Detecting planes and estimating their orientation from a single image

Haines, Osian; Calway, Andrew

doi:10.5244/c.26.31

Cited by 17 publications

(13 citation statements)

References 22 publications

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“…Greinera et al [4] have proposed a method to determine the surface normal using projective geometry and spectral analysis. Haines et al [5] describe a technique that makes use of prior training data gathered in an urban environment to classify planar/non-planar surfaces and to compute the orientaion of the planes.…”

Section: Normal Inference From a Single Motion Blurred Imagementioning

confidence: 99%

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Framework for Processing Videos in the Presence of Spatially Varying Motion Blur

Rajagopalan¹

2016

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Indian Institute of Technology Madras IIT Madras Chennai 600036 India PERFORMING ORGANIZATION REPORT NUMBERN/A SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)AOARD UNIT 45002 APO AP 96338-5002 SPONSOR/MONITOR'S ACRONYM(S)AFRL/AFOSR/IOA(AOARD) SPONSOR/MONITOR'S REPORT NUMBER(S)AOARD-134138 DISTRIBUTION/AVAILABILITY STATEMENTDistribution A: Approved for public release. Distribution is unlimited SUPPLEMENTARY NOTES ABSTRACTMotion blurring is both a bane and a boon. Most works treat motion blur as nuisance and seek ways and means to mitigate its effects so as to restore the original image. Unlike the optical blur, motion-blur can be space-varying even when the scene is planar; an example case is that of a rotating camera imaging a distant scene. However, it must be emphasized that motion blur can also serve as a vital cue for camera motion estimation, depth recovery, super-resolution, and image forensics.

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Section: Normal Inference From a Single Motion Blurred Imagementioning

confidence: 99%

“…From equation (5), the extremity of a PSF (say h 1 ) due to translation (say T Xp ) can be expressed as…”

Section: Normal From Point-correspondencesmentioning

confidence: 99%

Framework for Processing Videos in the Presence of Spatially Varying Motion Blur

Rajagopalan¹

2016

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“…For example, Haines and Calway [14] use a machine learning approach to perform plane detection. This work uses Markov random fields to segment the image into planer regions with their corresponding orientations the image is segmented into planar regions.…”

Section: Detection Of Planar Surfacesmentioning

confidence: 99%

Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

et al. 2017

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Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.

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“…This includes semantic segmentation [9], depth estimation from a single image [13,23], generation of a "blocks world" model through physical reasoning [10], estimating planar structure [11,12], and modelling 3D scenes using familiar objects [26]. Bao and Savarese [2] present a structure from motion system which, like us, uses the notion of geometric consistency of detected objects across two views.…”

Section: Related Workmentioning

confidence: 99%

Place recognition from disparate views

Frampton¹,

Calway²

2013

Procedings of the British Machine Vision Conference 2013

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Visual place recognition methods which use image matching techniques have shown success in recent years, however their reliance on local features restricts their use to images which are visually similar and which overlap in viewpoint. We suggest that a semantic approach to the problem would provide a more meaningful relationship between views of a place and so allow recognition when views are disparate and database coverage is sparse. As initial work towards this goal we present a system which uses detected objects as the basic feature and demonstrate promising ability to recognise places from arbitrary viewpoints. We build a 2D place model of object positions and extract features which characterise a pair of models. We then use distributions learned from training examples to compute the probability that the pair depict the same place and also an estimate of the relative pose of the cameras. Results on a dataset of 40 urban locations show good recognition performance and pose estimation, even for highly disparate views.

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Detecting planes and estimating their orientation from a single image

Cited by 17 publications

References 22 publications

Framework for Processing Videos in the Presence of Spatially Varying Motion Blur

Framework for Processing Videos in the Presence of Spatially Varying Motion Blur

Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

Place recognition from disparate views

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