Change detection in aerial images

Date

2004-01-01

Author

Borchani, M
Cloppet, F
Atalay, Mehmet Volkan
Stamon, G

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

194
views

0
downloads

This paper deals with how to characterize texture and how to get a good description of images with a minimal number of parameters. This procedure is more objective than textual data. Texture characterization has been used in a matching system to detect changes in couples of aerial images taken at two different times using different order of statistics to describe images. The results are quite encouraging.

Subject Keywords

Image retrieval, Image matching, Texture, Classification, Neural network

URI

https://hdl.handle.net/11511/48025

DOI

https://doi.org/10.1109/cccrv.2004.1301467

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Contrast Enhancement of Microscopy Images Using Image Phase Information Çakır, Serhat; Atalay, Rengül; ÇETİN, AHMET ENİS (2018-01-01) Contrast enhancement is an important preprocessing step for the analysis of microscopy images. The main aim of contrast enhancement techniques is to increase the visibility of the cell structures and organelles by modifying the spatial characteristics of the image. In this paper, phase information-based contrast enhancement framework is proposed to overcome the limitations of existing image enhancement techniques. Inspired by the groundbreaking design of the phase contrast microscopy (PCM), the proposed ima...
Shape : representation, description, similarity and recognition Arıca, Nafiz; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2003) In this thesis, we study the shape analysis problem and propose new methods for shape description, similarity and recognition. Firstly, we introduce a new shape descriptor in a two-step method. In the first step, the 2-D shape information is mapped into a set of 1-D functions. The mapping is based on the beams, which are originated from a boundary point, connecting that point with the rest of the points on the boundary. At each point, the angle between a pair of beams is taken as a random variable to define...
Shape recognition with generalized beam angle statistics Tola, OO; Arica, N; Yarman-Vural, F (2004-01-01) In this study, we develop a new shape descriptor and a matching algorithm in order to find a given template shape in an edge detected image without extracting the boundary. The shape descriptor based on Generalized Beam Angle Statistics (GBAS) defines the angles between the lines connecting each boundary point with the rest of the points, as random variable. Then, it assigns a feature vector to each point using the moments of beam angles. The proposed matching algorithm performs shape recognition by matchin...
Image Annotation With Semi-Supervised Clustering Sayar, Ahmet; Yarman Vural, Fatoş Tunay (2009-09-16) 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 three types of side information. The first one is the topic probability information obtained from the text document associated with the image. The second is the orientation an...
Shape recognition with generalized beam angle statistics Tola, OO; Arica, N; Yarman Vural, Fatoş Tunay (2004-04-30) In this study, we develop a new shape descriptor and matching algorithm in order to find a given template shape in an edge detected image without performing boundary extraction. The shape descriptor based on Generalized Beam Angle Statistics (GBAS) defines the angles between the lines connecting each boundary point with the rest of the points, as random variable. Then, it assigns a feature vector to each point using the moments of beam angles. The proposed matching algorithm performs shape recognition by ma...

Citation Formats

M. Borchani, F. Cloppet, M. V. Atalay, and G. Stamon, “Change detection in aerial images,” 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48025.