Photometric stereo for strong specular highlights

Khanian, Maryam; Boroujerdi, Ali Sharifi; Breuß, Michael

doi:10.48550/arxiv.1709.01357

Cited by 2 publications

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“…Let us remark that ( 19) can be easily extended to include more realistic reflectance [15,16] and lighting [28,31] models, as well as more robust estimators [14,33]: this only requires to change the definition of f , which stands for the global reprojection error E R .…”

Section: Discretisationmentioning

confidence: 99%

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Optimisation of Classic Photometric Stereo by Non-convex Variational Minimisation

et al. 2018

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Estimating shape and appearance of a three dimensional object from a given set of images is a classic research topic that is still actively pursued. Among the various techniques available, photometric stereo is distinguished by the assumption that the underlying input images are taken from the same point of view but under different lighting conditions. The most common techniques provide the shape information in terms of surface normals. In this work, we instead propose to minimise a much more natural objective function, namely the reprojection error in terms of depth. Minimising the resulting non-trivial variational model for photometric stereo allows to recover the depth of the photographed scene directly. As a solving strategy, we follow an approach based on a recently published optimisation scheme for non-convex and non-smooth cost functions.The main contributions of our paper are of theoretical nature. A technical novelty in our framework is the usage of matrix differential calculus. We supplement our approach by a detailed convergence analysis of the resulting optimisation algorithm and discuss possibilities to ease the computational complexity. At hand of an experimental evaluation we discuss important properties of the method. Overall, our strategy achieves more accurate results than competing approaches. The experiments also highlights some practical aspects of the underlying optimisation algorithm that may be of interest in a more general context. G. Radow · L. Hoeltgen · M. Breuß Chair for Applied Mathematics

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Section: Discretisationmentioning

confidence: 99%

“…As to complete our brief review of some general aspects of PS, let us note that it is possible to extend Woodham's classic PS model as for instance to non-Lambertian reflectance as e.g. in [2,14,16,21,37], or to take into account several types of lighting in a scene, see e.g. [3,31].…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Classic Photometric Stereo by Non-convex Variational Minimisation

et al. 2018

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“…Specular highlights often exist in real-world images due to the material property of objects and the capturing environments. It is always desired to reduce or eliminate these specular highlights to improve the visual quality and to facilitate the vision and graphics tasks, such as stereo matching [10,12], text recognition [16], image segmentation [3,6] and photo-consistency [30,31]. See Fig.…”

Section: Introductionmentioning

confidence: 99%

Text-Aware Single Image Specular Highlight Removal

Hou¹,

Wang²,

Quan³

et al. 2021

Preprint

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Removing undesirable specular highlight from a single input image is of crucial importance to many computer vision and graphics tasks. Existing methods typically remove specular highlight for medical images and specific-object images, however, they cannot handle the images with text. In addition, the impact of specular highlight on text recognition is rarely studied by text detection and recognition community. Therefore, in this paper, we first raise and study the text-aware single image specular highlight removal problem. The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images. To tackle this challenging problem, we first collect three high-quality datasets with fine-grained annotations, which will be appropriately released to facilitate the relevant research. Then, we design a novel two-stage network, which contains a highlight detection network and a highlight removal network. The output of highlight detection network provides additional information about highlight regions to guide the subsequent highlight removal network. Moreover, we suggest a measurement set including the end-to-end text detection and recognition evaluation and auxiliary visual quality evaluation. Extensive experiments on our collected datasets demonstrate the superior performance of the proposed method.

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Photometric stereo for strong specular highlights

Cited by 2 publications

References 0 publications

Optimisation of Classic Photometric Stereo by Non-convex Variational Minimisation

Optimisation of Classic Photometric Stereo by Non-convex Variational Minimisation

Text-Aware Single Image Specular Highlight Removal

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