Yazar "Saglam, Ali" seçeneğine göre listele

Listeleniyor 1 - 5 / 5
  • Küçük Resim Yok
    Öğe
    Effects of Color Spaces and Distance Norms on Graph-Based Image Segmentation
    (IEEE, 2017) Saglam, Ali; Baykan, Nurdan Akhan
    Use of the graph theory tools in image processing field is growing up with each passing day. Graph theory makes the operations easier for image processing applications, and can represent digital image components completely. In image segmentation processes, the graph theory tools are also used widely. These kinds of image segmentation processes are called graph-based image segmentation. In many image processing applications, it seems as a problem that which color space the color values of pixels should be considered according to and which distance norm should be used to measure the difference between two points in the space. In this work, a graph-based image segmentation algorithm is tested on several color spaces with different distance norms. The test is carried out on 100 real world images that take part in a general-purposed image segmentation dataset. The average segmentation results are given as F-measure in this work with regard to both color spaces and distance norms. The results show that L*a*b* and L*u*v* color spaces are more appropriate than RGB color space, in general. The squared Euclidean distance norm gives more accurate results than the Euclidean distance norm, used in the source paper, if the Gaussian smoothing is not used as pre-processing.
  • Küçük Resim Yok
    Öğe
    An Efficient Object Extraction with Graph-Based Image Segmentation
    (IEEE, 2015) Saglam, Ali; Baykan, Nurdan Akhan
    Object extraction process is a closely related issue with image segmentation process. To separate an image to several segments formed similar pixels, many methods are proposed in the area of image processing. Graph-based image segmentation is also one of the segmentation methods. Because of their representation convenience and ease of use, graphs are used as important tools in many image processing applications. While an image segmentation process runs, the processes splitting a graph to sub graphs and merging sub graphs are carried out in the meanwhile. To fulfill these processes, the method uses some local features such as differences between vertices in the graph, which represent pixels, or global features of the image and its segments. To extract an object from an image, we first segmented the entire image, because of to save global features, or to obtain more accurate segmentation. Finally, we extract the intended object from the image by merging the segments that are inside the area drawn before by us. We test the method on some images in the Segmentation Evaluating Database from Weizmann Institute of Science and evaluate the segmentation results. Our F-measure score values show that it seems noticeable good segmentation.
  • Küçük Resim Yok
    Öğe
    Roof Detection on Satellite Images
    (IEEE, 2017) Saglam, Ali; Baykan, Nurdan Akhan
    In digital image classification processes, image pixels are separated according to some features that they have. Satellite images are digital images that are taken by a satellite vehicle through some sensors which perceive the specific wavelength of the light. In this study, two different digital image classification method (Linear Discriminant Analysis and Normalized Distance Values) have compared to each other, using different color spaces (RGB, L*a*b* and HSV), on the satellite images that have been taken by digital airborne sensors so as to detect roof objects. The common features of the applied methods are those they are supervised because of using training data given previously and they run fast because of operating linearly using a threshold value. For this reason, some of the images in the dataset are used for the purpose of training in order to detect the certain coefficients and the threshold value. The dataset we used for training and testing are the images acquired from ISPRS WG III/4 2D Semantic Labeling database. In the database, the classification ground truth images are also available.
  • Küçük Resim Yok
    Öğe
    Sequential image segmentation based on minimum spanning tree representation
    (ELSEVIER SCIENCE BV, 2017) Saglam, Ali; Baykan, Nurdan Akhan
    Image segmentation is a very important stage in various image processing applications. Segmentation of pixels of an image and clustering of data are closely related to each other. For many graph-based data-clustering methods and many graph-based image-segmentation methods, minimum spanning tree (MST)-based approaches play a crucial role because of their ease of operation and low computational complexity. In this paper, we improve a successful data-clustering algorithm that uses Prim's sequential representation of MST, for the purpose of image segmentation. The algorithm runs by scanning the complete MST structure of the entire image, such that it finds, and then cuts, inconsistent edges among a constantly changing juxtaposed edge string whose elements are obtained from the MST at a specific length. In our method, the length of the string not only determines the edges to compare, but also helps to remove the small, undesired cluster particles. We also develop a new predicate for the cutting criterion. The criterion takes into account several local and global features that differ from image to image. We test our algorithm on a database that consists of real images. The results show that the proposed method can compete with the most popular image segmentation algorithms in terms of low execution time. (C) 2016 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    User Interactive Object Extraction with Sequential Image Segmentation
    (IEEE, 2018) Saglam, Ali; Baykan, Nurdan Akhan
    In this study, a graph-based image segmentation algorithm which was developed in recent years and achieves a significant success in terms of the performance of both the accuracy and speed is used as an intermediate process for a user interactive object extraction method. In the object extraction method developed, the related image is subdivided into segments and, then, these segments are merged according to their label values by using the area determined by the user at first. The image segmentation algorithm used in the scope of this work fulfills a sequential segmentation process on the one dimensional edge array of Prim's minimum spanning tree representation. The algorithm does the segmentation by cutting the specified edges on the tree. According to the method developed, these cut edges are kept and some of them are added to the tree again in the merging stage; so that, the segments at the ends of the edge added are merged. Owing to this process, the process of finding the least weighted edge between the two segments to be merged, which needs to be performed before the merging stage according to the previous studies, is not needed. The method developed here is compared with some methods in the literature on a dataset consist of real life images, and it seems that the method shows a significant superiority to the other methods.