A Robust Color Watershed Transformation and Image Segmentation Defined on RGB Spherical Coordinates

2013 ◽  
pp. 112-128
Author(s):  
Ramón Moreno ◽  
Manuel Graña ◽  
Kurosh Madani
Keyword(s):  
Image Segmentation ◽  
Color Space ◽  
Color Constancy ◽  
Spherical Coordinates ◽  
Spherical Coordinate ◽  
Nao Robot ◽  
Rgb Color Space ◽  
And Performance ◽  
Definition Of ◽  
Merging Strategy

The representation of the RGB color space points in spherical coordinates allows to retain the chromatic components of image pixel colors, pulling apart easily the intensity component. This representation allows the definition of a chromatic distance and a hybrid gradient with good properties of perceptual color constancy. In this chapter, the authors present a watershed based image segmentation method using this hybrid gradient. Oversegmentation is solved by applying a region merging strategy based on the chromatic distance defined on the spherical coordinate representation. The chapter shows the robustness and performance of the approach on well known test images and the Berkeley benchmarking image database and on images taken with a NAO robot.

scholarly journals Pixel-Based Image Processing for CIE Standard Sky Classification through ANN

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
D. Granados-López ◽  
A. García-Rodríguez ◽  
S. García-Rodríguez ◽  
A. Suárez-García ◽  
M. Díez-Mediavilla ◽  
...  
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Classification Problem ◽  
Color Spaces ◽  
Processing Methods ◽  
Image Information ◽  
Rgb Color Space ◽  
Definition Of ◽  
Sky Conditions ◽  
Sky Type

Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification problem.

scholarly journals Experiments on the White Patch Retinex in RGB and CIELAB color spaces

2012 ◽  
Vol 22 ◽  
pp. 21-26 ◽  
Author(s):  
Jonathan Cepeda-Negrete ◽  
Raul E. Sanchez-Yanez
Keyword(s):  
Color Space ◽  
Color Constancy ◽  
Important Process ◽  
White Patch ◽  
Color Spaces ◽  
Quality Metric ◽  
Retinex Algorithm ◽  
Reference Quality ◽  
Rgb Color Space ◽  
Cielab Color

Color constancy is an important process in a number of vision tasks. Most devices for capturing images operate on the RGB color space and, usually, the processing of the images is in this space, although some processes have shown a better performance when a perceptual color space is used instead. In this paper, experiments on the White Patch Retinex, a color constancy algorithm commonly used, are performed in two color spaces, RGB and CIELAB, for comparison purposes. Experimental results using an imagery set are analyzed using a no-reference quality metric and outcomes are discussed. It has been found that the White Patch Retinex algorithm shows a better performance in RGB than in CIELAB, but when color adjustments are implemented in sequence, firstly in CIELAB and then in RGB, much better results are obtained.

Картофелекопатель с ротационной сепарирующей поверхностью

2018 ◽  
Author(s):  
V.M. Alakin ◽  
G.S. Nikitin
Keyword(s):  
Performance Data ◽  
Pilot Studies ◽  
Research Results ◽  
Operating Modes ◽  
Analysis Of Results ◽  
Design Characteristics ◽  
And Performance ◽  
Definition Of

Приведены результаты исследований экспериментального картофелекопателя с ротационной сепарирующей поверхностью. Особое внимание уделяется обоснованию конструктивных параметров и определению рабочих характеристик нового сепарирующего устройства. На основе анализа результатов экспериментальных исследований определены наиболее оптимальные режимы работы экспериментального картофелекопателя.Research results of an experimental potato digger with rotational separating web are published in this article. Special attention is paid to definition of design characteristics and performance data of the new separating device. Admissible operating modes are defined on the basis of the analysis of results of pilot studies of the experimental potato digger.

Positive Workplaces

2009 ◽  
pp. 578-588
Author(s):  
Fred Luthans ◽  
Carolyn M. Youssef
Keyword(s):  
Organizational Behavior ◽  
Psychological Capital ◽  
Future Research ◽  
Positive Organizational Behavior ◽  
Performance Impact ◽  
Research Directions ◽  
Future Research Directions ◽  
And Performance ◽  
Definition Of ◽  
Psychological Capacities

Over the years, both management practitioners and academics have generally assumed that positive workplaces lead to desired outcomes. Unlike psychology, considerable attention has also been devoted to the study of positive topics such as job satisfaction and organizational commitment. However, to place a scientifically based focus on the role that positivity may play in the development and performance of human resources, and largely stimulated by the positive psychology initiative, positive organizational behavior (POB) and psychological capital (PsyCap) have recently been introduced into the management literature. This chapter first provides an overview of both the historical and contemporary positive approaches to the workplace. Then, more specific attention is given to the meaning and domain of POB and PsyCap. Our definition of POB includes positive psychological capacities or resources that can be validly measured, developed, and have performance impact. The constructs that have been determined so far to best meet these criteria are efficacy, hope, optimism, and resiliency. When combined, they have been demonstrated to form the core construct of what we term psychological capital (PsyCap). A measure of PsyCap is being validated and this chapter references the increasing number of studies indicating that PsyCap can be developed and have performance impact. The chapter concludes with important future research directions that can help better understand and build positive workplaces to meet current and looming challenges.

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.

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