scholarly journals Determination and Measurement of Color Dissimilarity

2020 ◽  
Vol 5 (1) ◽  
pp. 67
Author(s):  
I Gede Made Karma
Keyword(s):  
Color Space ◽  
Three Dimensional ◽  
Human Eye ◽  
A Value ◽  
Rgb Color Space ◽  
Range Of Values ◽  
Rgb Values

There are millions of different colors that exist in this nature. There are colors that can easily be distinguished from other colors, but many are also difficult to distinguish. The ability to distinguish colors is important in the recognition of an object, especially objects in an image. Color can be represented in a three-dimensional RGB color space and each color will have an RGB value with a range of values ??from 0-255. Conceptually, colors with different RGB values ??are different colors, but the human eye may not necessarily be able to distinguish them. This study aims to determine and measure the color dissimilarity that can be distinguished by humans. With an experiment comparing a color with a color made with a variation of RGB values, this study resulted that two colors that have a Delta E (?E) value of 8 can be distinguished by the eye. This result is obtained if both colors have a value of ?R, ?G or ?B of at least 8, or a value of ?RG, ?RB or ?GB of at least 6, or a value of ?RGB of at least 5.

scholarly journals The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

Agriculture ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Ewa Ropelewska
Keyword(s):  
Sweet Cherry ◽  
Color Space ◽  
Geometric Parameters ◽  
Geometric Features ◽  
Color Spaces ◽  
Discriminative Models ◽  
Sweet Cherries ◽  
Rgb Color Space ◽  
Cultivar Discrimination ◽  
Color Channels

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.

scholarly journals A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

2021 ◽  
Vol 13 (6) ◽  
pp. 1211
Author(s):  
Pan Fan ◽  
Guodong Lang ◽  
Bin Yan ◽  
Xiaoyan Lei ◽  
Pengju Guo ◽  
...  
Keyword(s):  
Vision System ◽  
False Positive Rate ◽  
Clustering Algorithms ◽  
False Negative ◽  
Color Space ◽  
False Negative Rate ◽  
Feature Selection Method ◽  
Image Features ◽  
Accurate Identification ◽  
Rgb Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

scholarly journals A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries

2021 ◽  
Vol 13 (5) ◽  
pp. 939
Author(s):  
Yongan Xue ◽  
Jinling Zhao ◽  
Mingmei Zhang
Keyword(s):  
Color Space ◽  
Cultivated Land ◽  
Color Spaces ◽  
Region Merging ◽  
Watershed Segmentation ◽  
Coverage Area ◽  
Time Efficiency ◽  
Image Contrast Enhancement ◽  
Segmentation Accuracy ◽  
Rgb Color Space

To accurately extract cultivated land boundaries based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm was proposed herein based on a combination of pre- and post-improvement procedures. Image contrast enhancement was used as the pre-improvement, while the color distance of the Commission Internationale de l´Eclairage (CIE) color space, including the Lab and Luv, was used as the regional similarity measure for region merging as the post-improvement. Furthermore, the area relative error criterion (δA), the pixel quantity error criterion (δP), and the consistency criterion (Khat) were used for evaluating the image segmentation accuracy. The region merging in Red–Green–Blue (RGB) color space was selected to compare the proposed algorithm by extracting cultivated land boundaries. The validation experiments were performed using a subset of Chinese Gaofen-2 (GF-2) remote sensing image with a coverage area of 0.12 km2. The results showed the following: (1) The contrast-enhanced image exhibited an obvious gain in terms of improving the image segmentation effect and time efficiency using the improved algorithm. The time efficiency increased by 10.31%, 60.00%, and 40.28%, respectively, in the RGB, Lab, and Luv color spaces. (2) The optimal segmentation and merging scale parameters in the RGB, Lab, and Luv color spaces were C for minimum areas of 2000, 1900, and 2000, and D for a color difference of 1000, 40, and 40. (3) The algorithm improved the time efficiency of cultivated land boundary extraction in the Lab and Luv color spaces by 35.16% and 29.58%, respectively, compared to the RGB color space. The extraction accuracy was compared to the RGB color space using the δA, δP, and Khat, that were improved by 76.92%, 62.01%, and 16.83%, respectively, in the Lab color space, while they were 55.79%, 49.67%, and 13.42% in the Luv color space. (4) Through the visual comparison, time efficiency, and segmentation accuracy, the comprehensive extraction effect using the proposed algorithm was obviously better than that of RGB color-based space algorithm. The established accuracy evaluation indicators were also proven to be consistent with the visual evaluation. (5) The proposed method has a satisfying transferability by a wider test area with a coverage area of 1 km2. In addition, the proposed method, based on the image contrast enhancement, was to perform the region merging in the CIE color space according to the simulated immersion watershed segmentation results. It is a useful attempt for the watershed segmentation algorithm to extract cultivated land boundaries, which provides a reference for enhancing the watershed algorithm.

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
Author(s):  
HUA YANG ◽  
MASAAKI KASHIMURA ◽  
NORIKADU ONDA ◽  
SHINJI OZAWA
Keyword(s):  
Color Image ◽  
Color Space ◽  
Input Image ◽  
Color Image Processing ◽  
Front Cover ◽  
Stroke Width ◽  
Other Information ◽  
Region Extraction ◽  
Hsi Color Space ◽  
Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

Digestible cell wall and undigested cellular contents of two grasses of low net energy value for growing cattle

1982 ◽  
Vol 99 (1) ◽  
pp. 207-208 ◽  
Author(s):  
K. W. Moir
Keyword(s):  
Cell Wall ◽  
Organic Matter ◽  
Dry Matter ◽  
Total Cell ◽  
Net Energy ◽  
Previous Evidence ◽  
Energy Value ◽  
A Value ◽  
Net Energy Value ◽  
Range Of Values

SUMMARYIn two grasses of low net energy value for growth and fattening of cattle, digestible cell-wall values were close to 40% of forage organic matter (OM), similar to the expected value for grass. A value of 14% of forage OM for apparently undigested cellular contents in both grasses was just outside the range of values for grass. The data supported previous evidence that total cell wall is the only value that can be used at present to define grass in terms of its digestible dry matter.

scholarly journals Framing the Values of Vernacular Architecture for a Value-Based Conservation: A Conceptual Framework

2021 ◽  
Vol 13 (9) ◽  
pp. 4974
Author(s):  
Obafemi A. P. Olukoya
Keyword(s):  
Vernacular Architecture ◽  
Conservation Strategy ◽  
Participatory Process ◽  
Value Assessment ◽  
A Value ◽  
Wide Range ◽  
Flexible Framework ◽  
Value Model ◽  
Contextual Values ◽  
Range Of Values

While a growing number of researchers have provided series of tough critiques of the typology-led heritage value assessment over the recent years, the impacts have been constrained by the continued obsession with expanding the list of the predetermined value typologies rather than escaping its limitations. While these sustained debates have provided important insights, this article argues that operationalizing these predetermined ‘one-size-fits-all’ value typologies is symptomatic of a number of shortcomings, especially in the context of capturing the pluralities of values in contextualized heritage such as vernacular architecture. It also often undermines inclusivity and participation in the valuing processes. However, rather than simply rejecting the values-based paradigm, this article proposes a conceptual value assessment framework that is informed by the theorization of vernacular architecture as a contextualized heritage. The proposed Vernacular Value Model (VVM) puts forward the ‘when(s)’ and ‘how(s)’ of amalgamating both technical and normative processes to capture the range of contextual values present in built vernacular heritage. To this end, this article posits that by drawing on such a proposed flexible framework, the conservation strategy for built vernacular heritage can be propagated as an inclusive and participatory process which captures the wide range of values for a more sustainable practices for conservation.

390 Characterisation of a Platelet Rich Fibrin Membrane and Formation of an Autologous Fibrin Mesh

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
Joshua Burke ◽  
Jack Helliwell ◽  
Mikolaj Kowal ◽  
David Jayne
Keyword(s):  
Three Dimensional ◽  
Clinical Results ◽  
Vascular Endothelial ◽  
Blood Draw ◽  
Platelet Rich Fibrin ◽  
Fibrin Scaffold ◽  
Surgical Sutures ◽  
Endothelial Growth Factor ◽  
Range Of Values ◽  
Fibrin Membrane

Abstract Aim Platelet-rich fibrin (PRF) is a three-dimensional fibrin scaffold with associated platelets and leukocytes which releases high quantities of growth factors over a sustained period of time. PRF has shown promising clinical results in promoting wound healing and tissue regeneration. The aims of this feasibility study were to establish optimal spinning methods for production of PRF, to quantify the production of vascular endothelial growth factor (VEGF) by PRF and to explore new vehicles of clinical PRF delivery. Method Assessment of optimal production involved comparisons between Protocol 1 (EDTA bottle) and Protocol 2 (no additive) at three different centrifugation forces: 400g, 1000g and 1700g. VEGF production was analysed using ELISA with varied incubation periods and PRF plug segments. Novel methods for PRF delivery were explored using surgical sutures and a Zimmer® Skin Graft Mesher. Results Protocol 2 demonstrated shorter average time to blood draw (9.8s compared to 13.6s) and to centrifuge (25.5s compared to 33.1s) with a decreased range of values. All PRF segments exhibited a positive correlation between incubation time and amount of VEGF produced with the bottom segments producing on average more VEGF. A segment of the fibrin plug was successfully secured on a suture and meshed in a 1:1.5 ratio. Conclusions PRF production can be optimised using blood bottles with no additive and high centrifugation forces. VEGF production by PRF peaks at 120 hours with the bottom PRF segment exhibiting the highest rate of production. The first description of a PRF mesh enables new clinical applications.

Bending of an Infinite Beam on an Elastic Foundation

1937 ◽  
Vol 4 (1) ◽  
pp. A1-A7 ◽  
Author(s):  
M. A. Biot
Keyword(s):  
Elastic Foundation ◽  
Poisson Ratio ◽  
Elementary Theory ◽  
Three Dimensional ◽  
Bending Moment ◽  
Two Dimensional ◽  
Concentrated Load ◽  
Elastic Continuum ◽  
Infinite Beam ◽  
A Value

Abstract The elementary theory of the bending of a beam on an elastic foundation is based on the assumption that the beam is resting on a continuously distributed set of springs the stiffness of which is defined by a “modulus of the foundation” k. Very seldom, however, does it happen that the foundation is actually constituted this way. Generally, the foundation is an elastic continuum characterized by two elastic constants, a modulus of elasticity E, and a Poisson ratio ν. The problem of the bending of a beam resting on such a foundation has been approached already by various authors. The author attempts to give in this paper a more exact solution of one aspect of this problem, i.e., the case of an infinite beam under a concentrated load. A notable difference exists between the results obtained from the assumptions of a two-dimensional foundation and of a three-dimensional foundation. Bending-moment and deflection curves for the two-dimensional case are shown in Figs. 4 and 5. A value of the modulus k is given for both cases by which the elementary theory can be used and leads to results which are fairly acceptable. These values depend on the stiffness of the beam and on the elasticity of the foundation.

Three-Dimensional Color Gamut Visualization of Digital Output Device Based on ICC Profile

2012 ◽  
Vol 262 ◽  
pp. 36-39 ◽  
Author(s):  
Yun Hui Luo ◽  
Mao Hai Lin
Keyword(s):  
Dimensional Space ◽  
Color Space ◽  
Three Dimensional ◽  
Color Gamut ◽  
Digital Output ◽  
Gamut Mapping ◽  
Output Device ◽  
High Quality Image ◽  
Image Appearance ◽  
Cie Lab

As color gamut of digital output device greatly affects image appearance, accurate and effective gamut description for output device is intensively required for developing high-quality image reproduction technique based on gamut mapping. In this paper, we present a novel method to determine color gamut of output device by using a specific 3D reconstruction technology and device ICC profile. First, we populate the device color space by uniform sampling in the RGB 3-Dimensional space, and convert these sampling points to CMYK color space. Then, we work out the CIE LAB value of these points according to the ICC profile of output device. At last, in CIE LAB color space the boundary of these points is determined by using a gamut boundary descriptor based on Ball-Pivoting Algorithm (BPA) proposed by Bernardini. Compared with the results generated by ICC3D, our proposed method can compute device gamut more efficiently and at the same time give a more accurate gamut description of the output device. It will be help to develop effective gamut mapping algorithms for color reproduction.

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