scholarly journals Digital Image Segmentation Using Delaunay Triangulation Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 268-283
Author(s):  
GU Onoja ◽  
T Aboiyar
Keyword(s):  
Image Segmentation ◽  
Image Enhancement ◽  
Delaunay Triangulation ◽  
Digital Image ◽  
Research Work ◽  
Research Area ◽  
Image Features ◽  
External Boundary ◽  
Future Research ◽  
Segmentation Image

Image segmentation is the process of partitioning a digital image into multiple segments according to some homogeneity criterion. Different approaches are suited to different types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to the fact that there may be much “correct” segmentation for a single image. Although the field encounters several challenges, this research work seeks to present a segmentation algorithm for gray intensity images. Image features extraction is first performed to obtain the approximate, if not exact, representation of a given image. Image enhancement was also carried out by exploiting the principle of histogram equalization which is one of the well-known image enhancement techniques. The proposed approach is based on the Delaunay (DT), one of the techniques in computational geometry, which generates clusters of intensity values using information from the vertices of the external boundary of triangulation. This makes it possible to produce segmented image regions. The algorithm was tested on sampled images before and after enhancement and the production compared. Our algorithm successfully extracts cogent features from a given image and made use of the Delaunay Triangulation to carry out segmentation. The method is robust, precise and independent of translation, rotation and scaling and that makes the result quite good for image segmentation. Image segmentation based on Delaunay triangulation technique seem to be a promising future research area with respect to its applicability to underwater mine and submarine detection, better target recognition, medical imaging as well as to automated computer vision.

scholarly journals Colour Image Segmentation using Background Subtraction with Global and Local Threshold

2021 ◽  
Vol 9 (VI) ◽  
pp. 1652-1657
Author(s):  
Amanpreet Kaur
Keyword(s):  
Image Segmentation ◽  
Performance Evaluation ◽  
Digital Image ◽  
Background Subtraction ◽  
Digital Images ◽  
Subjective Evaluation ◽  
Research Work ◽  
Colour Image ◽  
Segmentation Image ◽  
Global And Local

Image segmentation is one of the fundamental and essential steps in all the major applications of digital image processing. In this process the digital image is divided into various regions which are also known as segments. These segmented parts of the digital image could be used for further processing like detection of types of objects present in the segmented region, various tumors present in the digital images or the scene understanding process. Usually segmentation is classified as local segmentation and the global segmentation. Image segmentation is also classified on the basis of digital image properties also. In this case it is of two types. First one is non continuity detection and second one is the continuous detection. Various image segmentation techniques are proposed by researchers which have various limitations. Some techniques do not split the region uniformly and other techniques take enough time and memory for the processing of digital image. In this research work both the local and global thresholding concept is used to get the segmented regions of the image. The proposed technique will be able to extract the segmented objects from the digital image. To check the authenticity and efficiency of the proposed technique, it will be compared with other well known techniques of image segmentation using background subtraction of colored digital images. Time of computation, sensitivity and accuracy are used as objective parameters for the performance evaluation of the techniques. For the subjective evaluation visual quality of the digital image is used for performance evaluation.

MATLAB-Based Digital Image Processing Technology Research

2014 ◽  
Vol 687-691 ◽  
pp. 3769-3772 ◽  
Author(s):  
Chung Yang Shi
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Technology ◽  
Future Research ◽  
Technology Research ◽  
Image Processing Technology ◽  
The Future

This paper is an application of digital image processing technology based on MATLAB; this paper introduces the MATLAB image enhancement and image segmentation technology, in order to lay a foundation for the future research and application.

scholarly journals A Bibliometric Analysis of the Global Trend of Using Alginate, Gelatine, and Hydroxyapatite for Bone Tissue Regeneration Applications

Polymers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 647
Author(s):  
Mohamed Saiful Firdaus Hussin ◽  
Aludin Mohd Serah ◽  
Khairul Azri Azlan ◽  
Hasan Zuhudi Abdullah ◽  
Maizlinda Izwana Idris ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Bone Tissue Engineering ◽  
Bone Tissue ◽  
Bibliometric Analysis ◽  
Research Work ◽  
The United States ◽  
Research Area ◽  
Bone Tissue Regeneration ◽  
Future Research ◽  
Starting Point

Collecting information from previous investigations and expressing it in a scientometrics study can be a priceless guide to getting a complete overview of a specific research area. The aim of this study is to explore the interrelated connection between alginate, gelatine, and hydroxyapatite within the scope of bone tissue and scaffold. A review of traditional literature with data mining procedures using bibliometric analyses was considered to identify the evolution of the selected research area between 2009 and 2019. Bibliometric methods and knowledge visualization technologies were implemented to investigate diverse publications based on the following indicators: year of publication, document type, language, country, institution, author, journal, keyword, and number of citations. An analysis using a bibliometric study found that 7446 papers were located with the keywords “bone tissue” and “scaffold”, and 1767 (alginate), 185 (gelatine), 5658 (hydroxyapatite) papers with those specific sub keywords. The number of publications that relate to “tissue engineering” and bone more than doubled between 2009 (1352) and 2019 (2839). China, the United States and India are the most productive countries, while Sichuan University and the Chinese Academy of Science from China are the most important institutions related to bone tissue scaffold. Materials Science and Engineering C is the most productive journal, followed by the Journal of Biomedical Materials Research Part A. This paper is a starting point, providing the first bibliometric analysis study of bone tissue and scaffold considering alginate, gelatine and hydroxyapatite. A bibliometric analysis would greatly assist in giving a scientific insight to support desired future research work, not only associated with bone tissue engineering applications. It is expected that the analysis of alginate, gelatine and hydroxyapatite in terms of 3D bioprinting, clinical outcomes, scaffold architecture, and the regenerative medicine approach will enhance the research into bone tissue engineering in the near future. Continued studies into these research fields are highly recommended.

scholarly journals Infrared and Visible Image Fusion Techniques Based on Deep Learning: A Review

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2162
Author(s):  
Changqi Sun ◽  
Cong Zhang ◽  
Naixue Xiong
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Research Work ◽  
Image Features ◽  
Future Research ◽  
Learning Methods ◽  
Visible Image ◽  
Advantages And Disadvantages ◽  
Fusion Methods ◽  
Future Work

Infrared and visible image fusion technologies make full use of different image features obtained by different sensors, retain complementary information of the source images during the fusion process, and use redundant information to improve the credibility of the fusion image. In recent years, many researchers have used deep learning methods (DL) to explore the field of image fusion and found that applying DL has improved the time-consuming efficiency of the model and the fusion effect. However, DL includes many branches, and there is currently no detailed investigation of deep learning methods in image fusion. In this work, this survey reports on the development of image fusion algorithms based on deep learning in recent years. Specifically, this paper first conducts a detailed investigation on the fusion method of infrared and visible images based on deep learning, compares the existing fusion algorithms qualitatively and quantitatively with the existing fusion quality indicators, and discusses various fusions. The main contribution, advantages, and disadvantages of the algorithm. Finally, the research status of infrared and visible image fusion is summarized, and future work has prospected. This research can help us realize many image fusion methods in recent years and lay the foundation for future research work.

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Kamlesh Sharma ◽  
Nidhi Garg
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

scholarly journals Algorithmic Approaches For Image Edge Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1942-1948
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Algorithmic Approaches

Edge detection is most important technique in digital image processing. It play an important role in image segmentation and many other applications. Edge detection providesfoundation to many medical and military applications.It difficult to generate a generic code for edge detection so many kinds ofalgorithms are available. In this article 4 different approaches Global image enhancement with addition (GIEA), Global image enhancement with Multiplication (GIEM),Without Global image enhancement with Addition (WOGIEA),and without Global image enhancement with Multiplication (WOGIEM)for edge detection is proposed. These algorithms are validatedon 9 different images. The results showthat GIEA give us more accurate results as compare to other techniques.

Study of Various Image Segmentation Methodologies

2017 ◽  
pp. 1-27
Author(s):  
Abahan Sarkar ◽  
Ram Kumar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
New Technologies ◽  
Region Of Interest ◽  
Literature Survey ◽  
Segmentation Algorithms ◽  
Segmentation Image

In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, the digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. This chapter presents a brief outline of some of the most common segmentation techniques (e.g. Segmentation based on thresholding, Model based segmentation, Segmentation based on edge detection, Segmentation based on clustering, etc.,) mentioning its advantages as well as the drawbacks. The Matlab simulated results of different available image segmentation techniques are also given for better understanding of image segmentation. Simply, different image segmentation algorithms with their prospects are reviewed in this chapter to reduce the time of literature survey of the future researchers.

scholarly journals Analysis of Digital Image Watermarking Techniques through Hybrid Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Mahbuba Begum ◽  
Mohammad Shorif Uddin
Keyword(s):  
Digital Image ◽  
Hybrid Methods ◽  
Image Watermarking ◽  
Research Area ◽  
Multimedia Data ◽  
Future Research ◽  
Digital Image Watermarking ◽  
Research Directions ◽  
Future Research Directions ◽  
Transform Domains

Digital image watermarking is an attractive research area since it protects the multimedia data from unauthorized access. For designing an efficient and robust digital image watermarking system, the trade-off among imperceptibility, robustness, capacity, and security must be maintained. Various studies regarding this concern have been performed to ensure these requirements by hybridizing different domains, such as spatial and transform domains. In this paper, we have presented an analytical study of the existing hybrid digital image watermarking methods. At first, we have given a standard framework for designing a hybrid method that ensures the basic design requirements of watermarking for various applications. After a brief literature review, we compared and analyzed the complexity of several existing hybrid methods in a tabular form. The limitations and applications of these methods are also highlighted. Finally, we summarized the challenges of the existing methods and concluded the study by giving future research directions.

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
pp. 1-5
Author(s):  
Dr. Kamlesh Sharma ◽  
◽  
Nidhi Garg ◽  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

scholarly journals Sign Language Recognition using CNN

2021 ◽  
Vol 9 (9) ◽  
pp. 826-830
Author(s):  
Aniket Wattamwar
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Image Segmentation ◽  
American Sign Language ◽  
Sign Language ◽  
Digital Image Processing ◽  
Gesture Recognition ◽  
Digital Image ◽  
Research Work ◽  
American Sign

Abstract: This research work presents a prototype system that helps to recognize hand gesture to normal people in order to communicate more effectively with the special people. Aforesaid research work focuses on the problem of gesture recognition in real time that sign language used by the community of deaf people. The problem addressed is based on Digital Image Processing using CNN (Convolutional Neural Networks), Skin Detection and Image Segmentation techniques. This system recognizes gestures of ASL (American Sign Language) including the alphabet and a subset of its words. Keywords: gesture recognition, digital image processing, CNN (Convolutional Neural Networks), image segmentation, ASL (American Sign Language), alphabet

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