Detection of Edges from Polynomial Texture Maps

Pan, Rongjiang

doi:10.1007/s13319-016-0081-z

Cited by 3 publications

(8 citation statements)

References 10 publications

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“…They combine an ambient signal and the four photographs lit by different light sources by using several differences and ratio images. Other techniques exploit the variation of the parameters of a particular MLIC fitting model; for instance, Pan [Pan16] visualizes surface edges by computing them directly from Polynomial Texture Map coefficients (Sec. 5.3); the probability of having an edge is proportional to the largest variation of one of its six parameters in terms of direction and magnitude.…”

Section: Methodsmentioning

confidence: 99%

“…Look, for instance, at the Outlier Direction map of the infrared channel, which makes a goldish region of the knight's hat pop up; this particular behaviour is really hard to spot in common photographs of that painting. Finally, other approaches visualize several maps obtained by combining relightings, e.g., the difference between the same relighting of two acquisitions of the same object made at different times [PSM05], or computing Di Zenzo gradients [Pan16, GCD∗17]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…11 presents a comparative view of different signals; one is the Static Multi‐Light enhancement presented in Sec. 5.5, and the other are the Di Zenzo gradient map [Pan16, GCD∗17], a contrast‐enhanced normal map, and the Outlier Direction map. Arrows in the latter prove how those types of derived data are capable of make relief patterns across the object surface more visible and readable.…”

Section: Methodsmentioning

confidence: 99%

“…For shape understanding, feature maps are used in support of visualization and analysis at all scales. Approaches include both micro‐ and meso‐details (sub‐pixel geometric structures [TLQ06], normal/gradient maps or discontinuity maps [Pan16, BCDG13]), and more global shape information (e.g., visualization and analysis of structures such as leaf venations [ZHS∗18] or inscription [Ham15] supported by depth maps obtained from the integration of normal and/or shadow maps. When dealing with appearance, the main use is in support of material characterization and segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…Another common practice in the CH community is related to the use of illustrative enhancements (Sec.5.5), such as Diffuse gain, Specular enhancement , and Unsharp masking , to intensify the detail perception on tablets [WVM∗05, PCC∗10], petroglyphs or written stones [UW13, MCPC17], mural graffiti [CSS15], coins [KK13], or, generally, metallic alloys [Mac18]. In this context, a major research need is the enhancement of legibility and readability of written texts or engraved symbols [EBB∗11], and the processing of MLICs can lead to the extraction of meaningful lines and inscriptions on tablets [BCDG13, Pan16]. Further, an advanced application to automatically recover line drawings of inscriptions has been demonstrated on a collection of Mesopotamian clay cones [Ham15].…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Pintus

Dulecha

Ciortan

et al. 2019

Computer Graphics Forum

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Multi‐Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up‐to‐date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Pintus

Dulecha

Ciortan

et al. 2019

Computer Graphics Forum

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A novel framework for highlight reflectance transformation imaging

Giachetti

Ciortan

Daffara

et al. 2018

Computer Vision and Image Understanding

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We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.

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Quantitative evaluation of the Reflectance Transformation Imaging and Normal Integration technique in profilometric application.

Verni

Fiocco

Grifoni

et al. 2022

J. Phys.: Conf. Ser.

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This article provides a quantitative analysis of the ability to faithfully reproduce the profile of objects using the Reflectance Transformation Imaging (RTI) coupled with Normal Integration technique. The study was conducted using ad hoc designed and built reference targets. It was possible to compare the profiles obtained using RTI technique with those realized using more conventional profilometric and 3D techniques such as micro photogrammetry, laser scanner and optical profilometry. It has been seen that the RTI technique is especially valid in the reproduction of high spatial frequencies and that it is very suitable for the study of cultural heritage, as it is portable, fast and low-cost.

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Detection of Edges from Polynomial Texture Maps

Cited by 3 publications

References 10 publications

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

A novel framework for highlight reflectance transformation imaging

Quantitative evaluation of the Reflectance Transformation Imaging and Normal Integration technique in profilometric application.

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