This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and experimental results show that it is a powerful method for colour texture classification.

The employees’ health and well-being are an actual topic in our fast-moving world. Employers lose money when their employees suffer from different health problems and cannot work. The major problem is the spinal pain caused by the poor sitting posture on the office chair. This paper deals with the proposal and realization of the system for the detection of incorrect sitting positions. The smart chair has six flexible force sensors. The Internet of Things (IoT) node based on Arduino connects these sensors into the system. The system detects wrong seating positions and notifies the users. In advance, we develop a mobile application to receive those notifications. The user gets feedback about sitting posture and additional statistical data. We defined simple rules for processing the sensor data for recognizing wrong sitting postures. The data from smart chairs are collected by a private cloud solution from QNAP and are stored in the MongoDB database. We used the Node-RED application for the whole logic implementation.

Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.

In order to fill gap of growing demand for high efficient image and video processing, open source computer vision library (OpenCv) is way to deals with this task. Hence, this paper is about basic algorithm for image processing and their CPU time consumption in Matlab comparing with OpenCv. Algorithms are tested on images with resolution 3264×2448, 1920×1080, 1024×768 and 220×260. Multi-processors computer and multi-threading programs are used to improve processing efficiency.

In this paper, a novel system for automatic detection and classification of animal is presented. System called ASFAR (Automatic System For Animal Recognition) is based on distributed so-called `watching device' in designated area and main computing unit (MCU) acting as server and system manager. Watching devices are situated in wild nature and their task is to detect animal and then send data to MCU to evaluation. The main task of whole system is to determine migration corridors of wild animals in designated area. To create object representation, visual descriptors were chosen and Support Vector Machine (SVM) was used to classify descriptors.