; Anibal Pedraza; Jesús Ruiz-Santaquiteria; Jesús Salido; Gabriel Cristóbal; María Borrego-Ramos; Saúl Blanco. Automated Diatom Classification (Part A): Handcrafted Feature Approaches. Applied Sciences 2017, 7, 753 .
AMA StyleGloria Bueno, Oscar Deniz, Anibal Pedraza, Jesús Ruiz-Santaquiteria, Jesús Salido, Gabriel Cristóbal, María Borrego-Ramos, Saúl Blanco. Automated Diatom Classification (Part A): Handcrafted Feature Approaches. Applied Sciences. 2017; 7 (8):753.
Chicago/Turabian StyleGloria Bueno; Oscar Deniz; Anibal Pedraza; Jesús Ruiz-Santaquiteria; Jesús Salido; Gabriel Cristóbal; María Borrego-Ramos; Saúl Blanco. 2017. "Automated Diatom Classification (Part A): Handcrafted Feature Approaches." Applied Sciences 7, no. 8: 753.
This work describes a mobile application (Sainet) for image processing as an assistive technology devoted to visually impaired users. The app is targeted to the Android platform and usually executed in a mobile device equipped with a back camera for image acquisition. Moreover, a wireless bluetooth headphone provides the audio feedback to the user. Sainet has been conceived as an assistance tool to the user in a social interaction scenario. It is capable of providing audible information about the number and position (distance and orientation) of the interlocutors in the user frontal scenario. For validation purposes the app has been tested by a blind user who has provided valuable insights about its strengths and weaknesses.
Jesus Salido; Oscar Deniz; Gloria Bueno. Sainet: An Image Processing App for Assistance of Visually Impaired People in Social Interaction Scenarios. Lecture Notes in Computer Science 2016, 467 -477.
AMA StyleJesus Salido, Oscar Deniz, Gloria Bueno. Sainet: An Image Processing App for Assistance of Visually Impaired People in Social Interaction Scenarios. Lecture Notes in Computer Science. 2016; ():467-477.
Chicago/Turabian StyleJesus Salido; Oscar Deniz; Gloria Bueno. 2016. "Sainet: An Image Processing App for Assistance of Visually Impaired People in Social Interaction Scenarios." Lecture Notes in Computer Science , no. : 467-477.
Current indoor navigation solutions face many problems in hospitals. Heavy electro-magnetic interference from hospital equipment affects the compass on a mobile device. Existing infrastructure may also need to be replaced or modified to provide better signal triangulation indoors. Current solutions are challenging in terms of cost (for both hospitals and users) and disruption of normal operation. In this context, this paper introduces the patent-pending Smart Indoor navigation and way finding system. With Smart Indoor, the user only has to point his smartphone to any sign inside the hospital. The recognized text is sent to a server which contains a database of sign texts along with their position inside the building. This paper describes the computer vision task of recognizing texts with a smartphone, arguably the most important module of the whole system. Preliminary results show that the proposed approach can be effective for such Smartphone-based indoor navigation system.
Oscar Deniz; Julio Paton; Jesus Salido; Gloria Bueno; Janahan Ramanan. A Vision-Based Localization Algorithm for an Indoor Navigation App. 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies 2014, 7 -12.
AMA StyleOscar Deniz, Julio Paton, Jesus Salido, Gloria Bueno, Janahan Ramanan. A Vision-Based Localization Algorithm for an Indoor Navigation App. 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies. 2014; ():7-12.
Chicago/Turabian StyleOscar Deniz; Julio Paton; Jesus Salido; Gloria Bueno; Janahan Ramanan. 2014. "A Vision-Based Localization Algorithm for an Indoor Navigation App." 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies , no. : 7-12.
J. Salido-Tercero; G. Bueno; O. Deniz. EP-1692: Phantom to patient registration applied to dosimetry. Radiotherapy and Oncology 2014, 111, S244 .
AMA StyleJ. Salido-Tercero, G. Bueno, O. Deniz. EP-1692: Phantom to patient registration applied to dosimetry. Radiotherapy and Oncology. 2014; 111 ():S244.
Chicago/Turabian StyleJ. Salido-Tercero; G. Bueno; O. Deniz. 2014. "EP-1692: Phantom to patient registration applied to dosimetry." Radiotherapy and Oncology 111, no. : S244.
Gloria Bueno; Maria-Milagro Fernández-Carrobles; Oscar Déniz; Jesús Salido; Noelia Vállez; Marcial García-Rojo. An entropy-based automated approach to prostate biopsy ROI segmentation. Diagnostic Pathology 2013, 8, S24 -S24.
AMA StyleGloria Bueno, Maria-Milagro Fernández-Carrobles, Oscar Déniz, Jesús Salido, Noelia Vállez, Marcial García-Rojo. An entropy-based automated approach to prostate biopsy ROI segmentation. Diagnostic Pathology. 2013; 8 (S1):S24-S24.
Chicago/Turabian StyleGloria Bueno; Maria-Milagro Fernández-Carrobles; Oscar Déniz; Jesús Salido; Noelia Vállez; Marcial García-Rojo. 2013. "An entropy-based automated approach to prostate biopsy ROI segmentation." Diagnostic Pathology 8, no. S1: S24-S24.
An essential and indispensable component of automated microscopy framework is the automatic focusing system, which determines the in-focus position of a given field of view by searching the maximum value of a focusing function over a range of z-axis positions. The focus function and its computation time are crucial to the accuracy and efficiency of the system. Sixteen focusing algorithms were analyzed for histological and histopathological images. In terms of accuracy, results have shown an overall high performance by most of the methods. However, we included in the evaluation study other criteria such as computational cost and focusing curve shape which are crucial for real-time applications and were used to highlight the best practices.
Rafael Redondo; Gloria Bueno; Juan Carlos Valdiviezo; Rodrigo Nava; Gabriel Cristobal; Oscar Deniz; Marcial García-Rojo; Jesus Salido; Maria Del Milagro Fernández; Juan Vidal; Boris Escalante-Ramírez. Autofocus evaluation for brightfield microscopy pathology. Journal of Biomedical Optics 2012, 17, 0360081 -0360088.
AMA StyleRafael Redondo, Gloria Bueno, Juan Carlos Valdiviezo, Rodrigo Nava, Gabriel Cristobal, Oscar Deniz, Marcial García-Rojo, Jesus Salido, Maria Del Milagro Fernández, Juan Vidal, Boris Escalante-Ramírez. Autofocus evaluation for brightfield microscopy pathology. Journal of Biomedical Optics. 2012; 17 (3):0360081-0360088.
Chicago/Turabian StyleRafael Redondo; Gloria Bueno; Juan Carlos Valdiviezo; Rodrigo Nava; Gabriel Cristobal; Oscar Deniz; Marcial García-Rojo; Jesus Salido; Maria Del Milagro Fernández; Juan Vidal; Boris Escalante-Ramírez. 2012. "Autofocus evaluation for brightfield microscopy pathology." Journal of Biomedical Optics 17, no. 3: 0360081-0360088.
. In this paper, we present a new approach for continuous probabilisticmapping. The objective is to build metric maps of unknown environments throughcooperation between multiple autonomous mobile robots. The approach is based onaBayesian update rule that can be used to integrate the range sensing data comingfrom multiple sensors on multiple robots. In addition, the algorithm is fast andcomputationally inexpensive so that it can be implemented on small robots withlimited computation...
Jesus Salido Tercero; Christiaan J. J. Paredis; Pradeep K. Khosla. Continuous probabilistic mapping by autonomous robots. Sensing and Control for Autonomous Vehicles 2008, 275 -286.
AMA StyleJesus Salido Tercero, Christiaan J. J. Paredis, Pradeep K. Khosla. Continuous probabilistic mapping by autonomous robots. Sensing and Control for Autonomous Vehicles. 2008; ():275-286.
Chicago/Turabian StyleJesus Salido Tercero; Christiaan J. J. Paredis; Pradeep K. Khosla. 2008. "Continuous probabilistic mapping by autonomous robots." Sensing and Control for Autonomous Vehicles , no. : 275-286.