Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks.

Underwater images suffer from various degradation factors, such as blur, haze, color degradation, and marine snow. Marine snow is a type of noise, caused mostly by biological particles that fall into the ocean bottom, and which impedes proper object detection in underwater vision. A method for real-time marine snow removal from underwater color and monochrome video is presented. It is based on the proposed marine snow model, spatiotemporal patch analysis, and three-dimensional median filtering. The method was evaluated on a number of real underwater sequences endowed with the hand-annotated ground-truth data which were made available from the Internet. As shown by the experiments, the method attains high accuracy and performs in real time.

This paper presents a method for content change detection in multidimensional video signals. Video frames are represented as tensors of order consistent with signal dimensions. The method operates on unprocessed signals and no special feature extraction is assumed. The dynamic tensor analysis method is used to build a tensor model from the stream. Each new datum in the stream is then compared to the model with the proposed concept drift detector. If it fits, then a model is updated. Otherwise, a model is rebuilt, starting from that datum, and the signal shot is recorded. The proposed fast tensor decomposition algorithm allows efficient operation compared to the standard tensor decomposition method. Experimental results show many useful properties of the method, as well as its potential further extensions and applications.

In recent years many methods have been proposed for eye detection. In some cases however, such as driver drowsiness detection, lighting conditions are so challenging that only the thermal imaging is a robust alternative to the visible light sensors. However, thermal images suffer from poor contrast and high noise, which arise due to the physical properties of the long waves processing. In this paper we propose an efficient method for eyes detection based on thermal image processing which can be successfully used in challenging environments. Image pre-processing with novel virtual high dynamic range procedure is proposed, which greatly enhances thermal image contrast and allows for more reliable computation of sparse image descriptors. The bag-of-visual-words approach with clustering was selected for final detections. We compare our method with the YOLOv3 deep learning model. Our method attains high accuracy and fast response in real conditions without computational complexity and requirement of a big dataset associated with the deep neural networks. For quantitative analysis a series of thermal video sequences were recorded in which eye locations were manually annotated. Created dataset was made publicly available on our website.