An Overview of Image and Video Segmentation in the Last 40 Years

2006 ◽  
pp. 1-16 ◽  
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Segmentation ◽  
Research And Development ◽  
Video Segmentation ◽  
Digital Images ◽  
Great Change ◽  
Formal Definition ◽  
Survey Papers ◽  
Segmentation Algorithms ◽  
History Of ◽  
General Scope

The history of segmentation of digital images using computers could be traced back 40 years. Since then, this field has evolved very quickly and has undergone great change. In this chapter, the position of image segmentation in the general scope of image techniques is first introduced; the formal definition and extension of image segmentation as well as three layers of research on image segmentation are then explained. Based on the introduction and explanations, statistics for a number of developed algorithms is provided, the scheme for classifying different segmentation algorithms is discussed and a summary of existing survey papers for image segmentation is presented. These discussions provide a general rendering of research and development of image segmentation in the last 40 years.

Measures of Image and Video Segmentation

2017 ◽  
pp. 28-53
Author(s):  
Pushpajit A. Khaire ◽  
Roshan R. Kotkondawar
Keyword(s):  
Image Segmentation ◽  
Video Segmentation ◽  
Evaluation Process ◽  
Final Evaluation ◽  
Evaluation Metrics ◽  
Introductory Section ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Benchmark Datasets ◽  
Traditional Image

Study on Video and Image segmentation is currently limited by the lack of evaluation metrics and benchmark datasets that covers the large variety of sub-problems appearing in image and video segmentation. Proposed chapter provides an analysis of Evaluation Metrics, Datasets for Image and Video Segmentation methods. Importance is on wide-ranging, Datasets robust Metrics which used for evaluation purposes without inducing any bias towards the evaluation results. Introductory Section discusses traditional image and video segmentation methods available, the importance and need of measures, metrics and dataset required to evaluate segmentation algorithms are discussed in next section. Main focus of the chapter explains the measures, metrics and dataset available for evaluation of segmentation techniques of both image and video. The goal is to provide details about a set of impartial datasets and evaluation metrics and to leave the final evaluation of the evaluation process to the understanding of the reader.

Measures of Image and Video Segmentation

2018 ◽  
pp. 1182-1207
Author(s):  
Pushpajit A. Khaire ◽  
Roshan R. Kotkondawar
Keyword(s):  
Image Segmentation ◽  
Video Segmentation ◽  
Evaluation Process ◽  
Final Evaluation ◽  
Evaluation Metrics ◽  
Introductory Section ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Benchmark Datasets ◽  
Traditional Image

Study on Video and Image segmentation is currently limited by the lack of evaluation metrics and benchmark datasets that covers the large variety of sub-problems appearing in image and video segmentation. Proposed chapter provides an analysis of Evaluation Metrics, Datasets for Image and Video Segmentation methods. Importance is on wide-ranging, Datasets robust Metrics which used for evaluation purposes without inducing any bias towards the evaluation results. Introductory Section discusses traditional image and video segmentation methods available, the importance and need of measures, metrics and dataset required to evaluate segmentation algorithms are discussed in next section. Main focus of the chapter explains the measures, metrics and dataset available for evaluation of segmentation techniques of both image and video. The goal is to provide details about a set of impartial datasets and evaluation metrics and to leave the final evaluation of the evaluation process to the understanding of the reader.

New Advancements in Image Segmentation for CBIR

2005 ◽  
pp. 2105-2109 ◽  
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Fast Rate ◽  
Automatic Image Analysis ◽  
Survey Papers ◽  
Segmentation Algorithms ◽  
New Applications

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images (Pavlidis, 1988). Image segmentation consists of subdividing an image into its constituent parts and extracting these parts of interest (objects). A large number of segmentation algorithms have been developed since the middle of 1960’s (see survey papers and books, for example, Bovik, 2000; Fu & Mui, 1981; Lucchese & Mitra, 2001; Medioni, Lee, & Tang, 2000; Pal & Pal, 1993; Zhang, 2001), and this number continually increases from year to year in a fast rate. This number had attended, 10 years ago, the order of thousands (Zhang & Gerbrands, 1994). However, none of the proposed segmentation algorithms is generally applicable to all images, and different algorithms are not equally suitable for a particular application. Though several thousands of algorithms have been proposed, improvements for existing algorithms and developments for treating new applications are still going on.

Graph Based Segmentation of Digital Images

2013 ◽  
pp. 182-199
Author(s):  
B.K. Tripathy ◽  
P.V.S.S.R. Chandra Mouli
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Object Detection ◽  
Image Classification ◽  
Digital Images ◽  
Graph Theoretic ◽  
Graph Partitioning Problem ◽  
Segmentation Algorithms ◽  
Partitioning Problem ◽  
Segmentation Problem

Image Segmentation is the process of dividing an image into semantically relevant regions. The problem is still an active area due to wide applications in object detection and recognition, image retrieval, image classification, et cetera. The problem is challenging due to its subjective nature. Many researchers addressed this problem by exploring graph theoretic principles. The key idea is the transformation of segmentation problem into graph partitioning problem by representing the image as a graph. The aim of this chapter is to study various graph based segmentation algorithms.

A History of Geothermal Energy Research and Development in the United States. Reservoir Engineering 1976-2006

2010 ◽  
Author(s):  
B. Mack Kennedy ◽  
Karsten Pruess ◽  
Marcelo J. Lippmann ◽  
Ernest L. Majer ◽  
Peter E. Rose ◽  
...  
Keyword(s):  
United States ◽  
Research And Development ◽  
Geothermal Energy ◽  
The United States ◽  
Reservoir Engineering ◽  
Energy Research ◽  
History Of

Technical Issues Related to Computerized Music Performance Assessment

2019 ◽  
pp. 854-876
Author(s):  
Daniel Massoth
Keyword(s):  
Research And Development ◽  
Performance Assessment ◽  
Music Performance ◽  
External Factors ◽  
Assessment Process ◽  
Future Directions ◽  
Music Assessment ◽  
History Of ◽  
Technical Issues ◽  
Internal And External Factors

When technology is used for assessment in music, certain considerations can affect the validity, reliability, and depth of analysis. This chapter explores factors that are present in the three phases of the assessment process: recognition, analysis, and display of assessment of a musical performance. Each phase has inherent challenges embedded within internal and external factors. The goal here is not to provide an exhaustive analysis of any or all aspects of assessment but, rather, to present the rationale for and history of using technology in music assessment and to examine the philosophical and practical considerations. A discussion of possible future directions of product research and development concludes the chapter.

scholarly journals A history of high-power laser research and development in the United Kingdom

2021 ◽  
Vol 9 ◽  
Author(s):  
Colin N. Danson ◽  
Malcolm White ◽  
John R. M. Barr ◽  
Thomas Bett ◽  
Peter Blyth ◽  
...  
Keyword(s):  
Research And Development ◽  
High Power ◽  
Laser Action ◽  
Historical Review ◽  
High Power Laser ◽  
Power Laser ◽  
The United Kingdom ◽  
History Of ◽  
The Uk ◽  
Made In

Abstract The first demonstration of laser action in ruby was made in 1960 by T. H. Maiman of Hughes Research Laboratories, USA. Many laboratories worldwide began the search for lasers using different materials, operating at different wavelengths. In the UK, academia, industry and the central laboratories took up the challenge from the earliest days to develop these systems for a broad range of applications. This historical review looks at the contribution the UK has made to the advancement of the technology, the development of systems and components and their exploitation over the last 60 years.

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

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