CVABS: Moving Object Segmentation with Common Vector Approach for Videos

Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring. An accurate background model helps detecting activity of moving objects in the video. In this work, we have developed a new subspace based background modelling algorithm using the concept of Common Vector Approach with Gram-Schmidt orthogonalization. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilized as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl

Image coding for digitized libraries

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 1998.Thesis (Ph.D.) -- Bilkent University, 1998.Includes bibliographical references leaves 104-113III this thesis, image coding methods for two basic image types are developed under a digitized library framework. The two image types are gray tone or color images, and binary textual images, which are the digitized image versions of text documents. The grciy tone images are encoded using an adaptive subband decomposition followed by zerotree quantizers. The adaptive sub- l)and decomposition filter bank adaptively updates the filter bank coefficients in which the values of one of the subbands is predicted from the other sub- band. It is observed that the adaptive subband decomposition performs better than a regulcir subband decomposition with a fixed filter bank in terms of compression. For the binary textual images, a compression algorithm using binary subband decomposition followed by a textual image compression (TIC) method that exploits the redundancy in repeating characters is developed. The binary subband decomposition yields binary sub-images, and the TIC method is applied to the low band sub-image. Obtaining binary sub-images improves compression results as well as pattern matching time of the TIC method. Simulation results for both adaptive subband decomposition and multiresolution TIC methods indicate improvements over the methods described in the literature.Gerek, Ömer NezihPh.D

Design and implementation of a PC based medical image workstation

Ankara : Bilkent University, 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 100-102Gerek, Ömer NezihM.S

DESIGN AND IMPLEMENTATION OF AN AUTONOMOUS VEHICLE FOR WASTE MATERIAL COLLECTION AND FIRE DETECTION

Autonomous vehicles are becoming increasingly popular in a variety of applications, including waste collection and fire detection. In this work, we present the design and implementation of an autonomous vehicle for these tasks in urban environments. The vehicle is equipped with sensors and control algorithms to navigate, detect and collect plastic bottle wastes, and detect fires in real-time. The system uses an off-the-shelf, small-sized, battery-operated vehicle, a simple conveyor belt, and a vision-based, computerized system. Machine learning (ML-) based vision tasks are implemented to direct the vehicle to waste locations and initiate the waste removal process. A fire detection and alarm system are also incorporated, using a camera and machine learning algorithms to detect flames automatically. The vehicle was tested in a simulated urban environment, and the results demonstrate its effectiveness in waste material collection and fire detection. The proposed system has the potential to improve the efficiency and safety of such tasks in urban areas

Adaptive polyphase subband decomposition structures for image compression

Abstract—Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented. Index Terms—Adaptive polyphase structures, adaptive subband decomposition, image coding, lifting structures. I