Image segmentation using a two dimensional genetic algorithm

Mut, Oğuz


Image segmentation based on variational techniques
Duramaz, Alper; Ünver, Baki Zafer; Department of Electrical and Electronics Engineering (2006)
Recently, solutions to the problem of image segmentation and denoising are developed based on the Mumford-Shah model. The model provides an energy functional, called the Mumford-Shah functional, which should be minimized. Since the minimization of the functional has some difficulties, approximate approaches are proposed. Two such methods are the gradient flows method and the Chan-Vese active contour method. The performance evolution in terms of speed shows that the gradient flows method converges to the bou...
Image segmentation based on variational techniques
Altınoklu, Metin Burak; Ünver, Baki Zafer; Department of Electrical and Electronics Engineering (2009)
In this thesis, the image segmentation methods based on the MumfordShah variational approach have been studied. By obtaining an optimum point of the Mumford-Shah functional which is a piecewise smooth approximate image and a set of edge curves, an image can be decomposed into regions. This piecewise smooth approximate image is smooth inside of regions, but it is allowed to be discontinuous region wise. Unfortunately, because of the irregularity of the Mumford Shah functional, it cannot be directly used for ...
Image segmentation by fusion of low level and domain specific information via Markov Random Fields
Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2014-09-01)
We propose a new segmentation method by fusing a set of top-down and bottom-up segmentation maps under the Markov Random Fields (MRF) framework. The bottom-up segmentation maps are obtained by varying the parameters of an unsupervised segmentation method, such as Mean Shift. The top-down segmentation maps are constructed from some priori information, called domain specific information (DSI), received from a domain expert in the form of general properties about the image dataset. The properties are then used...
Image Annotation by Semi-Supervised Clustering Constrained by SIFT Orientation Information
Sayar, Ahmet; Yarman-Vural, Fatos T. (2008-10-29)
Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using the orientation information assigned to each interest point of Scale-invariant feature transform (SIFT) features to generate a visual codebook. The orientation information pro...
Image segmentation with Improved region modeling
Ersoy, Ozan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2004)
Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Image segmentation methods can be classified into four main categories: 1) clustering methods, 2) region-based methods, 3) ...
Citation Formats
O. Mut, “Image segmentation using a two dimensional genetic algorithm,” Middle East Technical University, 2001.