scholarly journals Predicting Camera Color Quality

2021 ◽  
Vol 2021 (1) ◽  
pp. 61-64
Author(s):  
Roy S. Berns
Keyword(s):  
Cultural Heritage ◽  
Computational Experiment ◽  
Image Data ◽  
Color Difference ◽  
Camera Systems ◽  
Color Quality ◽  
Standard Observer ◽  
Colorimetric Data ◽  
Synthetic Images

Color quality can be measured two ways. The first is target based where color-difference statistics are reported comparing image data with measurement-based colorimetric data. The second is based on measuring the camera sensor’s spectral sensitivities and calculating their similarity to a standard observer, for example, μ-factor. A computational experiment was performed where synthetic images of a variety of targets were rendered for four camera systems having μ-factors of 0.79, 0.88, 0.94, and 0.99. Each camera was profiled using the same target. Although profile color accuracy was acceptable for all the cameras, this did not predict the color accuracy for independent targets. μ-factor was a better predictor of color quality and its use is recommended when evaluating cameras for cultural heritage applications

scholarly journals Evaluation of Cleaning Processes Using Colorimetric and Spectral Data for the Removal of Layers of Limewash from Medieval Plasterwork

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7147
Author(s):  
Miguel Ángel Martínez-Domingo ◽  
Ana Isabel Calero Castillo ◽  
Eva Vivar García ◽  
Eva M. Valero
Keyword(s):  
Cultural Heritage ◽  
Hyperspectral Imaging ◽  
Color Difference ◽  
Cleaning Process ◽  
Heritage Preservation ◽  
The Mean ◽  
Cultural Heritage Preservation ◽  
Conservation Interventions

In the cultural heritage preservation of medieval buildings, it is common to find plaster walls covered in lime, which previously were painted in polychromy. The conservation interventions usually try to remove the whitewash, whilst maintaining the original color of the painted wall as much as possible. However, there is no agreement on which cleaning technique best preserves the original appearance of the colored plaster. Different pigments found below the lime layer may behave differently depending on the cleaning technique used. Usually, colorimetric or photometric area-based measurements are carried out to study the color of the cleaned areas to compare with their original color, obtained from pre-made plaster probes. However, this methodology fails when the mean color difference is not enough to fully characterize the changes in texture and color appearance. This study presents a set of experiments carried out using two different pigments (cinnabar and malachite) covered with lime, and treated with nine different cleaning techniques on plaster probes prepared according to medieval techniques. We have studied the effect of the cleaning process on the color and the homogeneity of the samples using a hyperspectral imaging workflow. Four different analysis methods are presented and discussed. Our results show that the proposed analysis is able to provide a much more comprehensive and diversified characterization of the quality of the cleaning method compared to the commonly used colorimetric or photometric area-based measurements.

scholarly journals Leveraging Known Data for Missing Label Prediction in Cultural Heritage Context

2018 ◽  
Vol 8 (10) ◽  
pp. 1768 ◽  
Author(s):  
Abdelhak Belhi ◽  
Abdelaziz Bouras ◽  
Sebti Foufou
Keyword(s):  
Neural Network ◽  
Cultural Heritage ◽  
Feature Learning ◽  
Image Data ◽  
Heritage Sites ◽  
Multimodal Classification ◽  
Label Prediction ◽  
Missing Labels ◽  
Traditional Approaches ◽  
Better Than

Cultural heritage represents a reliable medium for history and knowledge transfer. Cultural heritage assets are often exhibited in museums and heritage sites all over the world. However, many assets are poorly labeled, which decreases their historical value. If an asset’s history is lost, its historical value is also lost. The classification and annotation of overlooked or incomplete cultural assets increase their historical value and allows the discovery of various types of historical links. In this paper, we tackle the challenge of automatically classifying and annotating cultural heritage assets using their visual features as well as the metadata available at hand. Traditional approaches mainly rely only on image data and machine-learning-based techniques to predict missing labels. Often, visual data are not the only information available at hand. In this paper, we present a novel multimodal classification approach for cultural heritage assets that relies on a multitask neural network where a convolutional neural network (CNN) is designed for visual feature learning and a regular neural network is used for textual feature learning. These networks are merged and trained using a shared loss. The combined networks rely on both image and textual features to achieve better asset classification. Initial tests related to painting assets showed that our approach performs better than traditional CNNs that only rely on images as input.

scholarly journals STUDY ON MOSAIC AND UNIFORM COLOR METHOD OF SATELLITE IMAGE FUSION IN LARGE SREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1099-1102
Author(s):  
S. Liu ◽  
H. Li ◽  
X. Wang ◽  
L. Guo ◽  
R. Wang
Keyword(s):  
Information Entropy ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Texture Features ◽  
Color Difference ◽  
Large Area ◽  
Color Mapping ◽  
Multi Temporal ◽  
Mosaic Images

Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95 % and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

scholarly journals A New Approach to the Generation of Orthoimages of Cultural Heritage Objects—Integrating TLS and Image Data

2015 ◽  
Vol 7 (12) ◽  
pp. 16963-16985 ◽  
Author(s):  
Jakub Markiewicz ◽  
Piotr Podlasiak ◽  
Dorota Zawieska
Keyword(s):  
Cultural Heritage ◽  
Image Data ◽  
New Approach ◽  
Cultural Heritage Objects

scholarly journals Evaluation of Deep Learning-Based Segmentation Methods for Industrial Burner Flames

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1716
Author(s):  
Julius Großkopf ◽  
Jörg Matthes ◽  
Markus Vogelbacher ◽  
Patrick Waibel
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Processing System ◽  
Image Data ◽  
Waste Incineration ◽  
Industrial Environment ◽  
Camera Systems ◽  
Robust Segmentation ◽  
Combustion Processes ◽  
Instance Segmentation

The energetic usage of fuels from renewable sources or waste material is associated with controlled combustion processes with industrial burner equipment. For the observation of such processes, camera systems are increasingly being used. With additional completion by an appropriate image processing system, camera observation of controlled combustion can be used for closed-loop process control giving leverage for optimization and more efficient usage of fuels. A key element of a camera-based control system is the robust segmentation of each burners flame. However, flame instance segmentation in an industrial environment imposes specific problems for image processing, such as overlapping flames, blurry object borders, occlusion, and irregular image content. In this research, we investigate the capability of a deep learning approach for the instance segmentation of industrial burner flames based on example image data from a special waste incineration plant. We evaluate the segmentation quality and robustness in challenging situations with several convolutional neural networks and demonstrate that a deep learning-based approach is capable of producing satisfying results for instance segmentation in an industrial environment.

scholarly journals Kappa (κ): Analysis of Curvature in Biological Image Data using B-splines

2019 ◽  
Author(s):  
Hadrien Mary ◽  
Gary J. Brouhard
Keyword(s):  
Super Resolution ◽  
Image Data ◽  
Real Data ◽  
Point Spread Functions ◽  
B Splines ◽  
Accuracy And Precision ◽  
Synthetic Images ◽  
Microscopy Data ◽  
Biological Image

AbstractCurvature is a central morphological feature of tissues, cells, and sub-cellular structures. A challenge for computational biology is to measure the curvature of these structures from biological image data. We present an open-source Fiji plugin for measuring curvature using B-splines. The plugin is named Kappa after the Greek symbol for curvature, κ. Kappa is semi-automated: users create an initialization curve by a point-click method, and the initialization curve is fit to the underlying data using an iterative minimization algorithm. We demonstrate Kappa’s applicability on images of cytoskeletal filaments in vitro, the cell wall of budding yeast, and whole worms moving in an agar dish. In order to verify the accuracy and precision of Kappa, we created a bank of synthetic images of known curvature using sine waves and golden spirals, which we digitized with different signal-to-noise ratios (SNR), pixel sizes, and point-spread functions (PSF). For synthetic images with characteristics similar to real data, the measured curvatures of those images show a high correlation with the theoretical curvatures. Our fitting algorithms perform better with higher SNR, smaller pixel sizes, and especially PSFs equivalent to super-resolution microscopy data (surprise, surprise). Kappa is freely available under the MIT license for simple integration into Fiji-based workflows. The source code and documentation can be found on GitHub at https://github.com/brouhardlab/Kappa.

scholarly journals Intelligent Splicing Method of Virtual Reality Lingnan Cultural Heritage Panorama Based on Automatic Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yao Fu ◽  
Tingting Guo ◽  
Xingfang Zhao
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Cultural Heritage ◽  
Color Difference ◽  
Training Image ◽  
Automatic Machine ◽  
Panoramic Images ◽  
Multiple Regions ◽  
Application Fields ◽  
The Impact

With the increasing expansion of virtual reality application fields and the complexity of application content, the demand for real-time rendering of realistic graphics has increased sharply. This research mainly discusses the intelligent mosaic method of virtual reality Lingnan cultural heritage panorama based on automatic machine learning. In order to effectively make up for the impact of the insufficiency of the collection process on the quality of the final panoramic image of Lingnan cultural heritage, it is necessary to minimize the irregular rotation of the camera and collect images according to the overlapping area between adjacent images of appropriate size. In order to make Lingnan cultural heritage panoramic images have better visual effects, it is necessary to preprocess the images before image registration and fusion. Image preprocessing mainly includes image denoising and image projection transformation. In this study, cylindrical projection is used to construct the panorama of Lingnan cultural heritage. For each Lingnan cultural heritage training image, we first perform image segmentation to obtain multiple regions and extract the visual features of each region. We use automatic machine learning models to train the visual feature set and use the bagging method to generate different training subsets. In order to generate each component classifier, we determine the overlap area of the two images according to the matched SIFT feature points and determine the best stitching line during the implementation of stitching. In this paper, the number of pixels in the first row of the overlapping area is used to determine the candidate stitching line column, and the best stitching line position should be determined in consideration of the smallest color difference in the stitching area and the most similar texture on both sides. This article uses a Java Applet-based approach to realize virtual roaming of viewing panoramic images of Lingnan cultural heritage in IE browser. The highest accuracy of SIFT is 82.22%, and the lowest recognition time is 0.01 s. This research will promote the development of Lingnan cultural heritage.

scholarly journals TOWARDS STANDARDIZED EVALUATION OF IMAGE QUALITY FOR AIRBORNE CAMERA SYSTEMS

2018 ◽  
Vol XLII-1 ◽  
pp. 295-300 ◽  
Author(s):  
H. Meißner ◽  
M. Cramer ◽  
R. Reulke
Keyword(s):  
Image Quality ◽  
Image Data ◽  
Optical Quality ◽  
Image Resolution ◽  
Resolving Power ◽  
Mission Design ◽  
Camera Systems ◽  
Current State ◽  
Processing Techniques ◽  
Imaging Sensor

<p><strong>Abstract.</strong> Digital airborne camera systems and their high geometric resolution demand for new algorithms and procedures of image data analysis and interpretation. Parameters describing image quality are necessary for various fields of application (e.g. sensor and mission design, sensor comparison, algorithm development, in-orbit-behaviour of instruments). The effective sensor resolution is one important parameter which comprehensively estimates the optical quality of a given imaging sensor-lens combination. Although determination of resolving power is a well-studied field of research, there are still some scientific questions to be answered when it comes to a standardized (eventually absolute) determination. This is also research object of a committee of the “German Institute for Standardization” and the given contribution outlines the current state of investigation concerning effective resolving power for airborne camera systems. Therefore an approach using signal processing techniques to calculate the effective image resolution will be described. The open scientific issues will be introduced, explained and answered to some extend.</p>

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