Piotr Lech

Monte Carlo Based Algorithm for Fast Preliminary Video Analysis

In the paper a fast statistical image processing algorithm for video analysis is presented. Our method can be used on colour as well as grayscale or even binary images. The main component of the proposed approach is based on statistical analysis using the Monte Carlo method. A videos statistical information is acquired by specifying a logical condition for the Monte Carlo technique. The results of the algorithm depend on the correct choice of threshold values; thus the application area is limited by the adaptability of the thresholds to videos with large heterogeneity: e.g. videos with objects moving into and out of the scene, rapidly varying illumination, etc.

A Statistical Reduced-Reference Approach to Digital Image Quality Assessment

In the paper a fast method of the digital image quality estimation is proposed. Our approach is based on the Monte Carlo method applied for some classical and modern full-reference image quality assessment methods, such as Structural Similarity and SVD-based measure. Obtained results are compared to the effects achieved using the full analysis techniques. Significant reduction of the number of analysed pixels or blocks leads to fast and efficient estimation of image quality especially in low performance systems where the processing speed is much more important than the accuracy of the quality assessment.

A fast image analysis technique for the line tracking robots

Fast and simplified image processing and analysis methods can be successfully implemented for the robot control algorithms. Statistical methods seem to be very useful for such an approach, mainly because a significant reduction of analysed data is possible. In the paper the use of the fast image analysis based on the Monte Carlo area estimation for the simplified binary representation of the image is analysed and proposed for the mobile robot control. A possible implementation of the proposed method can applied in the line tracking robots and such application has been treated as the basic one for the testing purposes.

A Fast Histogram Estimation Based on the Monte Carlo Method for Image Binarization

In the paper the idea of fast histogram estimation is proposed which is based on the application of the Monte Carlo method. Presented method can be useful for fast image binarization especially for low computational efficiency solutions e.g. autonomous mobile robots. Proposed method has been compared with full image analysis and the obtained estimates have been used for threshold determination and binarization using well-known Otsu method.

The Detection of Horizontal Lines Based on the Monte Carlo Reduced Resolution Images

This paper presents the idea of fast algorithm for detecting horizontal lines in digital images. For this algorithm a dedicated procedure of data size reduction is proposed which utilizes the Monte Carlo method for preparation of lower size images from original High Definition ones. This approach is proposed for real-time, embedded systems or steering the mobile robot based on image analysis. The presented method is similar to downgrading the image resolution. The nearly real-time algorithm has been tested on real image data sets obtained from the mobile robot camera.

Fast Histogram Based Image Binarization Using the Monte Carlo Threshold Estimation

In the paper the idea of universal fast image binarization method is discussed which utilizes the histogram estimation using the Monte Carlo approach. Proposed reduction of the computational burden dependent on the number of analyzed pixels may be useful especially in real-time and embedded systems with limited amount of memory and processing power. An additional advantage of such simplified approach is relatively easy implementation independently on the used programming language.

Application of Super-Resolution Algorithms for the Navigation of Autonomous Mobile Robots

In the paper the idea of using the super-resolution algorithms for the self-localization and vision based navigation of autonomous mobile robots is discussed. Since such task is often limited both by the limited resolution of the mounted video camera as well as the available computational resources, a typical approach for video based navigation of mobile robots, similarly as many small flying robots (drones), is using low resolution cameras equipped with average class lenses. The images captured by such video system should be further processed in order to extract the data useful for real-time control of robot’s motion. In some simplified systems such navigation, especially in the within an enclosed environment (interior), is based on the edge and corner detection and binary image analysis, which could be troublesome for low resolution images.

Analysis of the IoT WiFi Mesh Network

This paper presents a conception of designing wireless sensor networks in mesh topology that perform their IoT tasks applying popular WiFi standards. Cheap IoT modules involve compromise between reliability and the price. Phenomena that occur in real wireless sensor network depends on many factors that are sometimes not well defined. Statistical analysis of the packet delays and failure rates for different scenario paths in our experimental network helps to identify anomaly nodes.

Fast Machine Vision Line Detection for Mobile Robot Navigation in Dark Environments

Navigation of mobile robots based on video analysis becomes one of the most popular application areas of machine vision in automation and robotics. Recently growing popularity of Unmanned Aerial Vehicles (drones) as well as some other types of autonomous mobile robots leads to rapid increase of their application possibilities e.g. related to exploration of some areas hardly accessible for people, such as caves, underground corridors, bunkers etc. However, such places are specific in view of lighting conditions so many classical image analysis algorithms cannot be applied effectively for navigation of mobile robots in such environments. In order to utilize the image data for robot navigation in such places some modified machine vision algorithms should be applied such as fast line detection based on statistical binarization discussed in this paper.

A Virtual Simulation of the Image Based Self-navigation of Mobile Robots

The paper concerns with the problem of fully visual self-navigation of mobile robots based on the analysis of similarity of images, acquired by the cameras mounted on the robot, with some previously captured images stored in a database. In order to simplify and speed-up the extraction of the necessary data from the image database it is assumed that the rough position of the robot is known e.g. based on the GPS module or some other sensors. Due to the application of the image analysis methods, the accuracy of the self-positioning of the robot can be significantly improved leading to fully visual self-navigation of autonomous mobile robots, assuming their continuous access to the image database. In order to verify the validity of the proposed approach, the virtual simulation environment based on the Simbad 3D robot simulator has been prepared. The initial results presented in the paper, obtained for synthetic images captured by the virtual robots, confirm the usefulness of the proposed approach being a good starting point for future experiments using the real images captured by the physical mobile robot also in various lighting conditions.