Outdoor images captured during hazy conditions have degraded visibility. The lack of both a medium transmission and atmospheric lights in a single haze image cause an ill-posed problem in the atmospheric scattering model. This paper proposes a novel haze density estimation model with a universal atmospheric-light extractor for single-image dehazing. The proposed method employs exponential inference to construct an exponential inference model to more accurately estimate haze density compared with the state-of-the-art methods. The coefficients in the proposed haze density estimation model are learned using a turbulent particle swarm optimization technique to obtain the best approximation of medium transmission. Moreover, a novel universal atmospheric-light extractor based on support vector data description is utilized to resolve the problem caused by a lack of atmospheric light. The overall results obtained by conducting qualitative and quantitative evaluations demonstrated that the proposed method has substantially higher dehazing efficacy and produces fewer artifacts than the state-of-the-art haze removal methods.

The real-time and robust fall detection is one of the key components of elderly people care and monitoring systems. Depth sensors, as they became more available, occupy an increasing place in event recognition systems. Some of them can directly produce a skeletal description of the human figure for compact representation of a person’s posture. Skeleton description makes the output of source video or detailed information about the depth outside the system unnecessary and raises the privacy of the entire system. Based on a comparative study of different RGB-D cameras, the most promising model for further development was chosen - Microsoft Kinect v2. The TST Fall Detection Dataset v2 is used here as a base for experiments. The proposed algorithm is based on the skeleton features encoding on the sequence of neighboring frames and support vector machine classifier. A version of a cumulative sum method is applied for combining the individual decisions on the consecutive frames. It is offered to use the one-class classifier for detection of low-quality skeletons. The 0.958 accuracy of our fall detection procedure was obtained in the cross-validation procedure based on the removal of records of a particular person from the database (Leave-one-Person-out).

Human posture recognition is the foundation of the human activity monitoring. The activity monitoring system is in high demand for the elderly living alone to monitor their health status and accidental fall since the world elderly population will be doubled by 2050. Researchers have developed many camera or wearable device-based human recognition systems; however, they are considered to be privacy-invasive and/or not practical for the long-term monitoring. We propose a device-free unobtrusive indoor human posture recognition system leveraging a low-resolution infrared sensor-based wireless sensor network and deep convolutional neural network (DCNN). We integrated AMG8833 sensor module with 8×8 thermal sensors for sensing the human body temperature and WiFi module for a wireless sensor network. Three wireless sensor nodes are used to capture 3-axis human thermal image. Totally, 15063 samples are collected from four volunteers while they had performed eight human postures as the ground truth for the 10-fold cross-validation of DCNN models. Experimental results indicate that the highest average F1-score for the eight postures was 0.9981. Thus, the proposed system has the high potential for monitoring elderly daily activities, exercise, and fall in emergency cases. Moreover, we believe that our proposed system will be a milestone in the device-free unobtrusive sensing technology.

Automated computational analysis of nanoparticles is the key approach urgently required to achieve further progress in catalysis, the development of new nanoscale materials, and applications. Analysis of nanoscale objects on the surface relies heavily on scanning electron microscopy (SEM) as the experimental analytic method, allowing direct observation of nanoscale structures and morphology. One of the important examples of such objects is palladium on carbon catalysts, allowing access to various chemical reactions in laboratories and industry. SEM images of Pd/C catalysts show a large number of nanoparticles that are usually analyzed manually. Manual analysis of a statistically significant number of nanoparticles is a tedious and highly time-consuming task that is impossible to perform in a reasonable amount of time for practically needed large amounts of samples. This work provides a comprehensive comparison of various computer vision methods for the detection of metal nanoparticles. In addition, multiple new types of data representations were developed, and their applicability in practice was assessed.