Vehicle Type Detection by Convolutional Neural Networks

In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks

Enhancing resolution is a permanent goal in magnetic resonance (MR) imaging, in order to keep improving diagnostic capability and registration methods. Super-resolution (SR) techniques are applied at the postprocessing stage, and their use and development have progressively increased during the last years. In particular, example-based methods have been mostly proposed in recent state-of-the-art works. In this paper, a combination of a deep-learning SR system and a random shifting technique to improve the quality of MR images is proposed, implemented and tested. The model was compared to four competitors: cubic spline interpolation, non-local means upsampling, low-rank total variation and a three-dimensional convolutional neural network trained with patches of HR brain images (SRCNN3D). The newly proposed method showed better results in Peak Signal-to-Noise Ratio, Structural Similarity index, and Bhattacharyya coefficient. Computation times were at the same level as those of these up-to-date methods. When applied to downsampled MR structural T1 images, the new method also yielded better qualitative results, both in the restored images and in the images of residuals.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Road pollution estimation using static cameras and neural networks

Este artículo presenta una metodología para estimar la contaminación en carreteras mediante el análisis de secuencias de video de tráfico. El objetivo es aprovechar la gran red de cámaras IP existente en el sistema de carreteras de cualquier estado o país para estimar la contaminación en cada área. Esta propuesta utiliza redes neuronales de aprendizaje profundo para la detección de objetos, y un modelo de estimación de contaminación basado en la frecuencia de vehículos y su velocidad. Los experimentos muestran prometedores resultados que sugieren que el sistema se puede usar en solitario o combinado con los sistemas existentes para medir la contaminación en carreteras.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks

https://doi.org/10.1007/978-3-319-77712-2_62The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Small and large intestine express a truncated Dab1 isoform that assembles in cell-cell junctions and co-localizes with proteins involved in endocytosis

Disabled-1 (Dab1) is an essential intracellular adaptor protein in the reelin pathway. Our previous studies in mice intestine showed that Dab1 transmits the reelin signal to cytosolic signalling pathways. Here, we determine the Dab1 isoform expressed in rodent small and large intestine, its subcellular location and co-localization with clathrin, caveolin-1 and N-Wasp. PCR and sequencing analysis reveal that rodent small and large intestine express a Dab1 isoform that misses three (Y198, Y200 and Y220) of the five tyrosine phosphorylation sites present in brain Dab1 isoform (canonical) and contains nuclear localization and export signals. Western blot assays show that both, crypts, which shelter progenitor cells, and enterocytes express the same Dab1 isoform, suggesting that epithelial cell differentiation does not regulate intestinal generation of alternatively spliced Dab1 variants. They also reveal that the canonical and the intestinal Dab1 isoforms differ in their total degree of phosphorylation. Immunostaining assays show that in enterocytes Dab1 localizes at the apical and lateral membranes, apical vesicles, close to adherens junctions and desmosomes, as well as in the nucleus; co-localizes with clathrin and with N-Wasp but not with caveolin-1, and in Caco-2 cells Dab1 localizes at cell-to-cell junctions by a Ca2+-dependent process. In conclusion, the results indicate that in rodent intestine a truncated Dab1 variant transmits the reelin signal and may play a role in clathrin-mediated apical endocytosis and in the control of cell-to-cell junction assembly. A function of intestinal Dab1 variant as a nucleocytoplasmic shuttling protein is also inferred from its sequence and nuclear location.Junta de Andalucía CTS 5884Ministerio de Educación y Ciencia AP2007-04201European Molecular Biology Organization ASTF45-201

Super- resolution of 3D MRI corrupted by heavy noise with the median filter transform

The acquisition of 3D MRIs is adversely affected by many degrading factors including low spatial resolution and noise. Image enhancement techniques are commonplace, but there are few proposals that address the increase of the spatial resolution and noise removal at the same time. An algorithm to address this vital need is proposed in this presented work. The proposal tiles the 3D image space into parallelepipeds, so that a median filter is applied in each parallelepiped. The results obtained from several such tilings are then combined by a subsequent median computation. The convergence properties of the proposed method are formally proved. Experimental results with both synthetic and real images demonstrate our approach outperforms its competitors for images with high noise levels. Moreover, it is demonstrated that our algorithm does not generate any hallucinations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting

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

Excitation by Axon Terminal GABA Spillover in a Sound Localization Circuit

Synapses from neurons of the medial nucleus of the trapezoid body (MNTB) onto neurons of the lateral superior olive (LSO) in the auditory brainstem are glycinergic in maturity, but also GABAergic and glutamatergic in development. The role for this neurotransmitter cotransmission is poorly understood. Here we use electrophysiological recordings in brainstem slices from P3-P21 mice to demonstrate that GABA release evoked from MNTB axons can spill over to neighboring MNTB axons and cause excitation by activating GABAAR. This spillover excitation generates patterns of staggered neurotransmitter release from different MNTB axons resulting in characteristic “doublet” postsynaptic currents in LSO neurons. Postembedding immunogold labeling and electron microscopy provide evidence that GABAARs are localized at MNTB axon terminals. Photolytic uncaging of p-hydroxyphenacyl (pHP) GABA demonstrates backpropagation of GABAAR-mediated depolarizations from MNTB axon terminals to the soma, some hundreds of microns away. These somatic depolarizations enhanced somatic excitability by increasing the probability of action potential generation. GABA spillover excitation between MNTB axon terminals may entrain neighboring MNTB neurons, which may play a role in the developmental refinement of the MNTB-LSO pathway. Axonal spillover excitation persisted beyond the second postnatal week, suggesting that this mechanism may play a role in sound localization, by providing new avenues of communication between MNTB neurons via their distal axonal projections. SIGNIFICANCE STATEMENT In this study, a new mechanism of neuronal communication between auditory synapses in the mammalian sound localization pathway is described. Evidence is provided that the inhibitory neurotransmitter GABA can spill over between axon terminals to cause excitation of nearby synapses to further stimulate neurotransmitter release. Excitatory GABA spillover between inhibitory axon terminals may have important implications for the development and refinement of this auditory circuit and may play a role in the ability to precisely localize sound sources