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.

Digital Color Image Processing Using Intuitionistic Fuzzy Hypergraphs

2021 ◽  
Vol 11 (3) ◽  
pp. 21-40
Author(s):  
Fateh Boutekkouk
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Images ◽  
Color Image ◽  
Fuzzy Theory ◽  
Entropy Measure ◽  
Mathematical Tool ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Theory ◽  
Fuzzy Hypergraphs

Hypergraphs are considered a useful mathematical tool for digital image processing and analysis since they can represent digital images as complex relationships between pixels or block of pixels. The notion of hypergraphs has been extended in fuzzy theory leading to the concept of fuzzy hypergraphs, then in intuitionistic fuzzy theory conducting to the concept of intuitionistic fuzzy hypergraphs or IFHG. The latter is very suitable to model digital images with uncertain or imprecise knowledge. This paper deals with color image denoising, segmentation, and edge detection in a color image initially represented in RGB space using intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to 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 edge detection.

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        

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.

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.

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.

Computer Processing of an Image

2021 ◽  
pp. 1-22
Author(s):  
Chandra Prabha R. ◽  
Shilpa Hiremath
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Images ◽  
Color Image ◽  
Image Representation ◽  
Computer Processing ◽  
Binary Image ◽  
Grayscale Image ◽  
Under Sampling

In this chapter, the authors have briefed about images, digital images, how the digital images can be processed. Image types like binary image, grayscale image, color image, and indexed image and various image formats are explained. It highlights the various fields where digital image processing can be used. This chapter introduces a variety of concepts related to digital image formation in a human eye. The mechanism of the human visual system is discussed. The authors illustrate the steps of image processing. Explanation on different elements of digital image processing systems like image acquisition, and others are also provided. The components required for capturing and processing the image are discussed. Concepts of image sampling, quantization, image representation are discussed. It portrays the operations of the image during sampling and quantization and the two operations of sampling which is oversampling and under-sampling. Readers can appreciate the key difference between oversampling and under-sampling applied to digital images.

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