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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Gonthina, Nagamani Prasad, L. V. Narasimha Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

One of the most challenging jobs in image processing techniques is image segmentation. To identify the objects of interest in an image, we segment the image into different parts and extract the interesting objects. Previous studies have addressed various techniques for image segmentation, like clustering, thresholding, watershed, neural networks, etc. However, a segmentation technique alone cannot achieve better-segmented results. Much research is being carried out by combining the segmentation technique with either an optimization algorithm or entropy measures to enhance the efficiency of segmented results in the segmentation process. So, there is a need to study various segmentation techniques combined with optimization algorithms or entropy measures. Recent studies focused only on segmentation techniques, entropy measures, or optimization algorithms rather than on these combinations. This paper presents various works based on optimization techniques and entropy measures combined with clustering and thresholding techniques of image segmentation. From the study it is observed that most of the work is carried out in combining thresholding-based image segmentation techniques with optimization algorithms and entropy measures. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Entropy measures Image segmentation Optimization algorithms segmentation techniques

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IMAGE segmentation APPROACHES: A COMPARATIVE STUDY 9

IMAGE SEGMENTATION APPROACHES: A COMPARATIVE STUDY

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on Computer Science and Information Systems 2024, MCCSIS 2024

作者： Miftah, Rajaa Hanoune, Mostafa Bentaib, Mohssine LTIM Laboratory Faculty of Science Ben M’sik Hassan II University of Casablanca Casablanca20000 Morocco

ISBN: (纸本)9789898704597

Object detection is an intriguing and pivotal topic in the field of computer vision. Meanwhile, segmentation plays a crucial role and stands as one of the oldest problem statements that researchers have extensively investigated in the realm of Artificial intelligence (AI). Within computer vision systems, image segmentation poses significant challenges. Consequently, numerous methods have been developed to address this complex task. Given the vast array of options available, this research focuses on exploring and comparing the frequently employed classical techniques for image segmentation. The study aims to shed light on the strengths, limitations, and performance characteristics of each approach. By comprehensively examining these traditional segmentation methods, we strive to provide valuable insights into their applicability, potential use cases, and overall effectiveness in addressing the challenges posed by image segmentation tasks. Ultimately, this research seeks to contribute to the advancement of computer vision systems and inspire further exploration in the field of image segmentation. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Artificial Intelligence Computer Vision Image segmentation Object Detection Scene Understanding segmentation techniques

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An efficient, font independent word and character segmentation algorithm for printed Arabic text

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第1期34卷 1330-1344页

作者： Qaroush, Aziz Jaber, Bassam Mohammad, Khader Washaha, Mahdi Maali, Eman Nayef, Nibal Birzeit Univ Dept Elect & Comp Engn Birzeit Palestine Univ La Rochelle L3i La Rochelle France

Characters segmentation is a necessity and the most critical stage in Arabic OCR system. It has attracted the interest of a wide range of researchers. However, the nature of the Arabic cursive script poses extra challenges that need further investigation. Therefore, having a reliable and efficient Arabic OCR system that is independent of font variations is highly required. In this paper, an indirect, font-in dependent word and character segmentation algorithm for printed Arabic text investigated. The proposed algorithm takes a binary line image as an input and produces a set of binary images consisting of one character or ligature as an output. The segmentation performed at two levels: a word segmentation performed in the first level, by employing a vertical projection at the input line image along with using Interquartile Range (IQR) method to differentiate between word gaps and within word gaps. A projection profile method used as a second level of segmentation along with a set of statistical and topological features, which are font-independent, to identify the correct segmentation points from all potential points. The APTI dataset used to test the proposed algorithm with a variety of font type, size, and style. The algorithm experimented on 1800 lines (approximately 24,816 words) with an average accuracy of 97.7% for words segmentation and 97.51% for characters segmentation. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Arabic OCR Word segmentation Character segmentation Cursive script segmentation techniques Baseline Projection profile

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Improved Classification techniques Using Convolutional Neural Networks for Rice Plant Disease Detection 4th

Improved Classification Techniques Using Convolutional Neura...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Saxena, Swati Gohil, Bhavesh N. Sardar Vallabhbhai Natl Inst Technol SVNIT Surat Gujarat India

ISBN: (纸本)9783031837951;9783031837968

Rice, one of the most important crops globally, serves as a vital nutritional source for over 50% of the world's population. However, it is highly susceptible to various diseases that can severely impact both the quality and quantity of the harvest, sometimes causing a reduction of 20%-40% in crop yield. Timely detection and accurate identification of rice plant diseases are critical for ensuring a healthy harvest. Farmers must be able to recognize these diseases visually, but daily field surveys are labor-intensive, time-consuming and costly, ultimately driving up the price of rice for consumers. Rice leaves are prone to several bacterial, viral and fungal diseases, which significantly affect rice production. To meet the growing global demand for rice, effective disease detection is essential. However, challenges in disease recognition arise due to background noise and varying capture conditions in images. Convolutional Neural Network (CNN)-based models have significantly advanced the detection and identification of rice plant diseases. This paper aims to achieve efficient and timely detection and identification of rice plant diseases by exploring the CNN models, both with and without segmentation techniques, for diseases such as Brown Spot, Leaf Blast and Neck Blast.

关键词： Rice Disease Image Processing segmentation techniques Convolutional Neural Networks

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Four recommendations to increase the level of pervasion of change projects in higher education institutions: a case report

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INTERNATIONAL JOURNAL OF EDUCATIONAL MANAGEMENT 2024年第2期38卷 587-601页

作者： Preusse, Joachim Ruhr Univ Bochum Open Resources Campus NRW Bochum Germany

PurposeThe purpose of this case report is to (1) to describe outputs of a change project at Muenster University of Applied Sciences (Germany), which targeted its "operational core" of professors and (2) on this basis to derive ideas on how to ensure a high level of pervasion of change projects in the higher education ***/methodology/approachThe author compiled data on the extent to which the project has penetrated the organisation derived from the project documentation, discusses satisfaction with the level of pervasion and presents four approaches to increase the level of organisational pervasion for similar projects. These were developed in introspective observations by the project team during project execution (2011-2021).FindingsThe level of pervasion achieved by the project was judged to be satisfactory overall, but in principle still capable of being increased, as there are clear differences between subject disciplines. Four approaches to increase the level of pervasion are derived: (1) explicit attention to and monitoring of informal structures, (2) use of milieu-specific segmentation techniques of target groups, (3) variations of the way change objectives are presented within the organisation and (4) a dynamisation of stakeholder *** implicationsThe paper offers guidance to change managers in higher education institutions on how to improve the planning phase of change projects. It is essential for change projects to achieve their intended outcome (project outcome) that the organisation is sufficiently pervaded in quantitative terms (project outputs). The recommendations presented in this paper should be of interest to all higher education staff involved in change management, as they can help to increase the level of organisational ***/valueAlthough Higher Education Institutions (HEIs) are considered to be a type of organisation that is difficult to change, change projects are an integral part of their management p

关键词： Change management Pervasion Teaching culture Informal structure segmentation techniques Pragmatic value of information Stakeholder management

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Learning-free, divide and conquer text-line extraction algorithm for printed Arabic text with diacritics

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第9期34卷 7699-7709页

作者： Qaroush, Aziz Awad, Abdalkarim Hanani, Abualsoud Mohammad, Khader Jaber, Basam Hasheesh, Ala Birzeit Univ Dept Elect & Comp Engn Birzeit Palestine

The extraction of text lines from document images is a critical step in optical character recognition. It is still considered an open document analysis problem. The presence of numerous font variations, diacritics, overlapping, and touching text-lines presents a challenge to algorithms designed for machine-printed text. In this paper, we present a simple and robust text-line extraction algorithm for printed Arabic text. The presented method is divided into two stages: preprocessing and text-line extraction. It extracts text -lines efficiently, even in small font sizes, by utilizing baselines, projection profiles, and a top-down divide and conquer technique. The presented method is fast and efficient when dealing with non-uniform inter-line spacing and the text-line overlapping problem. A set of experiments were conducted on the collected dataset. The experiments revealed that the pro-posed method outperforms two baseline approaches, with an average error rate of 3% on Arabic text without diacritics and 11% on Arabic text with diacritics. Furthermore, the experiments demonstrate that the proposed algorithm has a simple computational running time, with an average running time of 0.087 s per document image. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Arabic OCR Cursive script Diacritics Text-line segmentation segmentation techniques Baseline Projection profile

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Instance segmentation of stack composed of unknown objects

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期126卷

作者： Czubenko, Michal Chrzanowski, Artur Okunski, Rafal Intema Sp Zoo Siennicka 25a PL-80758 Gdansk Poland Gdansk Univ Technol Fac Elect Telecommun & Informat Dept Decis Syst & Robot Narutowicza 11-12 Gdansk Poland

The article reviews neural network architectures designed for the segmentation task. It focuses mainly on instance segmentation of stacked objects. The main assumption is that segmentation is based on a color image with an additional depth layer. The paper also introduces the Stacked Bricks Dataset based on three cameras: RealSense L515, ZED2, and a synthetic one. Selected architectures: DeepLab, Mask RCNN, DEtection TRansformer, Geometry-Aware Instance segmentation, Shapemask, Synthetic Depth Mask RCNN, Synthetic Fusion Mask RCNN (SF-Mask), Unseen Object Instance segmentation (UOIS), Unseen Object Clustering (UOC), and You Look Only At Coefficients, have been tested on various datasets. The results show that the best architectures for stacked elements segmentation are UOIS, SF-Mask, and UOC.

关键词： segmentation techniques Pick-and-place operation Deep neural networks

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A review on optimization techniques for medical image analysis

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第1期35卷 e7443-e7443页

作者： Kaur, Palwinder Singh, Rajesh Kumar IKG Punjab Tech Univ Dept Comp Sci Jalandhar Punjab India SUS Grp Coll Dept Comp Applicat Mohali India

Data mining of medical imaging approaches makes it difficult to determine their value in the disease's insight, analysis, and diagnosis. Image classification presents a significant difficulty in image analysis and plays a vital part in computer-aided diagnosis. This task concerned the use of optimization techniques for the utilization of image processing, pattern recognition, and classification techniques, as well as the validation of image classification results in medical expert reports. The primary intention of this study is to analyze the performance of optimization techniques explored in the area of medical image analysis. For this motive, the optimization techniques employed in existing literature from 2012 to 2021 are reviewed in this study. The contribution of optimization-based medical image classification and segmentation utilized image modalities, data sets, and tradeoffs for each technique are also discussed in this review study. Finally, this review study provides the gap analysis of optimization techniques used in medical image analysis with the possible future research direction.

关键词： brain disease diagnosis breast disease diagnosis heart disease diagnosis lung disease diagnosis medical image analysis optimization algorithm retinal disease diagnosis segmentation techniques

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Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision techniques: A Review

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CURRENT MEDICAL IMAGING 2020年第10期16卷 1229-1242页

作者： Liaqat, Amna Khan, Muhammad Attique Sharif, Muhammad Mittal, Mamta Saba, Tanzila Manic, K. Suresh Al Attar, Feras Nadhim Hasoon COMSATS Univ Islamabad Dept Comp Sci Wah Cantt Pakistan HITEC Univ Dept Comp Sci Taxila Pakistan GB Pant Govt Engn Coll Dept Comp Sci & Engn New Delhi India Prince Sultan Univ Dept Comp & Informat Sci Riyadh Saudi Arabia Natl Univ Sci & Technol Dept Elect & Comp Engn Muscat Oman

Recent facts and figures published in various studies in the US show that approximately 27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that the mortality rate is quite high in diagnosed cases. The early detection of these infections can save precious human lives. As the manual process of these infections is time-consuming and expensive, therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy specialists in their clinics. Generally, an automated method of gastric infection detections using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast pre-processing, feature extraction, segmentation of infected regions, and classification into their relevant categories. These steps consist of various challenges that reduce the detection and recognition accuracy as well as increase the computation time. In this review, authors have focused on the importance of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and the scope of endoscopy. Further, the general steps and highlighting the importance of each step have been presented. A detailed discussion and future directions have been provided at the end.

关键词： Wireless capsule endoscopy preprocessing techniques feature-based techniques segmentation techniques classification future trends

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Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2020年第11期58卷 2603-2620页

作者： Ul Haq, Ejaz Huang, Jianjun Kang, Li Ul Haq, Hafeez Zhan, Tijiang Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Shenzhen Univ ATR Key Lab Shenzhen Peoples R China Fujian Normal Univ Fuzhou Peoples R China Zunyi Med Univ Imaging Dept Affiliated Hosp Zunyi Guizhou Peoples R China

Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and advantageous methods for brain diseases detection and characterization. Magnetic resonance imaging (MRI), computed tomography (CT), and other various brain imaging scans are used to identify different brain diseases and disorders. Brain imaging scans are the efficient tool to understand the anatomical changes in brain in fast and accurate manner. These different brain imaging scans used with segmentation techniques and along with machine learning and deep learning techniques give maximum accuracy and efficiency. This paper focuses on different conventional approaches, machine learning and deep learning techniques used for the detection, and classification of brain diseases and abnormalities. This paper also summarizes the research gap and problems in the existing techniques used for detection and classification of brain disorders. Comparison and evaluation of different machine learning and deep learning techniques in terms of efficiency and accuracy are also highlighted in this paper. Furthermore, different brain diseases like leukoariaosis, Alzheimer's, Parkinson's, and Wilson's disorder are studied in the scope of machine learning and deep learning techniques.

关键词： Brain diseases Brain imaging scan Deep learning Machine learning segmentation techniques Magnetic resonance imaging Computed tomography

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