Scale-invariant representation of light field images for object recognition and tracking

Ghasemi, Alireza; Vetterli, Martin

doi:10.1117/12.2041105

Cited by 14 publications

(13 citation statements)

References 24 publications

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“…For the line detection phase, we use the exponential Radon transform (ERT) introduced in [21]. The exponential radon transform is a variant of the well-known Radon transform which is redesigned and optimized for EPI plane analysis.…”

Section: Detecting Flat Surfaces Using the Epi Informationmentioning

confidence: 99%

Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos

Ghasemi

Vetterli

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a novel approach for detecting printed photos from natural scenes using a light-field camera. Our approach exploits the extra information captured by a light-field camera and the multiple views of scene in order to infer a compact feature vector from the variance in the distribution of the depth of the scene. We then use this feature for robust detection of printed photos.Our algorithm can be used in person-based authentication applications to avoid intruding the system using a facial photo. Our experiments show that the energy of the gradients of points in the epipolar domain is highly discriminative and can be used to distinguish printed photos from original scenes.

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Section: Detecting Flat Surfaces Using the Epi Informationmentioning

confidence: 99%

Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos

Ghasemi

Vetterli

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…A number of existing works touch upon exploiting these characteristics for feature detection and description. Ghasemi et al [14] exploit depth information in the LF to build a global, scale-invariant descriptor useful for scene classification, though they do not address the localized features required for 3D reconstruction. Tosic et al [44] employ LF scale and depth to derive an edge-sensitive feature detector.…”

Section: Related Workmentioning

confidence: 99%

LiFF: Light Field Features in Scale and Depth

Dansereau

Girod

Wetzstein

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Feature detectors and descriptors are key low-level vision tools that many higher-level tasks build on. Unfortunately these fail in the presence of challenging light transport effects including partial occlusion, low contrast, and reflective or refractive surfaces. Building on spatio-angular imaging modalities offered by emerging light field cameras, we introduce a new and computationally efficient 4D light field feature detector and descriptor: LiFF. LiFF is scale invariant and utilizes the full 4D light field to detect features that are robust to changes in perspective. This is particularly useful for structure from motion (SfM) and other tasks that match features across viewpoints of a scene. We demonstrate significantly improved 3D reconstructions via SfM when using LiFF instead of the leading 2D or 4D features, and show that LiFF runs an order of magnitude faster than the leading 4D approach. Finally, LiFF inherently estimates depth for each feature, opening a path for future research in light field-based SfM.

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“…For odd n, Qw(y) = sign(y) case of odd n, is not further divided and, in the case of even n, is only divided through the origin. To understand this more precisely, let us consider the projection of the point p onto an image plane at angle α as a function of α: q o (α) = −x 0 sin α + y 0 cos α = p 2 sin(α + β), (6) where β = arctan2(y 0 , −x 0 ). For even n, the quantised version,…”

Section: Orthogonal Projectionmentioning

confidence: 99%

“…However, a formal, quantitive analysis on the accuracy of such recovered information is still lacking. This is surprising given the longstanding interest in these systems [1,2,3] and improvements that they can make to vision algorithms [4,5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

On the accuracy of point localisation in a circular camera-array

Ghasemi

Scholefield

Vetterli

2015

2015 IEEE International Conference on Image Processing (ICIP)

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Although many advances have been made in light-field and camera-array image processing, there is still a lack of thorough analysis of the localisation accuracy of different multi-camera systems. By considering the problem from a frame-quantisation perspective, we are able to quantify the point localisation error of circular camera configurations. Specifically, we obtain closed form expressions bounding the localisation error in terms of the parameters describing the acquisition setup.These theoretical results are independent of the localisation algorithm and thus provide fundamental limits on performance. Furthermore, the new frame-quantisation perspective is general enough to be extended to more complex camera configurations.

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Scale-invariant representation of light field images for object recognition and tracking

Cited by 14 publications

References 24 publications

Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos

Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos

LiFF: Light Field Features in Scale and Depth

On the accuracy of point localisation in a circular camera-array

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