scholarly journals A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

2021 ◽  
Author(s):  
Himanshu Mittal ◽  
Avinash Chandra Pandey ◽  
Mukesh Saraswat ◽  
Sumit Kumar ◽  
Raju Pal ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Performance Parameters ◽  
Clustering Methods ◽  
Benchmark Datasets ◽  
Comprehensive Survey

scholarly journals IMAGE SEGMENTATION AND OBJECT SELECTION BASED ON MULTI-THRESHOLD PROCESSING

2019 ◽  
Vol 22 (3) ◽  
pp. 24-35 ◽  
Author(s):  
Vladimir Yu. Volkov ◽  
Oleg A. Markelov ◽  
Mikhail I. Bogachev
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Test Methods ◽  
Object Identification ◽  
Detection Methods ◽  
Clustering Methods ◽  
Object Selection ◽  
Advantages And Disadvantages ◽  
Infrared Cameras ◽  
Threshold Processing

Introduction. Detection, isolation, selection and localization of variously shaped objects in images are essential in a variety of applications. Computer vision systems utilizing television and infrared cameras, synthetic aperture surveillance radars as well as laser and acoustic remote sensing systems are prominent examples. Such problems as object identification, tracking and matching as well as combining information from images available from different sources are essential. Objective. Design of image segmentation and object selection methods based on multi-threshold processing. Materials and methods. The segmentation methods are classified according to the objects they deal with, including (i) pixel-level threshold estimation and clustering methods, (ii) boundary detection methods, (iii) regional level, and (iv) other classifiers, including many non-parametric methods, such as machine learning, neural networks, fuzzy sets, etc. The keynote feature of the proposed approach is that the choice of the optimal threshold for the image segmentation among a variety of test methods is carried out using a posteriori information about the selection results. Results. The results of the proposed approach is compared against the results obtained using the well-known binary integration method. The comparison is carried out both using simulated objects with known shapes with additive synthesized noise as well as using observational remote sensing imagery. Conclusion. The article discusses the advantages and disadvantages of the proposed approach for the selection of objects in images, and provides recommendations for their use.

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.

Review of Fuzzy Image Segmentation Techniques

2011 ◽  
pp. 282-313 ◽  
Author(s):  
Gour C. Karmakar ◽  
Laurence Dooley ◽  
Mahbubhur Rahman Syed
Keyword(s):  
Image Segmentation ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Clustering Methods ◽  
Comprehensive Overview ◽  
Domain Specific ◽  
Soft Computing Techniques ◽  
Fuzzy Clustering Methods ◽  
Fuzzy Image ◽  
Multimodal Images

This chapter provides a comprehensive overview of various methods of fuzzy logic-based image segmentation techniques. Fuzzy image segmentation techniques outperform conventional techniques, as they are able to evaluate imprecise data as well as being more robust in noisy environment. Fuzzy clustering methods need to set the number of clusters prior to segmentation and are sensitive to the initialization of cluster centers. Fuzzy rule-based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. It is also capable of drawing partial inference using fuzzy IF-THEN rules. It has been also intensively applied in medical imaging. These rules are, however, application-domain specific and very difficult to define either manually or automatically that can complete the segmentation alone. Fuzzy geometry and thresholding-based image segmentation techniques are suitable only for bimodal images and can be applied in multimodal images, but they don’t produce a good result for the images that contain a significant amount of overlapping pixels between background and foreground regions. A few techniques on image segmentation based on fuzzy integral and soft computing techniques have been published and appear to offer considerable promise.

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

2014 ◽  
Vol 3 (1) ◽  
pp. 16 ◽  
Author(s):  
Minakshi Sharma ◽  
Saourabh Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Inference ◽  
Research Work ◽  
Diagnostic Technique ◽  
Clustering Methods ◽  
Inference System ◽  
Fuzzy C Means ◽  
Soft Computing Techniques

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques  (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus  Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of  Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>

A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Manjeet Singh ◽  
Surender Kumar Soni
Keyword(s):  
Cluster Head ◽  
Sensor Nodes ◽  
Tabular Form ◽  
Clustering Methods ◽  
Content Type ◽  
Clustering Techniques ◽  
Variables Selection ◽  
Comprehensive Survey ◽  
Decision Making Processes ◽  
Art Research

Purpose This paper aims to discuss a comprehensive survey on fuzzy-based clustering techniques. The determination of an appropriate sensor node as a cluster head straightforwardly affects a network’s lifetime. Clustering often possesses some uncertainties in determining suitable sensor nodes as a cluster head. Owing to various variables, selection of a suitable node as a cluster head is a perplexing decision. Fuzzy logic is capable of handling uncertainties and improving decision-making processes even with insufficient information. Then, state-of-the-art research in the field of clustering techniques has been reviewed. Design/methodology/approach The literature is presented in a tabular form with merits and limitations of each technique. Furthermore, the various techniques are compared graphically and classified in a tabular form and the flowcharts of important algorithms are presented with pseudocodes. Findings This paper comprehends the importance and distinction of different fuzzy-based clustering methods which are further supportive in designing more efficient clustering protocols. Originality/value This paper fulfills the need of a review paper in the field of fuzzy-based clustering techniques because no other paper has reviewed all the fuzzy-based clustering techniques. Furthermore, none of them has presented literature in a tabular form or presented flowcharts with pseudocodes of important techniques.

scholarly journals Comprehensive Survey of Recent Drug Discovery Using Deep Learning

2021 ◽  
Vol 22 (18) ◽  
pp. 9983
Author(s):  
Jintae Kim ◽  
Sera Park ◽  
Dongbo Min ◽  
Wankyu Kim
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Molecular Structures ◽  
De Novo Drug Design ◽  
Related Data ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Model Training

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

scholarly journals You can Try without Visiting: A Comprehensive Survey on Virtually Try-on Outfits

2021 ◽  
Author(s):  
Hajer Ghodhbani ◽  
Adel Alimi ◽  
Mohamed Neji ◽  
Imran Razzak
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Research Field ◽  
Future Research ◽  
Fashion Industry ◽  
Research Directions ◽  
Comprehensive Literature Review ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Future Research Directions

<p>Our work aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry and, especially, the image-based virtual fitting task by citing research works published in the last years. We have summarized their challenges, their main frameworks, the popular benchmark datasets, and the different evaluation metrics. Also, some promising future research directions are discussed to propose improvements in this research field.</p>

scholarly journals Challenges and Limitations in Human Action Recognition on Unmanned Aerial Vehicles: A Comprehensive Survey

2021 ◽  
Vol 38 (5) ◽  
pp. 1403-1411
Author(s):  
Nashwan Adnan Othman ◽  
Ilhan Aydin
Keyword(s):  
Action Recognition ◽  
Smart Cities ◽  
Large Angle ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Detection ◽  
Long Distance ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Aerial Vehicle

An Unmanned Aerial Vehicle (UAV), commonly called a drone, is an aircraft without a human pilot aboard. Making UAVs that can accurately discover individuals on the ground is very important for various applications, such as people searches, and surveillance. UAV integration in smart cities is challenging, however, because of problems and concerns such as privacy, safety, and ethical/legal use. Human action recognition-based UAVs can utilize modern technologies. Thus, it is essential for future development of the aforementioned applications. UAV-based human activity recognition is the procedure of classifying photo sequences with action labels. This paper offers a comprehensive study of UAV-based human action recognition techniques. Furthermore, we conduct empirical research studies to assess several factors that might influence the efficiency of human detection and action recognition techniques in UAVs. Benchmark datasets commonly utilized for UAV-based human action recognition are briefly explained. Our findings reveal that the existing human action recognition innovations can identify human actions on UAVs with some limitations in range, altitudes, long-distance, and a large angle of depression.

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