Color Image Processing and Self-Localization for a Humanoid Soccer Robot

2013 ◽  
Vol 461 ◽  
pp. 877-885
Author(s):  
Wei Hong ◽  
Yan Hui Zhang ◽  
Yan Tao Tian ◽  
Chang Jiu Zhou
Keyword(s):  
Image Processing ◽  
Humanoid Robot ◽  
Color Image ◽  
Hopfield Network ◽  
Color Image Segmentation ◽  
Adult Size ◽  
Image Processing Algorithm ◽  
Soccer Robot ◽  
Operator Field ◽  
Rule Changes

The paper proposed a series of image processing algorithm to recognize the evidences in an image accurately for humanoid soccer robot, such as color image segmentation based on HSV model, edge detection based on four linear operator, field straight line extraction by Hough transform based on 8-neighbhour connected domain clusters and identification of line intersection shape based on Hopfield network. Based on evidences from image processing, Piecewise Monte Carlo localization is presented to solve kidnap problem so that localization of humanoid robot can be not only adapt to rule changes for competition, but also be more efficient and robust. The effectiveness of the piecewise MCL is verified by RoboCup Adult Size humanoid soccer robot, Erectus. The experimental results showed that the humanoid robot was able to solve the kidnap problem adaptively with two strategies: resetting or revising, in which resetting was more efficient than revising gradually.

scholarly journals Extraction of urban buildings using color image processing and morphological operations.

2021 ◽  
Vol 11 (1) ◽  
pp. 45-66
Author(s):  
Mete Durlu ◽  
Ozan Eski ◽  
Emre Sumer
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Color Image ◽  
Color Image Segmentation ◽  
Morphological Operations ◽  
Morphological Image Processing ◽  
Urban Buildings ◽  
Colour Image ◽  
High Resolution Satellite Images

In many geospatial applications, automated detection of buildings has become a key concern in recent years. Determination of building locations provides great benefits for numerous geospatial applications such as urban planning, disaster management, infrastructure planning, environmental monitoring. The study  aims to present a practical technique for extracting the buildings from high-resolution satellite images using color image segmentation and binary morphological image processing. The proposed method is implemented on satellite images of 4 different selected study areas of the city of Batikent, Ankara.  According to experiments conducted on the study areas, overall accuracy, sensitivity, and F1 values were computed to be on average, respectively. After applying morphological operations, the same metrics are calculated . The results show that the determination of urban buildings can be done more successfully with the suitable combination of morphological operations using rectangular structuring element. Keywords: Building Extraction; Colour Image Processing;Colour space conversion; Image Morphology; Remote Sensing        

Algorithm for Real-Time Image Processing in the Robot Soccer

2011 ◽  
Vol 143-144 ◽  
pp. 737-741 ◽  
Author(s):  
Hai Bo Liu ◽  
Wei Wei Li ◽  
Yu Jie Dong
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Vision System ◽  
Color Space ◽  
Color Image Segmentation ◽  
New Method ◽  
Robot System ◽  
Robot Soccer ◽  
Seed Fill ◽  
Time Image

Vision system is an important part of the whole robot soccer system.In order to win the game, the robot system must be more quick and more accuracy.A color image segmentation method using improved seed-fill algorithm in YUV color space is introduced in this paper. The new method dramatically reduces the work of calculation,and speeds up the image processing. The result of comparing it with the old method based on RGB color space was showed in the paper.The second step of the vision sub system is identification the color block that separated by the first step.A improved seed fill algorithm is used in the paper.The implementation on MiroSot Soccer Robot System shows that the new method is fast and accurate.

Design of a color image processing algorithm using online arithmetic modules

2004 ◽  
Author(s):  
Mohammed H. Sinky ◽  
Alexandre F. Tenca ◽  
Ajay C. Shantilal ◽  
Luca Lucchese
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Processing Algorithm ◽  
Color Image Processing ◽  
Image Processing Algorithm ◽  
Online Arithmetic

Improved Color Image Segmentation Algorithm Based on GrabCut

2013 ◽  
Vol 373-375 ◽  
pp. 464-467 ◽  
Author(s):  
Wang Rui ◽  
Jin Ye Peng ◽  
Li Ping Che ◽  
Yu Ting Hou
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Color Image ◽  
Implementation Process ◽  
Gaussian Mixture ◽  
Extraction Process ◽  
User Participation ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Realistic Image

In realistic image processing, it is a problem of image foreground extraction. For a large number of color image processing, an important requirement is the automation of the extraction process. In this paper, by automatically setting foreground seed, we improve the image existing segmentation algorithm; by automatically searching image segmentation region, we accomplish image segmentation with the GrabCut algorithm, which is based on Gaussian Mixture Model and boundary computing. The improved algorithm in this paper can achieve the automation of image segmentation, without user participation in the implementation process, at the same time, it improves the efficiency of image segmentation, and gets a good result of image segmentation in complex background.

scholarly journals K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 610 ◽  
Author(s):  
Senquan Yang ◽  
Pu Li ◽  
HaoXiang Wen ◽  
Yuan Xie ◽  
Zhaoshui He
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Image Segmentation ◽  
Color Classification ◽  
Clustering Problem ◽  
Illumination Changes ◽  
Segmentation Approach

Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

scholarly journals Segmentación de Imágenes Basada en Entropía de Pixel

2018 ◽  
Vol 3 (1) ◽  
pp. 523
Author(s):  
Rony Caballero ◽  
Aránzazu Berbey ◽  
Alberto Cogley
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Light Scattering ◽  
Color Image ◽  
Color Image Segmentation ◽  
Preliminary Results

In this research, a new methodology for color image segmentation is proposed. In this approach, a pixel entropy derivative based rule is used. The algorithm is tested not only with good quality images, but also with some of them with light scattering and absortion problems.  Preliminary results shows good performance of this algorithm.Keywords: Image processing, segmentation, entropy, feedback. 

scholarly journals The use of color distribution analysis for ripeness prediction of Golden Apollo melon

2017 ◽  
Author(s):  
The Journal of Applied Horticulture ◽  
Usman Ahmad
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Skin Color ◽  
Color Image ◽  
Age Groups ◽  
Processing Algorithm ◽  
Distribution Analysis ◽  
Human Visual Perception ◽  
Image Processing Algorithm ◽  
Color Distribution

Human visual perception on color of melon fruit for ripeness judgement is a complex phenomenon that depends on many factors, making the judgement is often inaccurate and inconsistent. The objective of this study is to develop an image processing algorithm that can be used for distinguishing ripe melons from unripe ones based on their skin color. The image processing algorithm could then be used as a pre-harvest tool to facilitate farmers with enough information for making decisions about whether or not the melon is ready to harvest. Four sample groups of Golden Apollo melon were harvested at four different harvesting age, with 55 fruits in each group. The color distribution as results of the image analysis can be separated at the first two groups from other groups with minimal overlap, but they cannot be separated from the other two groups. The color image analysis of the melons in combination with discriminant analysis could be used to distinguish between harvesting age groups with an average accuracy of 86%.

scholarly journals Research on color image segmentation

2018 ◽  
Vol 25 (03) ◽  
pp. 138-143
Author(s):  
Wang He Xi Ge Tu ◽  
Bolormaa D
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Color Image ◽  
Color Image Segmentation ◽  
Primary Analysis ◽  
Programming Tools ◽  
Segmentation Methods ◽  
Segmentation Image

The basic foundation for the development of the image processing is image segments. Primary analysis, such as analysis of images and visualization of images, begins with segmentation. Image segmentation is one of the important parts of digital image processing. Depending on the accuracy and accuracy of the segmentation, the results of the image analysis, including the size of the object, the size of the object, and so on. In the first section of this study, briefly describe the types of image segments. Also use Mathlab language's powerful modern programming tools to explore the image segmentation methods and compare the results. As a result of the experiment, it is more accurate to accurately measure the trajectory of the image segmentation of the image as a result of the Otsu-based method of B space. This will apply to further research. Өнгөний мэдээлэлд суурилсан дүрс сегментчлэх аргын судалгаа Хураангуй: Дүрс боловсруулах судалгааны ажлын үндсэн суурь нь дүрс сегментчлэл юм. Дүрсэнд анализ хийх, дүрсийг ойлгох зэрэг анхан шатны боловсруулалт нь дүрс сегментчлэхээс эхэлдэг. Дүрс сегментчлэл нь дижитал дүрс боловсруулалтын чухал хэсгүүдийн нэг юм. Сегментчлэлийг хэр зэрэг үнэн зөв, нарийвчлал сайтай хийснээс шалтгаалан, дараагийн дүрс таних, обьектын хэмжээ зэрэг дүрс шинжлэлийн алхамын үр дүн ихээхэн хамаардаг. Энэхүү судалгааны ажлын эхний хэсэгт дүрс сегментчлэх арга төрлүүдийн талаар товч танилцуулна. Мөн орчин үеийн програмчлалын хүчтэй хэрэгсэл болох Mathlab хэлний функцуудыг ашиглан дүрс сегментчилж гарсан үр дүнгийн харьцуулалтыг танилцууллаа. Туршилтын үр дүнд RGB өнгөний орон зайн B бүрэлдэхүүнд суурилсан Otsu-ийн аргийг ашиглан дүрсийг сементчилэх нь уламжлалт дүрс сегментчилэх аргаас нэн сайн үр дүнтай илүү нарийвчлалтай байна. Үүнийг цаашдын судалгааны ажилдаа хэрэглэх болно. Түлхүүр үг: RGB дүрс, босго (Threshold) утга, гистограм, Otsu-ийн арга, дүрс боловсруулалт

Color Image Processing Under Uncertainty

2021 ◽  
Vol 12 (2) ◽  
pp. 46-67
Author(s):  
Fateh Boutekkouk ◽  
Narimane Sahel
Keyword(s):  
Image Processing ◽  
Digital Images ◽  
Color Image ◽  
Color Image Segmentation ◽  
Entropy Measure ◽  
Mathematical Tool ◽  
Intuitionistic Fuzzy ◽  
Lighting Conditions ◽  
Complex Relationships ◽  
Fuzzy Hypergraphs

Most digital images have uncertainties associated with the intensity levels of pixels and/or edges. These uncertainties can be traced back to the acquisition chain, to uneven lighting conditions used during imaging or to the noisy environment. On the other hand, intuitionistic fuzzy hypergraphs are considered a useful mathematical tool for digital image processing since they can represent digital images as complex relationships between pixels and model uncertain or imprecise knowledge explicitly. This paper presents the approach for noisy color image segmentation and edge detection based on intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to the HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edges detection. The first experimentations showed that the proposed approach gave good results especially in the case of dynamic threshold.

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