Affine-invariant SIFT descriptor with global context

Cao, Bei; Ma, Caiwen; Liu, ZhenTao

doi:10.1109/cisp.2010.5647071

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Terrestrial photogrammetry has evolved a lot in recent years, allowing more flexibility in data acquisition and better results. In recent years, successful homologous point finding has been achieved among images with very different perspectives (Cao et al, 2010;Lowe, 1999), even taken at different times and under different conditions, and these can then be used for orientation with progressively better accuracies. But there have also been significant improvements in 3D reconstruction, including textureless areas (Tan, 2016).…”

Section: Introductionmentioning

confidence: 99%

Accurate 3d Reconstruction Using a Videogrammetric Device for Heritage Scenarios

Ortiz-Coder

Cabecera

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In recent years, handheld laser scanning systems have been developed for documenting architectural heritage, among other applications. In this article we present a new alternative for the 3D documentation of historical heritage based on videogrammetry. For this purpose, a prototype has been designed with two cameras, a high resolution camera and a VGA camera which, when connected to a tablet, allow the user to establish a guidance system to ensure that the trajectory is not lost and enables highly flexible and long-lasting movements over time. This paper unveils the operation of the filtering and image selection system to finally proceed to an evaluation of the prototype in three areas of an archaeological site, called “Casa del Mitreo” in the city of Mérida (Spain). The results are compared to the Faro Focus 3D X330 laser scanner, yielding very similar accuracies and a capture time about 17 times faster than the 3D laser scanner. The article therefore proposes a real alternative to 3D data acquisition systems in applications for the graphic documentation of architectural and archaeological heritage.

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Section: Introductionmentioning

confidence: 99%

Accurate 3d Reconstruction Using a Videogrammetric Device for Heritage Scenarios

Ortiz-Coder

Cabecera

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Mikolajczyk et al [10] leveraged local feature and edge based information along with a geometric consistency verification for object class recognition. Cao et al [11] present an approach similar to [9] to make SIFT affine invariant. Hao et al [12] and Zhang et al [13] proposed two methods for incorporating the geometry of the scene in image matching using bundles of local features generally termed "visual phrases."…”

Section: Introductionmentioning

confidence: 99%

“…3-Our method matches all the features of one image simultaneously which essentially means they contribute to each others' match. This is different from the existing methods which perform feature matching on an individual basis [9], [10], [11]. 4-A number of methods perform geometric verification by fitting the fundamental matrix to a set of initially discovered correspondences in order to remove the incorrect matches [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs

Zamir

Shah

2014

IEEE Trans. Pattern Anal. Mach. Intell.

180

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Abstract-In this paper, we present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing consistency among their global features (e.g., GIST) using GMCP. In this context, we argue that using a robust distance function for finding the similarity between the global features is essential for the cases where the query matches multiple reference images with dissimilar global features. Towards this end, we propose a robust distance function based on the Gaussian Radial Basis Function (G-RBF). We evaluated the proposed framework on a new data set of 102k street view images; the experiments show it outperforms the state of the art by 10 percent.

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Improved global context descriptor for describing interest regions

Liu

Zeng

2012

J. Shanghai Jiaotong Univ. (Sci.)

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Affine-invariant SIFT descriptor with global context

Cited by 5 publications

References 12 publications

Accurate 3d Reconstruction Using a Videogrammetric Device for Heritage Scenarios

Accurate 3d Reconstruction Using a Videogrammetric Device for Heritage Scenarios

Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs

Improved global context descriptor for describing interest regions

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