Damaged watermarks detection in frequency domain as a primary method for video concealment

This paper deals with video transmission over lossy communication networks. The main idea is to develop video concealment method for information losses and errors correction. At the beginning, three main groups of video concealment methods, divided by encoder/decoder collaboration, are briefly described. The modified algorithm based on the detection and filtration of damaged watermark blocks encapsulated to the transmitted video was developed. Finally, the efficiency of developed algorithm is presented in experimental part of this paper

Vehicle Classification Based on FBG Sensor Arrays Using Neural Networks

This article is focused on the automatic classification of passing vehicles through an experimental platform using optical sensor arrays. The amount of data generated from various sensor systems is growing proportionally every year. Therefore, it is necessary to look for more progressive solutions to these problems. Methods of implementing artificial intelligence are becoming a new trend in this area. At first, an experimental platform with two separate groups of fiber Bragg grating sensor arrays (horizontally and vertically oriented) installed into the top pavement layers was created. Interrogators were connected to sensor arrays to measure pavement deformation caused by vehicles passing over the pavement. Next, neural networks for visual classification with a closed-circuit television camera to separate vehicles into different classes were used. This classification was used for the verification of measured and analyzed data from sensor arrays. The newly proposed neural network for vehicle classification from the sensor array dataset was created. From the obtained experimental results, it is evident that our proposed neural network was capable of separating trucks from other vehicles, with an accuracy of 94.9%, and classifying vehicles into three different classes, with an accuracy of 70.8%. Based on the experimental results, extending sensor arrays as described in the last part of the paper is recommended

A New Algorithm for Key Frame Xtraction Based on Depth Map Using Kinect

In this paper, a new algorithm for key frame extraction based on depth map for hand gesture recognition is presented. The all input sequences are captured by Microsoft Kinect camera system. These methods extract three key frames from captured depth video sequence. These key frames describe dynamic gesture. The proposed extraction method is composed of two parts. The first part, labelled as space segmentation extracts the region of hand from background. The second part labelled as time segmentation splits captured sequence into three parts and marks one frame per part as the key frame. A new gesture database for evaluation of proposed method was created. The proposed method to human evaluators was compared. The experimental results show that the proposed system obtained accuracy about 90%

A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor

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

Tool for Parsing Important Data from Web Pages

This paper discusses the tool for the main text and image extraction (extracting and parsing the important data) from a web document. This paper describes our proposed algorithm based on the Document Object Model (DOM) and natural language processing (NLP) techniques and other approaches for extracting information from web pages using various classification techniques such as support vector machine, decision tree techniques, naive Bayes, and K-nearest neighbor. The main aim of the developed algorithm was to identify and extract the main block of a web document that contains the text of the article and the relevant images. The algorithm on a sample of 45 web documents of different types was applied. In addition, the issue of web pages, from the structure of the document to the use of the Document Object Model (DOM) for their processing, was analyzed. The Document Object Model was used to load and navigation of the document. It also plays an important role in the correct identification of the main block of web documents. The paper also discusses the levels of natural language. These methods of automatic natural language processing help to identify the main block of the web document. In this way, the all-textual parts and images from the main content of the web document were extracted. The experimental results show that our method achieved a final classification accuracy of 88.18%

Novel Approach to Color Texture Retrieval Based on GLCM

The color and texture are very important features in image analysis. In this paper, the combination of these features is presented. Several approaches for color texture features extraction are researched. The improvement of the one-dimensional version of GLCM (Gray Level Co-occurrence Matrix) for color textures was designed. We named this method as Color Level Co-occurrence Matrix (1D-CLCM). The experiments on database of 2600 color images are provided. Finally, the evaluation and comparison of image retrieval results for separate methods are presented

Image Segmentation and Feature Extraction Using Sift-Sad Algorithm for Disparity Map Generation

In this paper, a stereo matching algorithm based on image segments and disparity measurement using stereo images is presented. We propose the hybrid segmentation algorithm that is based on a combination of the Belief Propagation and Mean Shift algorithms with aim to refine the final disparity map by using a stereo pair of images. Firstly, a color based segmentation method is applied for segmenting the left image of the input stereo pair (reference image) into regions. The aim of the segmentation is to simplify representation of the image into the form that is easier to analyze and is able to locate objects in images. Secondly, results of the segmentation are used as an input of the SIFTSAD matching method to determine the disparity estimate of each image pixel. This matching algorithm is proposed by combining Scale Invariant Feature Transform (SIFT) with the Sum of Absolute Difference (SAD). Finally, the comparisons between the three robust feature detection methods SIFT, Affine SIFT (ASIFT) and Speeded Up Robust Features (SURF) are presented. The obtained experimental results demonstrate that the proposed method has a positive effect on overall estimation of disparity map and outperforms other examined methods