Die Untersuchung der Überlebensfähigkeit von infektionsfähigen Endoparasitenlarven in Silagen unterschiedlicher Qualität

The aim of this study was to analyze the influence of silage quality and duration of ensiling to the recovery of infectious larvae of endoparasites. 48 silage bags of different quality were prepaired (gras, gras and earth, gras and silage additive, gras and earth and silage additive). Third larvae of a parasite larvae mixture were added. Larvae were recovered by the methode of Baermann-Wetzel. Significant differences were seen between groups in the larval recovery with more recovery in groups with additional earth. Differences in pH were ambivalent, but samples without added earth were at least numerically better

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

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

Analysis and recognition of human gait activity based on multimodal sensors

Remote health monitoring plays a significant role in research areas related to medicine, neurology, rehabilitation, and robotic systems. These applications include Human Activity Recognition (HAR) using wearable sensors, signal processing, mathematical methods, and machine learning to improve the accuracy of remote health monitoring systems. To improve the detection and accuracy of human activity recognition, we create a novel method to reduce the complexities of extracting features using the HuGaDB dataset. Our model extracts power spectra; due to the high dimensionality of features, sliding windows techniques are used to determine frequency bandwidth automatically, where an improved QRS algorithm selects the first dominant spectrum amplitude. In addition, the bandwidth algorithm has been used to reduce the dimensionality of data, remove redundant dimensions, and improve feature extraction. In this work, we have considered widely used machine learning classifiers. Our proposed method was evaluated using the accelerometer angles spectrum installed in six parts of the body and then reducing the bandwidth to know the evolution. Our approach attains an accuracy rate of 95.1% in the HuGaDB dataset with 70% of bandwidth, outperforming others in the human activity recognition accuracy.Partial funding for open access charge: Universidad de Málag

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

Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit.

This paper investigates the distribution characteristics of Fourier, discrete cosine, and discrete sine transform coefficients in T1 MRI images. The study reveals their adherence to Benford’s law, characterized by a logarithmic distribution of first digits. The impact of Rician noise on the first digit distribution is examined, causing deviations from the ideal distribution. A novel methodology is proposed for noise level estimation, employing metrics such as Bhattacharyya distance, Kullback-Leibler divergence, total variation distance, Hellinger distance, and Jensen-Shannon divergence. Supervised learning techniques utilize these metrics as regressors. Evaluations on MRI scans from several datasets coming from a wide range of different acquisition devices of 1.5T and 3T, comprising hundreds of patients, validate the adherence of noiseless T1 MRI frequency domain coefficients to Benford’s law. Through rigorous experimentation, our methodology has demonstrated competitiveness with established noise estimation techniques, even surpassing them in numerous conducted experiments. This research empirically supports the application of Benford’s law in transforms, offering a reliable approach for noise estimation in denoising algorithms and advancing image quality assessment.Partial funding for open access charge: Universidad de Málag

Comparación de marcos de trabajo de Aprendizaje Profundo para la detección de objetos

Muchas aplicaciones en visión por computador necesitan de sistemas de detección precisos y eficientes. Esta demanda coincide con el auge de la aplicación de técnicas de aprendizaje profundo en casi todos las áreas del aprendizaje máquina y la visión artificial. Este trabajo presenta un estudio que engloba diferentes sistemas de detección basados en aprendizaje profundo proporcionando una comparativa unificada entre distintos marcos de trabajo con el objetivo de realizar una comparación técnica de las medidas de rendimiento de los métodos estudiados.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech