Convolutional Neural Networks (CNNs) are widely used in image classification tasks, but their performance can degrade significantly under poor illumination conditions. Although numerous image enhancement methods have been developed to mitigate this issue, identifying the most appropriate technique for a specific image remains challenging. This work aims to determine the most effective image enhancement algorithm for a potentially dimmed input image to enhance the classification prediction performance. To do this, a trained regressor, which evaluates different features of an image, ascertains the increase or decrease in classification prediction performance between the input image and the enhanced image. These predictions are then used to select the most suitable image enhancement algorithm to be applied to the input image for optimal CNN classification. Experimental results using various CNN architectures and enhancement techniques demonstrate that the proposed strategy consistently improves classification performance under challenging lighting conditions.

The real-time synthesis of 3D views, facilitated by convolutional neural networks like NeX, is increasingly pivotal in various computer vision applications. These networks are trained using photographs taken from different perspectives during the training phase. However, these images may be susceptible to contamination from noise originating from the vision sensor or the surrounding environment. This research meticulously examines the impact of noise on the resulting image quality of 3D views synthesized by the NeX network. Various noise levels and scenes have been incorporated to substantiate the claim that the presence of noise significantly degrades image quality. Additionally, a new strategy is introduced to improve image quality by calculating consensus among NeX networks trained on images pre-processed with a denoising algorithm. Experimental results confirm the effectiveness of this technique, demonstrating improvements of up to 1.300 dB and 0.032 for Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), respectively, under certain scenes and noise levels. Notably, the performance gains are especially significant when using synthesized images generated by NeX from noisy inputs in the consensus process.

Abstract Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well‐developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high‐quality open‐access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease‐related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer‐aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection. In addition, baseline classification methods are proposed and analysed, validating the functionality of CADICA with deep learning‐based methods and giving the scientific community a starting point to improve CAD detection.

Abstract In the area of medical imaging, one of the factors that can negatively influence the performance of prediction algorithms is the limited number of observations for each class within a labeled dataset. Usually, in order to increase the samples, a second set of unlabeled images is used. However, this set adds two new problems (i) finding patient observations with different pathologies than those observed in the labeled data set and (ii) finding images belonging to a different distribution from the dataset used in the model training process. This way, merging datasets from different sources can have an adverse effect on the distribution of features. Encountering this type of data (better known as out-of-distribution data) within the deployment environments may also lead to varying degrees of performance degradation as can be seen in the different experimental results obtained. In this research, a study of the behavior of Feature Density is made, as a mathematical model for the estimation of predictive uncertainty in supervised classification algorithms, in order to improve the behavior when out-of-distribution data are presented in the dataset. The Feature Density method is based on the estimation of feature density by means of histogram calculation (or Probability Density Function). The advantage of this method over the baseline approach (Mahalanobis distance) is that it does not assume a Gaussian-type distribution of sample characteristics and serves to estimate the uncertainty. This work focuses on the binary classification of mammography X-ray images from three different datasets simulating the condition of a different degree of contamination with out-of-distribution sample. According to the obtained results, the performance of the proposed method depends directly on the architecture of the implemented neural network.

In this paper, we present a new framework for Content Based Image Retrieval (CBIR), based on Dynamic Ensemble Selection (DES) of classifiers. Herein, the classifiers consist of Convolutional Neural Networks (CNNs) that output the class probability vector of each input image. First, a diverse ensemble is built by training several weak classifiers on different training subsets, from the retrieval database. Then, each training image is passed throughout all the candidate classifiers to extract its class probability vector. These extracted vectors are then combined to make its final representation. When a new query image is input, the level of competence of all the candidate classifiers is measured on the region of competence of this image, in the training set. Then, only the most competent classifiers are selected to extract the class probability vectors from each query image. They are then combined to make a more discriminant image representation. To the best of our knowledge DES has never been applied to the CBIR area, which is the novelty of our research. The obtained results demonstrate the effectiveness of the dynamic selection, compared to the use of all the ensemble members, in terms of precision, recall, and mean average precision.

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.

Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning model under real world scenarios. The usage of unlabeled data to improve the accuracy of the model can be an approach to tackle the lack of target data. Moreover, important model attributes for the medical domain as model uncertainty might be improved through the usage of unlabeled data. Therefore, in this work we explore the impact of using unlabeled data through the implementation of a recent approach known as MixMatch, for mammogram images. We evaluate the improvement on accuracy and uncertainty of the model using popular and simple approaches to estimate uncertainty. For this aim, we propose the usage of the uncertainty balanced accuracy metric.

Image restoration, which is the process of denoising noisy images in order to recover their latent clean images, has been frequently addressed. The importance of this field resides in the impact of noisy images on the performance of computer vision systems. In this work, a deep autoencoder neural network architecture is proposed to denoise images affected by Gaussian noise. The performance of the system is enhanced by using a test time augmentation scheme. Experiments have been carried out by considering different levels of Gaussian noise. Results demonstrate the suitability of the proposed methodology in order to enhance the quality of the image restoration process in images affected by Gaussian noise.

Skin cancer is one of the most prevalent diseases among people. Physicians have a challenge every time they have to determine whether a diseased skin is benign or malign. There exist clinical diagnosis methods (such as the ABCDE rule), but they depend mainly on the physician's experience and might be imprecise. Deep learning models are very extended in medical image analysis, and several deep models have been proposed for moles classification. In this work, a convolutional neural network is proposed to support the diagnosis procedure. The proposed MobileNetV2-based model is improved by a shifting technique, providing better performance than raw transfer learning models for moles classification. Experiments show that this technique could be applied to the state-of-the-art deep models to improve their results and outperform the training phase.

Foreground object detection algorithms should be insensitive to noise present in the analyzed video sequences. In this work, a study of a type of non-supervised deep learning network, called autoencoder, is performed. They are suited to reduce input dimensionality and capture the most relevant information present in a region or image. Therefore, different types of autoencoders, deterministic and variational, with different architectures, activation functions and number of layers, are analyzed. This neural network is combined with a probabilistic mixture model which attempts to classify each video frame region as background and foreground.

Homography estimation from a pair of natural images is a problem of paramount importance for computer vision. Specialized deep convolutional neural networks have been proposed to accomplish this task. In this work, a method to enhance the result of this kind of homography estimators is proposed. Our approach generates a set of tentative color transformations for the image pair. Then the color transformed image pairs are evaluated by a regressor that estimates the quality of the homography that would be obtained by supplying the transformed image pairs to the homography estimator. Then the image pair that is predicted to yield the best result is provided to the homography estimator. Experimental results are shown, which demonstrate that our approach performs better than the direct application of the homography estimator to the original image pair, both in qualitative and quantitative terms.

Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X- ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison.

Convolutional Neural Networks (CNNs) are widely used due to their high performance in many tasks related to computer vision. In particular, image classification is one of the fields where CNNs are employed with success. However, images can be heavily affected by several inconveniences such as noise or illumination. Therefore, image enhancement algorithms have been developed to improve the quality of the images. In this work, the impact that brightness and image contrast enhancement techniques have on the performance achieved by CNNs in classification tasks is analyzed. More specifically, several well known CNNs architectures such as Alexnet or Googlenet, and image contrast enhancement techniques such as Gamma Correction or Logarithm Transformation are studied. Different experiments have been carried out, and the obtained qualitative and quantitative results are reported.

This paper presents a methodology for estimating pollution on roads by analyzing traffic video sequences. The objective is to take advantage of the huge network of static cameras which is possible to lind in the road system of any state or country to estimate the pollution on each area. This proposal uses deep learning neural networks for the object detection, and a pollution estimation model based on the frequency of vehicles and their speed. The experiments show promising results which suggest that the system can be used alone or combined with existing systems for measuring pollution on roads.

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its self-organization capabilities.

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 Tl images, the new method also yielded better qualitative results, both in the restored images and in the images of residuals.

Abstract The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on the neural gas network and a subsequent piecewise linear interpolation by Delaunay triangulation. Furthermore, an ensemble model of neural gas networks is also proposed. The approach can handle arbitrary camera directions and zooms for a pan‐tilt‐zoom camera‐based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed methods are effective and suitable to use for real‐time video surveillance applications.

One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection. The proposal is adequate for scenes where the background varies slowly. The performance of the method is tested against several state-of-the-art foreground detectors both quantitatively and qualitatively, with favorable results.

In this paper a controller for PTZ cameras based on an unsupervised neural network model is presented. It takes advantage of the foreground mask generated by a non-parametric foreground detection subsystem. Thus, our aim is to optimize the movements of the PTZ camera to attain the maximum coverage of the observed scene in presence of moving objects. A growing neural gas (GNG) is applied to enhance the representation of the foreground objects. Both qualitative and quantitative results are reported using several widely used datasets, which demonstrate the suitability of our approach.

The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.