Special Issue "New Trends in Image Processing for Cultural Heritage"

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 October 2018).

Special Issue Editors

Dr. Filippo Stanco
E-Mail Website
Guest Editor
Dipartimento di Matematica ed Informatica, Università di Catania, Viale A. Doria, 6, 95125 Catania, Italy
Interests: image processing; multimedia; cultural heritage; pattern recognition
Dr. Dario Allegra
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Interests: machine learning; computer vision; pattern recognition; image processing; cultural heritage

Special Issue Information

Dear Colleagues,

In the last few years, the technologies related to imaging, video processing, computer graphics, 3D modelling and multimedia have been greatly employed in field of cultural heritage preservation and exploitation. Indeed, cultural artifacts need to be captured, analyzed, restored, as well as classified, recognized, and rendered. The continuous development of these technologies leads researchers to propose new methodologies and applications in this field. Moreover, recent image-processing and machine learning algorithms give the opportunity to process datasets of artifacts, in order to extract information and develop new analysis procedures.

The intent of this Special Issue is two-fold: Firstly, present novel applications of the modern devices for data acquisition and visualization (e.g., depth sensors, 3D scanners, VR glasses, robots, etc.); and, secondly, propose new methodologies for large dataset processing using modern pattern recognition and machine learning approaches (e.g., deep learning, hypergraph learning, etc.).

The proposed Special Issue, named “New Trends in Image Processing for Cultural Heritage”, includes (but it is not limited) the following topics:

  • 3D Reconstruction
  • 3D Models processing
  • Augmented and Virtual Reality applications
  • Robotic applications
  • RGBD Analysis
  • Serious Game for Cultural Heritage
  • Advanced Image enhancement and denoising
  • Advanced Image classification and retrieval
  • Semantic segmentation
  • Virtual restoration
  • Image processing

Dr. Filippo Stanco
Dr. Dario Allegra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 3D models
  • Enhancement
  • Learning
  • Classification
  • Virtual Reality
  • Serious Game
  • Image Processing

Published Papers (3 papers)

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Research

Article
Airborne Optical Sectioning
J. Imaging 2018, 4(8), 102; https://doi.org/10.3390/jimaging4080102 - 13 Aug 2018
Cited by 10 | Viewed by 3875
Abstract
Drones are becoming increasingly popular for remote sensing of landscapes in archeology, cultural heritage, forestry, and other disciplines. They are more efficient than airplanes for capturing small areas, of up to several hundred square meters. LiDAR (light detection and ranging) and photogrammetry have [...] Read more.
Drones are becoming increasingly popular for remote sensing of landscapes in archeology, cultural heritage, forestry, and other disciplines. They are more efficient than airplanes for capturing small areas, of up to several hundred square meters. LiDAR (light detection and ranging) and photogrammetry have been applied together with drones to achieve 3D reconstruction. With airborne optical sectioning (AOS), we present a radically different approach that is based on an old idea: synthetic aperture imaging. Rather than measuring, computing, and rendering 3D point clouds or triangulated 3D meshes, we apply image-based rendering for 3D visualization. In contrast to photogrammetry, AOS does not suffer from inaccurate correspondence matches and long processing times. It is cheaper than LiDAR, delivers surface color information, and has the potential to achieve high sampling resolutions. AOS samples the optical signal of wide synthetic apertures (30–100 m diameter) with unstructured video images recorded from a low-cost camera drone to support optical sectioning by image integration. The wide aperture signal results in a shallow depth of field and consequently in a strong blur of out-of-focus occluders, while images of points in focus remain clearly visible. Shifting focus computationally towards the ground allows optical slicing through dense occluder structures (such as leaves, tree branches, and coniferous trees), and discovery and inspection of concealed artifacts on the surface. Full article
(This article belongs to the Special Issue New Trends in Image Processing for Cultural Heritage)
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Article
Long-Term Monitoring of Crack Patterns in Historic Structures Using UAVs and Planar Markers: A Preliminary Study
J. Imaging 2018, 4(8), 99; https://doi.org/10.3390/jimaging4080099 - 02 Aug 2018
Cited by 17 | Viewed by 2631
Abstract
This paper describes how Unmanned Aerial Vehicles (UAVs) may support the long-term monitoring of crack patterns in the context of architectural heritage preservation. In detail, this work includes: (i) a state of the art about the most used techniques in ancient structural monitoring; [...] Read more.
This paper describes how Unmanned Aerial Vehicles (UAVs) may support the long-term monitoring of crack patterns in the context of architectural heritage preservation. In detail, this work includes: (i) a state of the art about the most used techniques in ancient structural monitoring; (ii) the description of the implemented methods, taking into account the requirements and constraints of the case study; (iii) the results of the experimentation carried out in the lab; and (iv) conclusions and future works. Full article
(This article belongs to the Special Issue New Trends in Image Processing for Cultural Heritage)
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Article
A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents
J. Imaging 2018, 4(8), 97; https://doi.org/10.3390/jimaging4080097 - 01 Aug 2018
Cited by 2 | Viewed by 2445
Abstract
Recently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and [...] Read more.
Recently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set. Full article
(This article belongs to the Special Issue New Trends in Image Processing for Cultural Heritage)
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