Efficient Depth Edge Detection Using Structured Light

Park, Jiyoung; Kim, Cheolhwon; Yi, Juneho; Turk, Matthew

doi:10.1007/11595755_94

Cited by 3 publications

(2 citation statements)

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“…Other methods try to overcome this ambiguity by leveraging additional information, such as structured light [11], or a modified camera with physically offset light sources [12]. While both of these methods yield high quality results, they require extra hardware and modification of the original scene, which is not possible in all use cases.…”

Section: A Edge Detectionmentioning

confidence: 99%

Distinguishing Texture Edges From Object Boundaries in Video

Wang

Dümcke

Smolic

et al. 2013

IEEE Trans. on Image Process.

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One of the most fundamental problems in image processing and computer vision is the inherent ambiguity that exists between texture edges and object boundaries in real-world images and video. Despite this ambiguity, many applications in computer vision and image processing often use image edge strength with the assumption that these edges approximate object depth boundaries. However, this assumption is often invalidated by real world data, and this discrepancy is a significant limitation in many of today's image processing methods. We address this issue by introducing a simple, low-level, and patch-consistency assumption that leverages the extra information present in video data to resolve this ambiguity. Through analyzing how well patches can be modeled by simple transformations over time, we can obtain an indication of which image edges correspond to texture edges versus object boundaries. Our approach is simple to implement and has the potential to improve a wide range of image and video-based applications by suppressing the detrimental effects of strong texture edges on regularization terms. We validate our approach by presenting results on a variety of scene types and directly incorporating our augmented edge map into existing image segmentation and optical flow applications, showing results that better correspond to object boundaries.

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Section: A Edge Detectionmentioning

confidence: 99%

Distinguishing Texture Edges From Object Boundaries in Video

Wang

Dümcke

Smolic

et al. 2013

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Although very attractive, it only works where shadows can be reliably created. Recently, we used structured light patterns to efficiently detect depth edges [15], and presented basic ideas to control key parameters such as stripe width given the detectable depth difference. This paper is an extended and enhanced version of that work.…”

Section: Related Workmentioning

confidence: 99%