Image Analysis-Based Estimation of Metallic and Pearl Add-Ons Concentrations in Automotive Paints

2014 ◽  
Vol 19 (2-3) ◽  
pp. 119-126
Author(s):  
Krzysztof Ślot ◽  
Krzysztof Strzecha ◽  
Grzegorz Nowak
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Experimental Verification ◽  
Focal Plane ◽  
Dopant Concentration ◽  
Microscopic Image ◽  
Automotive Paint ◽  
Pigment Color ◽  
Preliminary Research ◽  
Concentration Assessment

Abstract The paper reports results of preliminary research on automotive paint dopant concentration assessment based on microscopic image segmentation. The considered task is illconditioned due to the richness and diversity in contents of images to be analyzed. The proposed procedure involves two main phases: image segmentation, where focal-plane paint addons are extracted from the background, and object analysis and classification. The results of experimental verification of the proposed method on a set of eighteen paint pigmented images (black and yellow) show that the estimation can be done with approximately 5% accuracy for paints doped with only single addon type. For add-on mixtures, the results were strongly dependent on pigment color and mutual add-on proportions.

scholarly journals Selected Applications of Scale Spaces in Microscopic Image Analysis

2015 ◽  
Vol 15 (7) ◽  
pp. 5-12
Author(s):  
Dimiter Prodanov ◽  
Tomasz Konopczynski ◽  
Maciej Trojnar
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Edge Detection ◽  
Scale Space ◽  
Microscopic Image ◽  
Space Theory ◽  
Systematic Treatment ◽  
Microscopic Image Analysis ◽  
Segmentation Methods ◽  
Scale Spaces

Abstract Image segmentation methods can be classified broadly into two classes: intensity-based and geometry-based. Edge detection is the base of many geometry-based segmentation approaches. Scale space theory represents a systematic treatment of the issues of spatially uncorrelated noise with its main application being the detection of edges, using multiple resolution scales, which can be used for subsequent segmentation, classification or encoding. The present paper will give an overview of some recent applications of scale spaces into problems of microscopic image analysis. Particular overviews will be given to Gaussian and alpha-scale spaces. Some applications in the analysis of biomedical images will be presented. The implementation of filters will be demonstrated.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

Color Reproduction Accuracy Promotion of 3D-Printed Surfaces Based on Microscopic Image Analysis

2019 ◽  
Vol 34 (01) ◽  
pp. 2054004 ◽  
Author(s):  
Xiaochun Wang ◽  
Chen Chen ◽  
Jiangping Yuan ◽  
Guangxue Chen
Keyword(s):  
Surface Roughness ◽  
Image Analysis ◽  
Three Dimensional ◽  
Prediction Method ◽  
Chromatic Aberration ◽  
Optimization Methods ◽  
Experimental Models ◽  
Microscopic Image ◽  
Color Reproduction ◽  
Microscopic Image Analysis

Full-color three-dimensional (3D) printing technology is a powerful process to manufacture intelligent customized colorful objects with improved surface qualities; however, poor surface color optimization methods are the main impeding factors for its commercialization. As such, the paper explored the correlation between microstructure and color reproduction, then an assessment and prediction method of color optimization based on microscopic image analysis was proposed. The experimental models were divided into 24-color plates and 4-color cubes printed by ProJet 860 3D printer, then impregnated according to preset parameters, at last measured by a spectrophotometer and observed using both a digital microscope and a scanning electron microscope. The results revealed that the samples manifested higher saturation and smaller chromatic aberration ([Formula: see text]) after postprocessing. Moreover, the brightness of the same color surface increased with the increasing soaked surface roughness. Further, reduction in surface roughness, impregnation into surface pores, and enhancement of coating transparency effectively improved the accuracy of color reproduction, which could be verified by the measured values. Finally, the chromatic aberration caused by positioning errors on different faces of the samples was optimized, and the value of [Formula: see text] for a black cube was reduced from 8.12 to 0.82, which is undetectable to human eyes.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

scholarly journals A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 241-247 ◽  
Author(s):  
T. Kavzoglu ◽  
M. Yildiz Erdemir ◽  
H. Tonbul
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Classification Accuracy ◽  
Scale Parameter ◽  
Rate Of Change ◽  
Multi Scale ◽  
Object Based Image Analysis ◽  
Optimal Scale ◽  
Segmentation Process ◽  
Object Based

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

A Critical Overview of Image Segmentation Techniques Based on Transition Region

2018 ◽  
pp. 1308-1318
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Transition Region ◽  
Google Scholar ◽  
Research Directions ◽  
Distinctive Type ◽  
Critical Overview

Image segmentation is the key step from image processing to image analysis, and is an important technique of image engineering. Image segmentation based on transition region is a special or distinctive type of techniques that are different from traditional boundary-based or region-based techniques. Since the first technique using transition region proposed, there are many subsequent related researches and applications, and a series of papers in the literature citing are published worldwide. Using Google Scholar, a number of papers citing the original papers are searched, a study on the statistics of these papers is conducted. These papers are sorted first according to the publishing year, and then grouped according to their purposes and contents (with techniques used). Some questionable issues in these papers are pointed out and critically discussed, and several further research directions are indicated and analyzed.

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