Nihat Adar

Priority-based task allocation in auction-based applications

One of the main subjects that are heavily studied in multi-robot domain is task allocation according to selected constraints and construct paths using these allocated tasks. In this paper a market-based task allocation method is proposed to allocate tasks according to their priorities and create paths for a mobile group in a known indoor environment. Proposed methodology is applied to an example with four mobile robots on a map that has twenty nodes. In order to solve tour construction with priority problem, Nearest Neighbor heuristic approach is modified and used. In the study agents communicate with each other through an Open Agent Architecture (OAA). Also simulations on MobileSim simulator program are conducted to show the effectiveness of the proposed method.

Fractal Dimension Based Shot Transition Detection in Sport Videos

Increase in application fields of video has boosted the demand to analyze and organize video libraries for efficient scene analysis and information retrieval. This paper addresses the detection of shot transitions, which plays a crucial role in scene analysis, using a novel method based on fractal dimension (FD) that carries information on roughness of image intensity surface and textural structure. The proposed method is tested on sport videos including soccer and tennis matches that contain considerable amount of abrupt and gradual shot transitions. Experimental results indicate that the FD based shot transition detection method offers promising performance with respect to pixel and histogram based methods available in the literature.

Gradual shot change detection in soccer videos via fractals

Accurate shot change detection plays vital role to organize video contents into meaningful parts for video scene analysis. Shot changes may occur with either abrupt or gradual shot transition. In abrupt shot transition, the change of video content occurs over a single frame. In gradual shot transition, however, the content change takes place gradually through a short sequence of frames. The gradual transition is also divided into several subgroups such as dissolve, pan, zoom and wipe. In the literature, there exist numerous methods to detect shot changes. While these methods are able to detect abrupt changes properly, they usually fail to detect gradual changes. This paper proposes a new method for detecting gradual shot changes successfully. The proposed method employs fractal dimension information of gray scale video frames. In the experimental work, efficacy of fractal based gradual shot change detection method is tested on soccer videos including different types of gradual shot changes and compared with well known pixel and histogram based shot change detection methods. Results of the experimental study indicate that the proposed method yields higher accuracy with respect to other methods in most cases.

Real-time Motion Region Detection and Contouring System

Many real world applications require real-time motion detection. Performance of a motion detection system should be fast enough so that moving objects in video can be detected and processed in real time. Once motion region in a video is detected, object tracking, image data mining, semantic meaning extraction, and other video/image processing techniques can be performed. There is a lot of research in realtime motion detection however most of these studies are either computationally expensive or not robust enough against real world image overheads like background clutter and pixel noise. In this paper, we propose a robust real-time motion detection system with low computational complexity. The proposed system finds the difference images from successive frames, applies filtering and the pixel clustering operation on them. Then, sub-contours of the observed motion regions are found using either minimum bounding coarse rectangles, minimum bounding rectangles using moments or minimum bounding convex hull methods. Finally, our contour merging algorithm merges sub-contours to identify moving object regions. This novel approach, detects moving objects in a video frame with tight enclosing boundary contours without sacrificing robustness. Since no constraint is attached to the technique, it is a good candidate to be used as motion detection of choice in wide range of applications. We obtained promising results in the experiments with real time data.

Image enhancement with outlier detection

In this study, a new method is proposed to achieve a high resolution (HR) image from low resolution (LR) video frames, which include camera motions as well as local motions. The best matching block in the motion estimation is obtained by using the variable size block matching (VSBM) algorithm. Furthermore, before merging the pixels, the outlier pixels were cleaned using statistical outlier detection method. Peak-signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) were used to compare the experimental results of the proposed method.

Video super-resolution based on block motion estimation and gradient magnitude

Super-resolution (SR) technology aims to improve image resolution from a set of observed low resolution (LR) frames. This paper presents a new SR approach based on block motion estimation (ME) with gradient magnitude and the block types. ME is applied between a reference and its neighboring frames. If the motion is estimated inaccurately undesirable results are occurred at high resolution (HR) output frame. So, adaptive and static thresholds are used to eliminate improper blocks in registration. Outlier pixel detection is applied in reconstruction step. Finally, a deblurring process is performed on the HR frame. Proposed method results are compared with different interpolation methods. The experimental results are shown both peak-signal-to-noise ratio (PSNR) and as visually.

TASK ALLOCATION IN MARKET-BASED APPLICATIONS BY THE NEAREST NEIGHBOR HEURISTIC

One of the main subjects that is studied in multi-robot applications is allocating tasks to robots and constructing objective-efficient tours for the robots using these allocated tasks. The main purpose of this paper is to allocate tasks via market-based task allocation architecture and to construct collision-free routes for multi-robot systems in a known indoor environment. For task allocation, a market-based architecture is constructed and applied to an example of a team of four heterogeneous mobile robots. In the study, tasks are allocated according to one of two constraints: minimizing the makespan of the robots and maximizing the robot task matching value of each robot in the group. These constraints are achieved by changing the value of a decision parameter. To show the effectiveness of the proposed method, comparisons are made within these constraints. To construct path, a combination of the Nearest Neighbor heuristic and Dijkstras shortest path algorithms is used. To construct non-conflicting paths, a method that detects and solves collisions is developed. In the study, for agent communication Open Agent Architecture is used. Additionally, simulations on the MobileSim platform are conducted to verify the feasibility of the proposed method.

Modeling a Web Service-Based Decentralized Parallel Programming Environment

Abstract: Heterogeneous computer clusters which deploy COTS PCs and/or high performance server computers are economic parallel systems. However, development and execution software should be installed on each cluster host along with the operating system. Cluster members can no longer be used as standalone PCs without interrupting cluster work. On the other hand, usability of hosts in both individual and cluster-related tasks at the same time is strongly desired. In this paper, we present a web service-based parallel heterogeneous model in which participation is conditional and dynamic. Grid functionality is provided by using service oriented software architecture. Since service architecture is widely supported by operating systems, the hosts can easily be adapted to the parallel environment. Completion of all jobs is guaranteed theoretically and shown practically on the laboratory implementation as long as at least one host is operational in the system.

Use of multiple features in license plate detection

Recognition of license plates in images is analyzed in two steps as the detection of the license plate and the recognition of the text included within. Although this problem is widely regarded as solved, there is still room for research and development in cases of low quality images and existence of other structures in images hindering correct detection. In this paper, the techniques used in the detection process are evaluated and it is shown that employment of multiple features improves success rate compared to the implementations where only a single feature is used.

Plant classification using fractal dimension in HSV color space

In this paper, the use of fractal dimension measurements in HSV color space for image segmentation is investigated. The regions including different plant species, especially weed and non-weed, are analyzed as application. With this purpose, several features are extracted using HSV color components belonging to images, which contain different plant types, and corresponding partial fractal dimensions. Then, the extracted features are fed into Bayes classifier so that the regarding plant types are classified. Experimental results indicate that the features obtained with HSV components provide results as good as the ones that are extracted using gray scale values only. When all the features are used together, the best performance is obtained.