Mesh saliency

Lee, Chang Ha; Varshney, Amitabh; Jacobs, David W.

doi:10.1145/1073204.1073244

Cited by 515 publications

(268 citation statements)

References 43 publications

(44 reference statements)

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“…While this formulation could be used as an alternative mathematical framework in current work, the required planar parameterization introduces an additional level of complexity that the currently proposed method avoids. [12] proposes a mesh saliency method, based on the center-surround operator, adapted from the visual attention literature. Photometric information is not taken into account by these methods.…”

Section: Related Workmentioning

confidence: 99%

Surface feature detection and description with applications to mesh matching

Zaharescu¹,

Boyer²,

Varanasi³

et al. 2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

114

149

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In this paper we revisit local feature detectors/descriptors developed for 2D images and extend them to the more general framework of scalar fields defined on 2D manifolds. We provide methods and tools to detect and describe features on surfaces equiped with scalar functions, such as photometric information. This is motivated by the growing need for matching and tracking photometric surfaces over temporal sequences, due to recent advancements in multiple camera 3D reconstruction. We propose a 3D feature detector (MeshDOG) and a 3D feature descriptor (MeshHOG) for uniformly triangulated meshes, invariant to changes in rotation, translation, and scale. The descriptor is able to capture the local geometric and/or photometric properties in a succinct fashion. Moreover, the method is defined generically for any scalar function, e.g., local curvature. Results with matching rigid and non-rigid meshes demonstrate the interest of the proposed framework.

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Section: Related Workmentioning

confidence: 99%

Surface feature detection and description with applications to mesh matching

Zaharescu¹,

Boyer²,

Varanasi³

et al. 2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

114

149

View full text Add to dashboard Cite

show abstract

“…We next present a few example of how salience is defined and used for visual datasets to enhance their depiction. Notable advances include defining saliency for polygonal meshes [30], comparing it to human eye movements [26], and illuminating meshes based on saliency [29]. Howlett et al [15] use eye-tracking data to identify salient features on meshes and carry out user studies to validate their findings.…”

Section: Visual Data Analysismentioning

confidence: 99%

Saliency-Assisted Navigation of Very Large Landscape Images

Varshney

2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-The field of visualization has addressed navigation of very large datasets, usually meshes and volumes. Significantly less attention has been devoted to the issues surrounding navigation of very large images. In the last few years the explosive growth in the resolution of camera sensors and robotic image acquisition techniques has widened the gap between the display and image resolutions to three orders of magnitude or more. This paper presents the first steps towards navigation of very large images, particularly landscape images, from an interactive visualization perspective. The grand challenge in navigation of very large images is identifying regions of potential interest. In this paper we outline a three-step approach. In the first step we use multi-scale saliency to narrow down the potential areas of interest. In the second step we outline a method based on statistical signatures to further cull out regions of high conformity. In the final step we allow a user to interactively identify the exceptional regions of high interest that merit further attention. We show that our approach of progressive elicitation is fast and allows rapid identification of regions of interest. Unlike previous work in this area, our approach is scalable and computationally reasonable on very large images. We validate the results of our approach by comparing them to user-tagged regions of interest on several very large landscape images from the Internet.

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“…[2] defines a measure of mesh saliency using a center-surround operator on Gaussianweighted mean curvatures. This work incorporates insights from human perception, while the extraction of interesting feature parts is not their concern.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Salient Features for Analyzing 3D Shapes

Yang

Shen

2012

J. Comput. Sci. Technol.

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Extracting feature regions on mesh models is crucial for shape analysis and understanding. It can be widely used for various 3D content-based applications in graphics and geometry field. In this paper, we present a new algorithm of extracting multi-scale salient features on meshes. This is based on robust estimation of curvature on multiple scales. The coincidence between salient feature and the scale of interest can be established straightforwardly, where detailed feature appears on small scale and feature with more global shape information shows up on large scale. We demonstrate this kind of multi-scale description of features accords with human perception and can be further used for several applications as feature classification and viewpoint selection. Experiments exhibit that our method as a multi-scale analysis tool is very helpful for studying 3D shapes.

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Mesh saliency

Cited by 515 publications

References 43 publications

Surface feature detection and description with applications to mesh matching

Surface feature detection and description with applications to mesh matching

Saliency-Assisted Navigation of Very Large Landscape Images

Multi-Scale Salient Features for Analyzing 3D Shapes

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