Search

The standard approach to the estimation of homographies consists in the application of the RANSAC algorithm to a set of tentative matches. More recent strategies based on deep learning, namely convolutional architectures, have become available. In this work, a new algorithm for the estimation of homographies is developed. It is rooted in a convolutional neural network for homography estimation, which is provided with a range of versions of the input pair of pictures. Such versions are generated by perturbation of the color levels of the input images. Each generated pair of images yields a distinct estimation of the homography, and then the estimations are combined together to obtain a final, more robust estimation. Experiments have been designed and carried out to test the validity of our approach, including qualitative and quantitative performance measures. In particular, it is demonstrated that our approach consistently outperforms the baseline approach consisting of using the output of the homography estimation deep network for the original input pair of images.

The detection of small objects is one of the problems present in deep learning due to the context of the scene or the low number of pixels of the objects to be detected. According to these problems, current pre-trained models based on convolutional neural networks usually give a poor average precision, highlighting some as CenterNet HourGlass104 with a mean average precision of 25.6%, or SSD-512 with 9%. This work focuses on the detection of small objects. In particular, our proposal aims to vehicle detection from images captured by video surveillance cameras with pretrained models without modifying their structures, so it does not require retraining the network to improve the detection rate of the elements. For better performance, a technique has been developed which, starting from certain initial regions, detects a higher number of objects and improves their class inference without modifying or retraining the network. The neural network is integrated with processes that are in charge of increasing the resolution of the images to improve the object detection performance. This solution has been tested for a set of traffic images containing elements of different scales to check the efficiency depending on the detections obtained by the model. Our proposal achieves good results in a wide range of situations, obtaining, for example, an average score of 45.1% with the EfficientDet-D4 model for the first video sequence, compared to the 24.3% accuracy initially provided by the pre-trained model. This work is partially supported by the Ministry of Science, Innovation and Universities of Spain [grant number RTI2018-094645-B-I00], project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behaviour 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 Málaga (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. The authors acknowledge the funding from the Universidad de Málaga. I.G.-A. is funded by a scholarship from the Autonomous Government of Andalusia (Spain) under the Young Employment operative program [grant number SNGJ5Y6-15]. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. Funding for open access charge: Universidad de Málaga / CBUA.

In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method.

Skin lesions are caused due to multiple factors, like allergies, infections, exposition to the sun, etc. These skin diseases have become a challenge in medical diagnosis due to visual similarities, where image classification is an essential task to achieve an adequate diagnostic of different lesions. Melanoma is one of the best-known types of skin lesions due to the vast majority of skin cancer deaths. In this work, we propose an ensemble of improved convolutional neural networks combined with a test-time regularly spaced shifting technique for skin lesion classification. The shifting technique builds several versions of the test input image, which are shifted by displacement vectors that lie on a regular lattice in the plane of possible shifts. These shifted versions of the test image are subsequently passed on to each of the classifiers of an ensemble. Finally, all the outputs from the classifiers are combined to yield the final result. Experiment results show a significant improvement on the well-known HAM10000 dataset in terms of accuracy and Fscore. In particular, it is demonstrated that our combination of ensembles with test-time regularly spaced shifting yields better performance than any of the two methods when applied alone. This work is partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in 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_02, project name Self-Organizing Neural Systems for Non-Stationary Environments, and B1-2019_01, project name Anomaly detection on roads by moving cameras. The authors 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. The authors acknowledge the funding from the Universidad de Málaga. Funding for open access charge: Universidad de Málaga / CBUA.