Split and Merge: A Region Based Image Segmentation

2018 ◽  
Vol 6 (8) ◽  
pp. 306 ◽  
Author(s):  
Anju Bala ◽  
Aman Kumar Sharma
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Digital Image ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Region Segmentation ◽  
Segmentation Algorithms ◽  
Different Types ◽  
Split And Merge

Image segmentation is a very challenging task in digital image processing field. It is defined as the process of takeout objects from an image by dividing it into different regions where regions that depicts some information are called objects. There are different types of image segmentation algorithms. The segmentation process depends upon the type of description required for an application for which segmentation is to be performed. Hence, there is no universally accepted segmentation algorithm. Region segmentation is divided into three categories region growing, split and merge and watershed. But this study confines only to split and merge techniques. This paper includes split and merge approaches and their extended versions. This study highlights the main limitations and potentials of these approaches.

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.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

scholarly journals Application of Digital Image Processing to Determine the Properties of Aggregates and Soil using MATLAB: A Case Study

2021 ◽  
Vol 7 (1) ◽  
pp. 711-716
Author(s):  
Maruti Chowdary Bathula ◽  
Saisantosh Vamshi Harsha Madiraju
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Naturally Occurring ◽  
Different Types ◽  
Bounded Space ◽  
Management School ◽  
Flakiness Index ◽  
Aggregate Shape

The vital physical characteristics of aggregates used in engineering and for interpretation of the genesis of naturally occurring sediment are shape and size of rocks. The mineralogical and physical composition of the rock is necessary to cater to the interaction within bounded space and soundings. Several image processing programs are available to measure the size and shape of various types of objects. The accuracy and reproducibility of the results of these imaging methods could be bettered. This paper aims to determine the aggregate dimension and volume to space behaviors through image processing tools. This study is extended investigation of the coarse aggregate shape properties such as area, volume, center of gravity (CG), elongation and flakiness index. Also, this paper discuss the development of an easy to use image processing tool to determine the shrinkage of soils due to lack of moisture content either because of natural ways/artificial ways. Two different types of soils including first sample near the SNU lake and second sample near Business Management School at Shiv Nadar University (SNU), India were collected. The samples were oven-dried over a period and shrinkage values were determined by Digital Image Processing (DIP) using MATLAB. The results are compared to wax-water method (manual method) results of shrinkage. The properties found by DIP method are reasonably agreeable for multiple aggregates and minor percentage differences in the soil shrinkage results.

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.

Research and Simulation on the Image Segmentation Algorithm with Corn Image Based on HSI Color Model

2012 ◽  
Vol 459 ◽  
pp. 128-131
Author(s):  
Xue Feng Hou ◽  
Yuan Yuan Shang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Detection Algorithm ◽  
Color Model ◽  
Edge Detection Algorithm ◽  
Segmentation Algorithms ◽  
Hsi Color Model ◽  
Classical Image ◽  
Image Segmentation Algorithm

Image segmentation is one focus of digital image processing. In this paper, fourteen different kinds of classical image segmentation algorithms are studied and compared using corn image and simulating in MATLAB based on HSI color model. The result reveals that the method that using H component based on HSI color model to deal with the histogram threshold algorithm and Laplace edge detection algorithm is effectively extract the plant from the corn image

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

scholarly journals Definition of hue of different types of pokostivskiy granodiorite using digital image processing

2016 ◽  
Vol 4 (5(82)) ◽  
pp. 52 ◽  
Author(s):  
Valentyn Korobiichuk ◽  
Volodymyr Shamrai ◽  
Oksana Iziumova ◽  
Oleksandr Tolkach ◽  
Ruslan Sobolevskyi
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Different Types ◽  
Definition Of
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