Alignment of uncalibrated images for multi-view classification

Download

index.pdf

Date

2011-12-29

Author

Arık, Sercan Ömer
Vural, Elif
Frossard, Pascal

Metadata

Show full item record

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

Item Usage Stats

264
views

0
downloads

Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pairwise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of images is necessary prior to distance computation. We propose a method for the registration of uncalibrated images that capture the same 3D scene or object. We model the depth map of the scene as an algebraic surface, which yields a warp model in the form of a rational function between image pairs. The warp model is computed by minimizing the registration error, where the registered image is a weighted combination of two images generated with two different warp functions estimated from feature matches and image intensity functions in order to provide robust registration. We demonstrate the flexibility of our alignment method by experimentation on several wide-baseline image pairs with arbitrary scene geometries and texture levels. Moreover, the results on multi-view image classification suggest that the proposed alignment method can be effectively used in graph-based classification algorithms for the computation of pairwise distances where it achieves significant improvements over distance computation without prior alignment.

Subject Keywords

Image alignment, Image registration, Image warping, Multi-view image classification, Graph-based classification

URI

https://hdl.handle.net/11511/36448

DOI

https://doi.org/10.1109/icip.2011.6116116

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Analysis of nanoparticle Transmission Electron Microscopy data using a public-domain image-processing program, Image Woehrle, GH; Hutchison, JE; Özkar, Saim; Finke, RG (2006-01-01) The need to easily and quickly count larger numbers of nanoparticles, in order to obtain statistically useful size and size-distribution data, is addressed via the use of a readily available, free, public-domain program for particle counting, NIH-Image (and 2 others derived from it, Scion Image and Image J), collectively referred to herein as Image. The best protocols that we have found useful for the use of Image are reported; both appropriate as well as problematic applications of Image are then illustrat...
Efficient algorithms for convolutional inverse problems in multidimensional imaging Doğan, Didem; Öktem, Figen S.; Department of Electrical and Electronics Engineering (2020) Computational imaging is the process of indirectly forming images from measurements using image reconstruction algorithms that solve inverse problems. In many inverse problems in multidimensional imaging such as spectral and depth imaging, the measurements are in the form of superimposed convolutions related to the unknown image. In this thesis, we first provide a general formulation for these problems named as convolutional inverse problems, and then develop fast and efficient image reconstruction algorith...
Design and implementation of a novel visual analysis system for image clasiffication Altintakan, Ümit Lütfü; Yazıcı, Adnan; Körpeoğlu, İbrahim; Department of Computer Engineering (2013) Possibilities offered by the technology to create, share and disseminate image and video data have resulted in a rapid increase in the available visual data. However, the data is useless unless it is effectively accessed, which necessitates the semantic analysis of visual data. In this dissertation, we present a novel visual analysis system along with its application to image classification problem. We aim to address the challenges in the area originated from the semantic gap, and to facilitate the research...
A Shadow based trainable method for building detection in satellite images Dikmen, Mehmet; Halıcı, Uğur; Department of Geodetic and Geographical Information Technologies (2014) The purpose of this thesis is to develop a supervised building detection and extraction algorithm with a shadow based learning method for high-resolution satellite images. First, shadow segments are identified on an over-segmented image, and then neighboring shadow segments are merged by assuming that they are cast by a single building. Next, these shadow regions are used to detect the candidate regions where buildings most likely occur. Together with this information, distance to shadows towards illuminati...
Automated building detection from satellite images by using shadow information as an object invariant Yüksel, Barış; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2012) Apart from classical pattern recognition techniques applied for automated building detection in satellite images, a robust building detection methodology is proposed, where self-supervision data can be automatically extracted from the image by using shadow and its direction as an invariant for building object. In this methodology; first the vegetation, water and shadow regions are detected from a given satellite image and local directional fuzzy landscapes representing the existence of building are generate...

Citation Formats

S. Ö. Arık, E. Vural, and P. Frossard, “Alignment of uncalibrated images for multi-view classification,” 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36448.