Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms

One of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortality rate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer aided diagnosis by intelligent image analysis can help in the diagnosis tasks associated with this disease. Here we propose an automated system for histopathological image analysis that is based on deep learning neural networks with convolutional layers. Rather than a single network, an ensemble of them is built so as to attain higher recognition rates, which are obtained by computing a consensus decision from the individual networks of the ensemble. A final step involves the optimization of the set of networks that are included in the ensemble by a genetic algorithm. Experimental results are provided with a set of benchmark images, with favorable outcomes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Automate d lab eling of training data for improved object detection in traffic videos by fine-tuned deep convolutional neural networks

The exponential increase in the use of technology in road management systems has led to real-time vi- sual information in thousands of locations on road networks. A previous step in preventing or detecting accidents involves identifying vehicles on the road. The application of convolutional neural networks in object detection has significantly improved this field, enhancing classical computer vision techniques. Al- though, there are deficiencies due to the low detection rate provided by the available pre-trained models, especially for small objects. The main drawback is that they require manual labeling of the vehicles that appear in the images from each IP camera located on the road network to retrain the model. This task is not feasible if we have thousands of cameras distributed across the extensive road network of each nation or state. Our proposal presented a new automatic procedure for detecting small-scale objects in traffic sequences. In the first stage, vehicle patterns detected from a set of frames are generated automatically through an offline process, using super-resolution techniques and pre-trained object detection networks. Subsequently, the object detection model is retrained with the previously obtained data, adapting it to the analyzed scene. Finally, already online and in real-time, the retrained model is used in the rest of the traffic sequence or the video stream generated by the camera. This framework has been successfully tested on the NGSIM and the GRAM datasets.Funding for open access charge: Universidad de Málaga/CBU

Generative Adversarial Network and Its Application in Aerial Vehicle Detection and Biometric Identification System

In recent years, generative adversarial networks (GANs) have shown great potential in advancing the state-of-the-art in many areas of computer vision, most notably in image synthesis and manipulation tasks. GAN is a generative model which simultaneously trains a generator and a discriminator in an adversarial manner to produce real-looking synthetic data by capturing the underlying data distribution. Due to its powerful ability to generate high-quality and visually pleasingresults, we apply it to super-resolution and image-to-image translation techniques to address vehicle detection in low-resolution aerial images and cross-spectral cross-resolution iris recognition. First, we develop a Multi-scale GAN (MsGAN) with multiple intermediate outputs, which progressively learns the details and features of the high-resolution aerial images at different scales. Then the upscaled super-resolved aerial images are fed to a You Only Look Once-version 3 (YOLO-v3) object detector and the detection loss is jointly optimized along with a super-resolution loss to emphasize target vehicles sensitive to the super-resolution process. There is another problem that remains unsolved when detection takes place at night or in a dark environment, which requires an IR detector. Training such a detector needs a lot of infrared (IR) images. To address these challenges, we develop a GAN-based joint cross-modal super-resolution framework where low-resolution (LR) IR images are translated and super-resolved to high-resolution (HR) visible (VIS) images before applying detection. This approach significantly improves the accuracy of aerial vehicle detection by leveraging the benefits of super-resolution techniques in a cross-modal domain. Second, to increase the performance and reliability of deep learning-based biometric identification systems, we focus on developing conditional GAN (cGAN) based cross-spectral cross-resolution iris recognition and offer two different frameworks. The first approach trains a cGAN to jointly translate and super-resolve LR near-infrared (NIR) iris images to HR VIS iris images to perform cross-spectral cross-resolution iris matching to the same resolution and within the same spectrum. In the second approach, we design a coupled GAN (cpGAN) architecture to project both VIS and NIR iris images into a low-dimensional embedding domain. The goal of this architecture is to ensure maximum pairwise similarity between the feature vectors from the two iris modalities of the same subject. We have also proposed a pose attention-guided coupled profile-to-frontal face recognition network to learn discriminative and pose-invariant features in an embedding subspace. To show that the feature vectors learned by this deep subspace can be used for other tasks beyond recognition, we implement a GAN architecture which is able to reconstruct a frontal face from its corresponding profile face. This capability can be used in various face analysis tasks, such as emotion detection and expression tracking, where having a frontal face image can improve accuracy and reliability. Overall, our research works have shown its efficacy by achieving new state-of-the-art results through extensive experiments on publicly available datasets reported in the literature

Vehicle overtaking hazard detection over onboard cameras using deep convolutional networks

The development of artificial vision systems to support driving has been of great interest in recent years, especially after new learning models based on deep learning. In this work, a framework is proposed for detecting road speed anomalies, taking as reference the driving vehicle. The objective is to warn the driver in realtime that a vehicle is overtaking dangerously to prevent a possible accident. Thus, taking the information captured by the rear camera integrated into the vehicle, the system will automatically determine if the overtaking that other vehicles make is considered abnormal or dangerous or is considered normal. Deep learning-based object detection techniques will be used to detect the vehicles in the road image. Each detected vehicle will be tracked over time, and its trajectory will be analyzed to determine the approach speed. Finally, statistical regression techniques will estimate the degree of anomaly or hazard of said overtaking as a preventive measure. This proposal has been tested with a significant set of actual road sequences in different lighting conditions with very satisfactory results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques

L'abstract è presente nell'allegato / the abstract is in the attachmen

Homography estimation with deep convolutional neural networks by random color transformations

Most classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks

Air quality and reduction of emissions in the transport sector are determinant factors in achieving a sustainable global climate. The monitoring of emissions in traffic routes can help to improve route planning and to design strategies that may make the pollution levels to be reduced. In this work, a method which detects the pollution levels of transport vehicles from the images of IP cameras by means of computer vision techniques and neural networks is proposed. Specifically, for each sequence of images, a homography is calculated to correct the camera perspective and determine the real distance for each pixel. Subsequently, the trajectory of each vehicle is computed by applying convolutional neural networks for object detection and tracking algorithms. Finally, the speed in each frame and the pollution emitted by each vehicle are determined. Experimental results on several datasets available in the literature support the feasibility and scalability of the system as an emission control strategy.This work is partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, roject name ‘‘Automated detection with low-cost hardware of unusual activities n video sequences’’. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name ‘‘Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems’’. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Malaga (Spain) under grants B1-2019_01, project name ‘‘Anomaly detection on roads by moving cameras’’, and B1-2019_02, project name ‘‘Self-Organizing Neural Systems for Non-Stationary Environments’’. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga.thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. Finally, the authors thankfully acknowledge the grant of the Universidad de Málaga and the Instituto de Investigación Biomédica de Málaga - IBIMA. Funding for Open Access charge: University of Málaga/CBU

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?

Current technology for autonomous cars primarily focuses on getting the passenger from point A to B. Nevertheless, it has been shown that passengers are afraid of taking a ride in self-driving cars. One way to alleviate this problem is by allowing the passenger to give natural language commands to the car. However, the car can misunderstand the issued command or the visual surroundings which could lead to uncertain situations. It is desirable that the self-driving car detects these situations and interacts with the passenger to solve them. This paper proposes a model that detects uncertain situations when a command is given and finds the visual objects causing it. Optionally, a question generated by the system describing the uncertain objects is included. We argue that if the car could explain the objects in a human-like way, passengers could gain more confidence in the car's abilities. Thus, we investigate how to (1) detect uncertain situations and their underlying causes, and (2) how to generate clarifying questions for the passenger. When evaluating on the Talk2Car dataset, we show that the proposed model, \acrfull{pipeline}, improves \gls{m:ambiguous-absolute-increase} in terms of $IoU_{.5}$ compared to not using \gls{pipeline}. Furthermore, we designed a referring expression generator (REG) \acrfull{reg_model} tailored to a self-driving car setting which yields a relative improvement of \gls{m:meteor-relative} METEOR and \gls{m:rouge-relative} ROUGE-l compared with state-of-the-art REG models, and is three times faster.Comment: Accepted in Engineering Applications of Artificial Intelligence (EAAI) journa