A matching algorithm based on linear features

Date

1998-07-01

Author

Atalay, Mehmet Volkan

Metadata

Show full item record

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

Item Usage Stats

139
views

0
downloads

A two step feature matching algorithm which is primarily aimed at problems related to the analysis of aerial images of man-made sites is presented. Only linear features and their geometric attributes are used in the algorithm. First, the rotation between the two images is calculated and then matching by relaxation is performed assuming that there is only translation.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/47230

Journal

PATTERN RECOGNITION LETTERS

DOI

https://doi.org/10.1016/s0167-8655(98)00060-9

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology AYTEKİN, orsan; Ulusoy, İlkay (Elsevier BV, 2011-10-01) The morphological profile (MP) and differential morphological profile (DMP) have been used extensively to acquire spatial information to be used in the segmentation of very high resolution (VHR) remotely sensed images. In most of the previous approaches, the maxima of the MP and DMP were investigated to estimate the best representative scale in the spatial domain for the pixel under consideration. Then, the object type (i.e. dark, bright or flat) was estimated based on the location of the maximum. Finally, ...
A similarity-based approach for shape classification using Asian skeletons Erdem, Aykut; Tarı, Zehra Sibel (Elsevier BV, 2010-10-01) Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. However, their potential for shape classification has not been investigated much. In this study, we present a similarity-based approach for classifying 2D shapes based on their Asian skeletons (Asian and Tan, 2005; Aslan et al., 2008). The coarse structure of this skeleton representation allows us to represent each shape category in the form of a reduced set of prot...
A tracker-aware detector threshold optimization formulation for tracking maneuvering targets in clutter Aslan, Murat Samil; Saranlı, Afşar (Elsevier BV, 2011-09-01) In this paper, we consider a tracker-aware radar detector threshold optimization formulation for tracking maneuvering targets in clutter. The formulation results in an online method with improved transient performance. In our earlier works, the problem was considered in the context of the probabilistic data association filter (PDAF) for non-maneuvering targets. In the present study, we extend the ideas in the PDAF formulation to the multiple model (MM) filtering structures which use PDAFs as modules. Althou...
A statistical approach to sparse multi-scale phase-based stereo Ulusoy, İlkay (Elsevier BV, 2007-09-01) In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic ...
Low-level multiscale image segmentation and a benchmark for its evaluation Akbaş, Emre (Elsevier BV, 2020-10-01) In this paper, we present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Each region is associated with a scale that d...

Citation Formats

M. V. Atalay, “A matching algorithm based on linear features,” PATTERN RECOGNITION LETTERS, pp. 857–867, 1998, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47230.