Muhammet Balcilar

Performance analysis of Lab2000HL color space for background subtraction

Background subtraction techniques are commonly used to identify moving objects in computer vision applications. This is still a challenging problem, especially when there is a non-stationary background such as a waving sea, or in the case where camera oscillations exist, or when videos have non-stationary backgrounds because of sudden changes in lightning. One of the most significant sub-tasks of a generic background subtraction technique is the background modeling step, which determines how background will be represented. A wide range of the literature is upon development of statistical models for background modeling. Especially, Gaussian Mixture Model (GMM) is a basic method. In this method, values of each pixels features with respect to time are represented with a few normal distributions. The problem with which features pixels will be represented is an important research topic. Recent studies involve applications using different color space with both pixel and region based features. In this study, in addition to color spaces used in literature, new color space which have linear hue band and named as Lab2000HL is aimed to test. The segmentation of foreground/background performance is measured with average precision rate. Nine different videos from I2R dataset having non-static background examples are used as test dataset.

Background estimation method with incremental iterative Re-weighted least squares

The basic steps for computer vision-based automatic video analysis are to detect and track objects. In order to do these steps, the most important and commonly used methods are background subtraction methods. This paper proposes a novel background subtraction method, which is a member of estimation-based background model, involving robust regression technique. The method proposed can estimate backgrounds at enough precision even when there are foreground objects stationary for a long time, which is often the case in images belonging to urban traffic cameras. The method has been tested with existing datasets in the literature and proved its success compared with other known methods. Moreover, it has been tested also with the dataset prepared during this research, which involves images where vehicles stop in different periods and then move again.

Region Based Fuzzy Background Subtraction Using Choquet Integral

Background subtraction, is a widely used method for identifying moving objects in multimedia applications such as video surveillance. Deterministic approaches are the first applications in literature, and they followed statistical approaches; however, more recently prediction-based filter approaches are preferred by researchers. The methods suggested for background subtraction in traffic surveillance applications, which are subject to many uncertainties, such as illumination noise, sudden changes in ambient light and structural changes, have to date failed to satisfy the requirements. Fuzzy approaches in the Artificial Intelligence method are widely used by researchers to eliminate uncertainties within the problem. In this study, a fuzzy background subtraction method, using choquet integral that process certain group of pixels together in order to eliminate uncertainties is suggested. The method is tested on traffic surveillance dataset, leading to satisfying results.

Implementation of frontier-based exploration algorithm for an autonomous robot

Exploration is defined as the selection of target points that yield the biggest contribution to a specific gain function at an initially unknown environment. Exploration for autonomous mobile robots is closely related to mapping, navigation, localization and obstacle avoidance. In this study an autonomous frontier-based exploration strategy is implemented. Frontiers are defined as the border points that are calculated throughout the mapping and navigation stage between known and unknown areas. Frontier-based exploration implementation is compatible with the Robot Operating System (ROS). Also in this study, real robot platform is utilized for testing and the effect of different frontier target assignment approaches are comparatively analyzed by means of total path length and thereby total exploration time.

A fuzzy expert system design for forecasting return quantity in reverse logistics network

Purpose – The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network. Design/methodology/approach – The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted. Findings – The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas. Research limitations/implications – In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other exp...

A Critical Review on the Numerical Methods of Two-Phase Flows

The two-phase flow processes play a significant role in the heat transfer processes in the chemical and power industry, including in nuclear power plants. This study is a critical review on the determination of the heat transfer characteristics of pure refrigerants flowing in vertical and horizontal tubes. The authors’ previous publications on this issue, including the numerical analyses, are summarized here. The lengths of the vertical and horizontal test sections varied between 0.5 m and 4 m countercurrent flow double-tube heat exchangers with refrigerant flowing in the inner tube and cooling water flowing in the annulus. The measured data are compared to numerical predictions based on the solution of the artificial intelligence methods and CFD analyses for the condensation and evaporation processes in the smooth and enhanced tubes. The theoretical solutions are related to the design of passive containment cooling systems (PCCS) in simplified water boiling reactors (SWBR). A genetic algorithm (GA), various artificial neural network models (ANN) such as multilayer perceptron (MLP), radial basis networks (RBFN), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS), and various optimization techniques such as unconstrained nonlinear minimization algorithm-Nelder-Mead method (NM), non-linear least squares error method (NLS), and Fluent CFD program are used in the numerical solution. It is shown that the heat transfer characteristics of laminar and turbulent condensing and evaporating film flows such as heat transfer coefficient and pressure drop can be predicted by means of numerical analyses reasonably well if there is a sufficient amount of reliable experimental data. Regression analysis gave convincing correlations, and the most suitable coefficients of the proposed correlations are depicted as compatible with the large number of experimental data by means of the computational numerical methods. Dependency of the output of the ANNs from various numbers of input values is also shown for condensing and evaporating flows.Copyright

Design and servo control of a leak tightness machine working based on hydrostatic pressure aging method

The leakage problem, which occurs due to the high hydrostatic pressure values of the fluid inside the pipes, is a serious issue in most of the pipeline systems. Therefore, the leak tightness test is required in different standards for verification of almost every kind of pipes used in both academic researches and industrial applications. The behavior of the hydrostatic pressure parameter in the test pipe has to be as smooth as possible during the leak tightness test. The conventional control loops of the servo mechanisms used for pumping the test fluid into the test pipe are weak to provide the minimum overshoot, the rise time and the steady-state error. In this situation, hydrostatic pressure tests are not capable of giving meaningful results. In this study, a leak tightness test machine working with a servo control loop to provide the optimum hydrostatic pressure pattern is designed. The efficacy of the designed leak tightness test machine and proposed optimized servo control loop have been shown with both simulation and experimental results.

Obstacle avoidance with Vector Field Histogram algorithm for search and rescue robots

The capability of avoid obstacles is the one of the key issues in autonomous search-and-rescue robots research area. In this study, the avoiding obstacles capability has been provided to the virtula robots in USARSim environment. The aim is finding the minimum movement when robot faces an obstacle in path. For obstacle avoidance we used an real time path planning method which is called Vector Field Histogram (VFH). After experiments we observed that VFH method is successful method for obstacle avoidance. Moreover, the usage of VFH method is highly incresing the amount of the visited places per unit time.

Routing with Dijkstra in Mobile Ad-Hoc Networks

It is important that robot teams have an effective communication infrastructure, especially for robots making rescue operations in debris areas. The robots making rescue operation in a large area of disaster are not always directly connected with central operator. In such large areas robots can move around without losing communication with each other only by passing messages from one to another up to the central operator. Routing methods determine from which node to which node the messages are conveyed. In this work blind flooding and table-based routing methods are tested for three different scenarios to measure their effectiveness using the simulation environment USARSIM and its wireless simulation server WSS. Message delay times and maximum data packet streaming rates are considered for measuring the effectiveness. Although it has some deficiencies, it was observed that table-based approach is more advantageous than blind flooding.

Usage of HoG (histograms of oriented gradients) features for victim detection at disaster areas

Employing robot teams at disaster areas requires usage of autonomous navigation methods. Moreover, autonomous navigation requires autonomous victim detection. Human skin color based victim detection methods may not be robust due to the variations in lightening conditions at disaster areas. Histograms of Oriented Gradients (HoG) were presented as an alternative way of human detection. In literature, HoG based methods proved their efficiency on the datasets including upright humans. But, the victims have very large variation of poses at a disaster area. In this work, the efficiency of HoG based methods was investigated on a dataset including very different poses and lightening conditions. We have reached 95% success on automatic victim detection problem in real time simulation environment.