In this study a Deep Learning (DL) based-Brain-Computer Interface (BCI) system able to automatically detect and decode voluntary eye blinks from the analysis of electroen-cephalographic (EEG) signals is proposed for controlling, in principle, an external device by means of ocular movements. To this end, a Convolutional Neural Network (CNN) is developed to classify EEG recordings related to natural (or involuntary) blinks, forced (or voluntary) blinks and baseline (no blinks) category. The proposed system achieved an impressive average classification performance: accuracy rate up to 99.4% +/- 1.3%. However, the core of the present study was to investigate the explainability and interpretability of the proposed CNN with the ultimate aim of explore which segments of the EEG signal is the most relevant in the voluntary/involuntary blink discrimination process. To this end, explainable Artificial Intelligence (xAI) techniques were applied. Specifically, the Gradient-weighted Class Activation Mapping (Grad-CAM) and the Local Interpretable Model Agnostic Explanation (LIME) algorithms were used. xAI allowed us to visually identify the most relevant EEG areas especially for the voluntary and involuntary blink detection. Indeed, limited to the analyzed dataset, for natural blinks, the discriminating region was the interval ranged from the temporal instant the eye was closed till the following instants of the reopening (vice-versa for voluntary blinks). The baseline (no blink), on the other hand, was characterized by a low activation threshold throughout the EEG segment.

The goal of this paper is to develop a Brain Computer Interface (BCI) based on voluntary eye blinks decoding. In particular, the study was focused on the signals generated in the cortex by eye blinking, which can be collected by frontopolar scalp Electroencephalographic (EEG) sensors. Normally, EEG recording systems meant for clinical applications are expensive and cannot be used in large-scale user-friendly applications. Thanks to a prototype made by the STMicroelectronics company, based on an Open Source EEG project, a low-cost EEG recording system was created in this work. The goal is to develop an algorithm that can detect and discriminate between voluntary (forced) and involuntary (natural) blinking so that, in the future, an EEG-based BCI system that is able to control a device through eye movements could be developed, which would be of great use for all people with motor disabilities who can control eye movements. The proposed algorithm is based on a one-dimensional (1D) Convolutional Neural Network (CNN) architecture. Frontopolar EEG signals were collected during the execution of voluntary and spontaneous blinks by four healthy subjects. A dataset of EEG epochs of including blinks was constructed and used to train and validate the proposed CNN. The proposed system allowed to discriminate the blinks performed by the subjects (voluntary vs. involuntary) with an average accuracy of 97.92%.

Perspective cameras are the most popular imaging sensors used in computer vision. However, many application fields, including automotive, surveillance, and robotics, require the use of wide angle cameras (e.g., fisheye), which allow to acquire a larger portion of the scene using a single device at the cost of the introduction of noticeable radial distortion in the images. Affine covariant feature detectors have proved successful in a variety of computer vision applications, including object recognition, image registration, and visual search. Moreover, their robustness to a series of variabilities related to both the scene and the image acquisition process has been thoroughly studied in the literature. In this paper, we investigate their effectiveness on fisheye images providing both theoretical and experimental analyses. As theoretical outcome, we show that the inherently non-linear radial distortion can be locally approximated by linear functions with a reasonably small error. The experimental analysis builds on Mikolajczyk's benchmark to assess the robustness of three popular affine region detectors (i.e., maximally stable extremal regions, and Harris and Hessian affine region detectors), with respect to different variabilities as well as to radial distortion. To support the evaluations, we rely on the Oxford data set and introduce a novel benchmark data set comprising 50 images depicting different scene categories. Experiments are carried out on rectilinear images to which radial distortion is artificially added, and on real-world images acquired using fisheye lenses. Our analysis points out that affine region detectors can be effectively employed directly on fisheye images and that the radial distortion is locally modeled as an additional affine variability.

In this paper we tackle the problem of correctly estimating the gradient of distorted images. The proper estimation of the gradient in the presence of distortion is of great interest due to the large number of applications relying on wide angle cameras (e.g., in surveillance, automotive, robotics). To this aim we propose the Generalized Sobel Filters (GSF), a family of adaptive Sobel filters able to correctly estimate the gradient of distorted images. To assess the performances of the proposed method, we acquired a benchmark dataset of high resolution images belonging to different categories which are relevant to application domains where the gradient estimation is usually employed. We build an objective evaluation pipeline and perform experiments which show that our method outperforms the state-of-the-art.

Feature extractors play an important role in different Computer Vision application domains such as registration, recognition and visual search. Different detectors have been proposed and evaluated so far assuming images taken with classic cameras. However, many operating cameras (e.g., in surveillance and automotive) are built considering a fisheye model and a preprocessing step is performed to remove the distortion of the images before running a detector. The following question arises: are the current detectors suitable to work directly in the fisheye domain? To answer this question, in this paper a benchmark dataset and objective evaluation measures are considered to evaluate the performances of the state-of-the-art detectors in the fisheye domain. Test images are properly generated starting from benchmark rectilinear images and considering different fisheye focal lengths. The experiments evaluate the performances of the detectors against both increasing fisheye distortion and the combination of the fisheye distortion with photometric and geometric variability of the image content. The experiments demonstrate that affine covariant detectors can be employed directly in the fisheye domain. Furthermore, although the transformation between the rectilinear and the fisheye coordinates is not affine, we show that the mapping can be locally approximated by linear functions with a small error.

One of the most common problems in the image forensics field is the reconstruction of the history of an image or a video. The data related to the characteristics of the camera that carried out the shooting, together with the reconstruction of the (possible) further processing, allow us to have some useful hints about the originality of the visual document under analysis. For example, if an image has been subjected to more than one JPEG compression, we can state that the considered image is not the exact bitstream generated by the camera at the time of shooting. It is then useful to estimate the quantization steps of the first compression, which, in case of JPEG images edited and then saved again in the same format, are no more available in the embedded metadata. In this paper, we present a novel algorithm to achieve this goal in case of double JPEG compressed images. The proposed approach copes with the case when the second quantization step is lower than the first one, exploiting the effects of successive quantizations followed by dequantizations. To improve the results of the estimation, a proper filtering strategy together with a function devoted to find the first quantization step, have been designed. Experimental results and comparisons with the state-of-the-art methods, confirm the effectiveness of the proposed approach.

To assess if a digital image has been (or not) doubly compressed is a challenging issue especially in forensics domain where could be fundamental clarify if, in addition to the compression at the time of shooting, the picture was decompressed (in some way) and then resaved. This is not a clear indication of forgery, but it guarantees that the image, probably, is not the original one. In this paper we propose a novel technique able to recover the coefficients of the first compression in a double compressed JPEG image under some assumptions. The proposed approach exploits how successive quantizations followed by dequantizations introduce some regularities (e.g., sequence of zero and not zero values) on the histograms of coefficient distributions that could be analyzed to recover the original compression parameters. Experimental results and comparisons with state of the art methods confirm the effectiveness of the proposed approach.

Digital video stabilization enables to acquire video sequences without disturbing jerkiness by compensating unwanted camera movements. In this paper, we propose a novel fast image registration algorithm based on block matching. Unreliable motion vectors (i.e., not related with jitter movements) are properly filtered out by making use of ad-hoc rules taking into account local similarity, local “activity,” and matching effectiveness. Moreover, a temporal analysis of the relative error computed at each frame has been performed. Reliable information is then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.

Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In this paper we propose a novel fast video stabilization algorithm based on block matching of local motion vectors. Some of these vectors are properly filtered out by making use of ad-hoc rules taking into account local similarity, local ldquoactivityrdquo and matching effectiveness. Also a temporal analysis of the relative error computed at each frame has been achieved. Reliable information are then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.

In this paper we propose a novel fast fuzzy classifier able to find regular and low distorted near regular texture taking into account the constraints of video stabilization applications. Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In presence of regular or near regular texture, video stabilization approaches typically fail. These kind of patterns, due to their periodicity, create multiple matching that degrade motion estimation performances. The proposed classifier has been used as a filtering module in a block based video stabilization approach. Experiments on real sequences with (and without) regular texture confirm the effectiveness of the proposed approach.

Image denoising is one of the most recurrent problems to face in the design of image generation pipelines. This paper proposes a novel method for the estimation of the noise level in images contaminated by additive white Gaussian noise (AWGN). The estimated noise level is updated on a frame basis allowing convergence to the optimal value. At the same time, dynamic adjustment to changing noise levels is permitted. The proposed technique has been successfully implemented in a real system, demonstrating the validity of the proposed solution.